diff --git a/Backend/EnergeticFlowPlace.mo b/Backend/EnergeticFlowPlace.mo
new file mode 100644
index 0000000000000000000000000000000000000000..fdc121986a5040d8892ea83b2ae0c6bf3714f3e0
--- /dev/null
+++ b/Backend/EnergeticFlowPlace.mo
@@ -0,0 +1,183 @@
+within PNRG.Backend;
+
+// The following code is modified Code from PNlib library.
+
+model EnergeticFlowPlace
+ Real t "marking";
+ //****MODIFIABLE PARAMETERS AND VARIABLES BEGIN****//
+ parameter Integer nIn(min = 0) = 0 "number of input transitions" annotation(
+ Dialog(enable = true, group = "Connector sizing"));
+ parameter Integer nOut(min = 0) = 0 "number of output transitions" annotation(
+ Dialog(enable = true, group = "Connector sizing"));
+ parameter Real arcWeightOutSplit[nOut] = fill(1/nOut, nOut) "Split of input arcs sum to output" annotation(
+ Dialog(enable = true, group = "Output arc weights"));
+ parameter Real startMarks = 0 "start marks" annotation(
+ Dialog(enable = true, group = "Marks"));
+ Boolean reStart = false "restart condition" annotation(
+ Dialog(enable = true, group = "Marks"));
+ parameter Integer N = settings.N "N+1=amount of levels" annotation(
+ Dialog(enable = true, group = "Level Concentrations"));
+ parameter PNlib.Types.EnablingType enablingType = PNlib.Types.EnablingType.Priority "resolution type of actual conflict (type-1-conflict)" annotation(
+ Dialog(enable = true, group = "Enabling"));
+ parameter Integer enablingPrioIn[nIn] = 1:nIn "enabling priorities of input transitions" annotation(
+ Dialog(enable = if enablingType == PNlib.Types.EnablingType.Probability then false else true, group = "Enabling"));
+ parameter Integer enablingPrioOut[nOut] = 1:nOut "enabling priorities of output transitions" annotation(
+ Dialog(enable = if enablingType == PNlib.Types.EnablingType.Probability then false else true, group = "Enabling"));
+ parameter Real enablingProbIn[nIn] = fill(1/nIn, nIn) "enabling probabilities of input transitions" annotation(
+ Dialog(enable = if enablingType == PNlib.Types.EnablingType.Priority then false else true, group = "Enabling"));
+ Real power "Power (sum of arcWeightIn)";
+ parameter Real enablingProbOut[nOut] = fill(1/nOut, nOut) "enabling probabilities of output transitions" annotation(
+ Dialog(enable = if enablingType == PNlib.Types.EnablingType.Priority then false else true, group = "Enabling"));
+ //****MODIFIABLE PARAMETERS AND VARIABLES END****//
+ Real levelCon "conversion of tokens to level concentration according to M and N of the settings box";
+ Boolean showPlaceName = settings.showPlaceName "only for place animation and display (Do not change!)";
+ Boolean showCapacity = settings.showCapacity "only for place animation and display (Do not change!)";
+ Boolean animateMarking = settings.animateMarking "only for place animation and display (Do not change!)";
+ Real color[3] "only for place animation and display (Do not change!)";
+ parameter Boolean showTokenFlow = settings.showTokenFlow annotation(
+ Dialog(enable = true, group = "Token flow"));
+ PNlib.Blocks.tokenFlowCon tokenFlow(nIn = nIn, nOut = nOut, conFiringSumIn = firingSumIn.conFiringSum, conFiringSumOut = firingSumOut.conFiringSum, fireIn = fireIn, fireOut = fireOut, arcWeightIn = arcWeightIn, arcWeightOut = arcWeightOut, instSpeedIn = instSpeedIn, instSpeedOut = instSpeedOut) if showTokenFlow;
+ parameter Integer localSeedIn = PNlib.Functions.Random.counter() "Local seed to initialize random number generator for input conflicts" annotation(
+ Dialog(enable = true, group = "Random Number Generator"));
+ parameter Integer localSeedOut = PNlib.Functions.Random.counter() "Local seed to initialize random number generator for output conflicts" annotation(
+ Dialog(enable = true, group = "Random Number Generator"));
+protected
+ outer PNlib.Components.Settings settings "global settings for animation and display";
+ Real minMarks = 0 "minimum capacity";
+ Real maxMarks = 0 "maximum capacity";
+ Real reStartMarks = 0 "number of marks at restart";
+ Real disMarkChange "discrete mark change";
+ Real conMarkChange "continuous mark change";
+ Real arcWeightIn[nIn] "weights of input arcs";
+ Real arcWeightOut[nOut] "weights of output arcs";
+ Real instSpeedIn[nIn] "instantaneous speed of input transitions";
+ Real instSpeedOut[nOut] "instantaneous speed of output transitions";
+ Real maxSpeedIn[nIn] "maximum speed of input transitions";
+ Real maxSpeedOut[nOut] "maximum speed of output transitions";
+ Real prelimSpeedIn[nIn] "preliminary speed of input transitions";
+ Real prelimSpeedOut[nOut] "preliminary speed of output transitions";
+ Real tokenscale "only for place animation and display";
+ Real t_(start = startMarks, fixed = true) "marking";
+ Boolean disMarksInOut(start = false, fixed = true) "discrete marks change";
+ Boolean preFireIn[nIn] "pre-value of fireIn";
+ Boolean preFireOut[nOut] "pre-value of fireOut";
+ Boolean fireIn[nIn](each start = false, each fixed = true) "Does any input transition fire?";
+ Boolean fireOut[nOut](each start = false, each fixed = true) "Does any output transition fire?";
+ Boolean disTransitionIn[nIn] "Are the input transitions discrete?";
+ Boolean disTransitionOut[nOut] "Are the output transitions discrete?";
+ Boolean activeIn[nIn] "Are the input transitions active?";
+ Boolean activeOut[nOut] "Are the output transitions active?";
+ Boolean enabledByInPlaces[nIn] "Are the input transitions enabled by all their input places?";
+ //****BLOCKS BEGIN****// since no events are generated within functions!!!
+ //enabling discrete transitions
+ PNlib.Blocks.enablingInCon enableIn(active = activeIn, delayPassed = delayPassedIn.anytrue, nIn = nIn, arcWeight = arcWeightIn, t = t_, maxMarks = maxMarks, TAein = enabledByInPlaces and activeIn, enablingType = enablingType, enablingPrio = enablingPrioIn, enablingProb = enablingProbIn, disTransition = disTransitionIn, localSeed = localSeedIn, globalSeed = settings.globalSeed);
+ PNlib.Blocks.enablingOutCon enableOut(delayPassed = delayPassedOut.anytrue, nOut = nOut, arcWeight = arcWeightOut, t = t_, minMarks = minMarks, TAout = activeOut, enablingType = enablingType, enablingPrio = enablingPrioOut, enablingProb = enablingProbOut, disTransition = disTransitionOut, localSeed = localSeedOut, globalSeed = settings.globalSeed);
+ //Does any delay passed of a connected transition?
+ PNlib.Blocks.anyTrue delayPassedOut(vec = activeOut and disTransitionOut);
+ PNlib.Blocks.anyTrue delayPassedIn(vec = activeIn and disTransitionIn);
+ //Does any discrete transition fire?
+ PNlib.Blocks.anyTrue disMarksOut(vec = fireOut and disTransitionOut);
+ PNlib.Blocks.anyTrue disMarksIn(vec = fireIn and disTransitionIn);
+ //Is the place fed by input transitions?
+ PNlib.Blocks.anyTrue feeding(vec = preFireIn and not disTransitionIn);
+ //Is the place emptied by output transitions?"
+ PNlib.Blocks.anyTrue emptying(vec = preFireOut and not disTransitionOut);
+ //firing sum calculation
+ PNlib.Blocks.firingSumCon firingSumIn(fire = preFireIn, arcWeight = arcWeightIn, instSpeed = instSpeedIn, disTransition = disTransitionIn);
+ PNlib.Blocks.firingSumCon firingSumOut(fire = preFireOut, arcWeight = arcWeightOut, instSpeed = instSpeedOut, disTransition = disTransitionOut);
+ //****BLOCKS END****//
+ Real decFactorIn[nIn] "decreasing factors for input transitions";
+ Real decFactorOut[nOut] "decreasing factors for output transitions";
+public
+ PNlib.Interfaces.PlaceIn inTransition[nIn](each t=t_,
+ each tint=1,
+ each maxTokens=maxMarks,
+ each maxTokensint=1,
+ enable=enableIn.TEin_,
+ each emptied = emptying.anytrue,
+ decreasingFactor = decFactorIn,
+ each disPlace = false,
+ each speedSum= firingSumOut.conFiringSum,
+ fire=fireIn,
+ disTransition=disTransitionIn,
+ active=activeIn,
+ arcWeight=arcWeightIn,
+ instSpeed=instSpeedIn,
+ maxSpeed=maxSpeedIn,
+ prelimSpeed=prelimSpeedIn,
+ enabledByInPlaces=enabledByInPlaces) if nIn > 0 "connector for input transitions" annotation(Placement(
+ transformation(extent={{-114, -10}, {-98, 10}}, rotation=0),
+ iconTransformation(extent={{-116, -10}, {-100, 10}})));
+ PNlib.Interfaces.PlaceOut outTransition[nOut](each t = t_,
+ each tint=1,
+ each minTokens=minMarks,
+ each minTokensint=1,
+ enable=enableOut.TEout_,
+ each fed=feeding.anytrue,
+ decreasingFactor=decFactorOut,
+ each disPlace=false,
+ each arcType=PNlib.Types.ArcType.NormalArc,
+ each speedSum=firingSumIn.conFiringSum,
+ each tokenInOut=pre(disMarksInOut),
+ fire=fireOut,
+ disTransition=disTransitionOut,
+ active=activeOut,
+ arcWeight=arcWeightOut,
+ instSpeed=instSpeedOut,
+ maxSpeed=maxSpeedOut,
+ prelimSpeed=prelimSpeedOut,
+ each testValue=-1,
+ each testValueint=-1,
+ each normalArc=false) if nOut > 0 "connector for output transitions" annotation(Placement(
+ transformation(extent={{100, -10}, {116, 10}}, rotation=0)));
+ Modelica.Blocks.Interfaces.RealOutput pc_t=t
+ "connector for Simulink connection" annotation(Placement(
+ transformation(extent={{-36, 68}, {-16, 88}}), iconTransformation(
+ extent={{-10, -10}, {10, 10}},
+ rotation=90,
+ origin={0, 108})));
+
+
+equation
+ for i in 1:nOut loop
+ arcWeightOut[i] = arcWeightOutSplit[i] * power;
+ end for;
+ power = sum(arcWeightIn);
+//decreasing factor calculation
+ (decFactorIn, decFactorOut) = PNlib.Functions.decreasingFactor(nIn = nIn, nOut = nOut, t = t_, minMarks = minMarks, maxMarks = maxMarks, speedIn = firingSumIn.conFiringSum, speedOut = firingSumOut.conFiringSum, maxSpeedIn = maxSpeedIn, maxSpeedOut = maxSpeedOut, prelimSpeedIn = prelimSpeedIn, prelimSpeedOut = prelimSpeedOut, arcWeightIn = arcWeightIn, arcWeightOut = arcWeightOut, firingIn = fireIn and not disTransitionIn, firingOut = fireOut and not disTransitionOut);
+//calculation of continuous mark change
+ conMarkChange = firingSumIn.conFiringSum - firingSumOut.conFiringSum;
+ der(t_) = conMarkChange;
+//calculation of discrete mark change
+ disMarkChange = firingSumIn.disFiringSum - firingSumOut.disFiringSum;
+ disMarksInOut = pre(disMarksOut.anytrue) or pre(disMarksIn.anytrue);
+ when {disMarksInOut, reStart} then
+ reinit(t_, if reStart then reStartMarks else t_ + pre(disMarkChange));
+ end when;
+//Conversion of tokens to level concentrations
+ levelCon = t*settings.M/N;
+ for i in 1:nOut loop
+ preFireOut[i] = if disTransitionOut[i] then fireOut[i] else pre(fireOut[i]);
+ end for;
+ for i in 1:nIn loop
+ preFireIn[i] = if disTransitionIn[i] then fireIn[i] else pre(fireIn[i]);
+ end for;
+ t = noEvent(if t_ < minMarks then minMarks elseif t_ > maxMarks then maxMarks else t_);
+//****MAIN END****//
+//****ANIMATION BEGIN****//
+//scaling of tokens for animation
+ tokenscale = t*settings.scale;
+ color = if settings.animatePlace then if tokenscale < 100 then {255, 255 - 2.55*tokenscale, 255 - 2.55*tokenscale} else {255, 0, 0} else {255, 255, 255};
+//****ANIMATION END****//
+//****ERROR MESSENGES BEGIN****//
+ assert(PNlib.Functions.OddsAndEnds.prioCheck(enablingPrioIn, nIn) or nIn == 0 or enablingType == PNlib.Types.EnablingType.Probability, "The priorities of the input priorities may be given only once and must be selected from 1 to nIn");
+ assert(PNlib.Functions.OddsAndEnds.prioCheck(enablingPrioOut, nOut) or nOut == 0 or enablingType == PNlib.Types.EnablingType.Probability, "The priorities of the output priorities may be given only once and must be selected from 1 to nOut");
+ assert(PNlib.Functions.OddsAndEnds.isEqual(sum(enablingProbIn), 1.0, 1e-6) or nIn == 0 or enablingType == PNlib.Types.EnablingType.Priority, "The sum of input enabling probabilities has to be equal to 1");
+ assert(PNlib.Functions.OddsAndEnds.isEqual(sum(enablingProbOut), 1.0, 1e-6) or nOut == 0 or enablingType == PNlib.Types.EnablingType.Priority, "The sum of output enabling probabilities has to be equal to 1");
+ assert(startMarks >= minMarks and startMarks <= maxMarks, "minMarks<=startMarks<=maxMarks");
+//****ERROR MESSENGES END****//
+ annotation(
+ defaultComponentName = "P1",
+ Icon(graphics = {Ellipse(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 98}, {100, -96}}), Ellipse(fillColor = DynamicSelect({255, 255, 255}, color), fillPattern = FillPattern.Solid, extent = {{-88, 86}, {88, -86}}), Text(origin = {-7, -44}, extent = {{-227, 108}, {227, -108}}, textString = "~", fontName = "Arial"), Polygon(origin = {42, -46}, fillPattern = FillPattern.Solid, points = {{-22, 26}, {-22, -26}, {22, 0}, {-22, 26}, {-22, 26}})}),
+ Diagram(graphics));
+end EnergeticFlowPlace;
diff --git a/Backend/EnergeticTransition.mo b/Backend/EnergeticTransition.mo
new file mode 100644
index 0000000000000000000000000000000000000000..bd67f2f88ad2b26a7944db8377661a22d4a555f9
--- /dev/null
+++ b/Backend/EnergeticTransition.mo
@@ -0,0 +1,107 @@
+within PNRG.Backend;
+
+model EnergeticTransition
+ parameter Integer nIn(min = 0) = 0 "number of input places" annotation(
+ Dialog(enable = true, group = "Connector sizing"));
+ parameter Integer nInAct(min = 0) = 0 "number of activators input places" annotation(
+ Dialog(enable = true, group = "Connector sizing"));
+ parameter Integer nOut(min = 0) = 0 "number of output places" annotation(
+ Dialog(enable = true, group = "Connector sizing"));
+ //****MODIFIABLE PARAMETERS AND VARIABLES BEGIN****//
+ Real power;
+ Real maximumSpeed = 1 "maximum speed" annotation(
+ Dialog(enable = true, group = "Maximum Speed"));
+ Real arcWeightInActivator[nInAct] = fill(1, nInAct) "arc weights of activator input places" annotation(
+ Dialog(enable = true, group = "Arc Weights"));
+ Real arcWeightOut[nOut] = fill(1, nOut) "arc weights of output places" annotation(
+ Dialog(enable = true, group = "Arc Weights"));
+ Boolean firingCon = true "additional firing condition" annotation(
+ Dialog(enable = true, group = "Firing Condition"));
+ //****MODIFIABLE PARAMETERS AND VARIABLES END****//
+ Boolean fire "Does the transition fire?";
+ Real instantaneousSpeed "instantaneous speed";
+ Real actualSpeed = if fire then instantaneousSpeed else 0.0;
+ Boolean showTransitionName = settings.showTransitionName "only for transition animation and display (Do not change!)";
+ Boolean animateSpeed = settings.animateSpeed "only for transition animation and display (Do not change!)";
+ Real color[3] "only for transition animation and display (Do not change!)";
+ PNlib.Interfaces.TransitionIn[nIn] inPlaces(each active = activation.active, each fire = fire, arcWeight = arcWeightIn[1:nIn], arcWeightint = arcWeightIntIn[1:nIn], each disTransition = false, each instSpeed = instantaneousSpeed, each prelimSpeed = prelimSpeed, each maxSpeed = maximumSpeed, t = tIn[1:nIn], tint = tIntIn[1:nIn], arcType = arcType[1:nIn], minTokens = minTokens[1:nIn], minTokensint = minTokensInt[1:nIn], fed = fed[1:nIn], disPlace = disPlaceIn[1:nIn], enable = enableIn[1:nIn], speedSum = speedSumIn[1:nIn], decreasingFactor = decreasingFactorIn[1:nIn], testValue = testValue[1:nIn], testValueint = testValueInt[1:nIn], normalArc = normalArc[1:nIn]) if nIn > 0 "connector for input places" annotation(
+ Placement(visible = true, transformation(origin = {0, -50}, extent = {{-56, -10}, {-40, 10}}, rotation = 0), iconTransformation(origin = {0, -50}, extent = {{-56, -10}, {-40, 10}}, rotation = 0)));
+ PNlib.Interfaces.TransitionOut[nOut] outPlaces(each active = activation.active, each fire = fire, each enabledByInPlaces = true, arcWeight = arcWeightOut, arcWeightint = arcWeightIntOut, each disTransition = false, each instSpeed = instantaneousSpeed, each prelimSpeed = prelimSpeed, each maxSpeed = maximumSpeed, t = tOut, tint = tIntOut, maxTokens = maxTokens, maxTokensint = maxTokensInt, emptied = emptied, disPlace = disPlaceOut, speedSum = speedSumOut, decreasingFactor = decreasingFactorOut) if nOut > 0 "connector for output places" annotation(
+ Placement(transformation(extent = {{40, -10}, {56, 10}}, rotation = 0)));
+ PNlib.Interfaces.TransitionIn[nInAct] activatorInPlace(each active = activation.active, arcType = arcType[nIn + 1:nIn + nInAct], arcWeight = arcWeightIn[nIn + 1:nIn + nInAct], arcWeightint = arcWeightIntIn[nIn + 1:nIn + nInAct], decreasingFactor = decreasingFactorIn[nIn + 1:nIn + nInAct], disPlace = disPlaceIn[nIn + 1:nIn + nInAct], each disTransition = false, enable = enableIn[nIn + 1:nIn + nInAct], fed = fed[nIn + 1:nIn + nInAct], each fire = fire, each instSpeed = instantaneousSpeed, each maxSpeed = maximumSpeed, minTokens = minTokens[nIn + 1:nIn + nInAct], minTokensint = minTokensInt[nIn + 1:nIn + nInAct], normalArc = normalArc[nIn + 1:nIn + nInAct], each prelimSpeed = prelimSpeed, speedSum = speedSumIn[nIn + 1:nIn + nInAct], t = tIn[nIn + 1:nIn + nInAct], testValue = testValue[nIn + 1:nIn + nInAct], testValueint = testValueInt[nIn + 1:nIn + nInAct], tint = tIntIn[nIn + 1:nIn + nInAct]) annotation(
+ Placement(visible = true, transformation(origin = {0, 50}, extent = {{-56, -10}, {-40, 10}}, rotation = 0), iconTransformation(origin = {0, 50}, extent = {{-56, -10}, {-40, 10}}, rotation = 0)));
+protected
+ outer PNlib.Components.Settings settings "global settings for animation and display";
+ Real arcWeightIn[nIn + nInAct] "arc weights of input places" annotation(
+ Dialog(enable = true, group = "Arc Weights"));
+ Real prelimSpeed "preliminary speed";
+ Real tIn[nIn + nInAct] "tokens of input places";
+ Real tOut[nOut] "tokens of output places";
+ Real minTokens[nIn + nInAct] "minimum tokens of input places";
+ Real maxTokens[nOut] "maximum tokens of output places";
+ Real speedSumIn[nIn + nInAct] "Input speeds of continuous input places";
+ Real speedSumOut[nOut] "Output speeds of continuous output places";
+ Real decreasingFactorIn[nIn + nInAct] "decreasing factors of input places";
+ Real decreasingFactorOut[nOut] "decreasing factors of output places";
+ Real testValue[nIn + nInAct] "test values of test or inhibitor arcs";
+ PNlib.Types.ArcType arcType[nIn + nInAct] "type of input arcs 1=normal, 2=real test arc, 3=test arc, 4=real inhibitor arc, 5=inhibitor arc, 6=read arc";
+ Integer arcWeightIntIn[nIn + nInAct] "Integer arc weights of discrete input places (for generating events!)";
+ Integer arcWeightIntOut[nOut] "Integer arc weights of discrete output places (for generating events!)";
+ Integer minTokensInt[nIn + nInAct] "Integer minimum tokens of input places (for generating events!)";
+ Integer maxTokensInt[nOut] "Integer maximum tokens of output places (for generating events!)";
+ Integer tIntIn[nIn + nInAct] "integer tokens of input places (for generating events!)";
+ Integer tIntOut[nOut] "integer tokens of output places (for generating events!)";
+ Integer testValueInt[nIn + nInAct] "Integer test values of input arcs (for generating events!)";
+ Boolean normalArc[nIn + nInAct] "1=no, 2=yes, i.e. double arc: test and normal arc or inhibitor and normal arc";
+ Boolean fed[nIn + nInAct] "Are the input places fed by their input transitions?";
+ Boolean emptied[nOut] "Are the output places emptied by their output transitions?";
+ Boolean disPlaceIn[nIn + nInAct] "Are the input places discrete?";
+ Boolean disPlaceOut[nOut] "Are the output places discrete?";
+ Boolean enableIn[nIn + nInAct] "Is the transition enabled by all its discrete input transitions?";
+ //****BLOCKS BEGIN****// since no events are generated within functions!!!
+ //activation process
+ PNlib.Blocks.activationCon activation(testValue = testValue, testValueInt = testValueInt, normalArc = normalArc, nIn = nIn, nOut = nOut, tIn = tIn, tOut = tOut, tIntIn = tIntIn, tIntOut = tIntOut, arcType = arcType, arcWeightIn = arcWeightIn, arcWeightOut = arcWeightOut, arcWeightIntIn = arcWeightIntIn, arcWeightIntOut = arcWeightIntOut, minTokens = minTokens, maxTokens = maxTokens, minTokensInt = minTokensInt, maxTokensInt = maxTokensInt, firingCon = firingCon, fed = fed, emptied = emptied, disPlaceIn = disPlaceIn, disPlaceOut = disPlaceOut);
+ //PNlib.Blocks.activationCon activation(testValue = testValue[1:nIn], testValueInt = testValueInt[1:nIn], normalArc = normalArc[1:nIn], nIn = nIn, nOut = nOut, tIn = tIn[1:nIn], tOut = tOut, tIntIn = tIntIn[1:nIn], tIntOut = tIntOut[1:nIn], arcType = arcType[1:nIn], arcWeightIn = arcWeightIn[1:nIn], arcWeightOut = arcWeightOut[1:nIn], arcWeightIntIn = arcWeightIntIn[1:nIn], arcWeightIntOut = arcWeightIntOut[1:nIn], minTokens = minTokens[1:nIn], maxTokens = maxTokens[1:nIn], minTokensInt = minTokensInt[1:nIn], maxTokensInt = maxTokensInt[1:nIn], firingCon = firingCon, fed = fed[1:nIn], emptied = emptied[1:nIn], disPlaceIn = disPlaceIn[1:nIn], disPlaceOut = disPlaceOut[1:nIn]);
+ //PNlib.Blocks.activationCon activatorActivation(testValue = testValue[nIn+1:nIn+nInAct], testValueInt = testValueInt[nIn+1:nIn+nInAct], normalArc = normalArc[nIn+1:nIn+nInAct], nIn = nInAct, nOut = nOut, tIn = tIn[nIn+1:nIn+nInAct], tOut = tOut[nIn+1:nIn+nInAct], tIntIn = tIntIn[nIn+1:nIn+nInAct], tIntOut = tIntOut, arcType = arcType[nIn+1:nIn+nInAct], arcWeightIn = arcWeightIn[nIn+1:nIn+nInAct], arcWeightOut = arcWeightOut[nIn+1:nIn+nInAct], arcWeightIntIn = arcWeightIntIn[nIn+1:nIn+nInAct], arcWeightIntOut = arcWeightIntOut[nIn+1:nIn+nInAct], minTokens = minTokens[nIn+1:nIn+nInAct], maxTokens = maxTokens[nIn+1:nIn+nInAct], minTokensInt = minTokensInt[nIn+1:nIn+nInAct], maxTokensInt = maxTokensInt[nIn+1:nIn+nInAct], firingCon = firingCon, fed = fed[nIn+1:nIn+nInAct], emptied = emptied[nIn+1:nIn+nInAct], disPlaceIn = disPlaceIn[nIn+1:nIn+nInAct], disPlaceOut = disPlaceOut[nIn+1:nIn+nInAct]);
+ //firing process
+ Boolean fire_ = PNlib.Functions.OddsAndEnds.allTrue(/* hack for Dymola 2017 */PNlib.Functions.OddsAndEnds.boolOr(enableIn, not disPlaceIn));
+ //****BLOCKS END****//
+equation
+//****MAIN BEGIN****//
+//preliminary speed calculation
+ arcWeightIn[nIn + 1:nIn + nInAct] = arcWeightInActivator;
+ prelimSpeed = PNlib.Functions.preliminarySpeed(nIn = nIn + nInAct, nOut = nOut, arcWeightIn = arcWeightIn, arcWeightOut = arcWeightOut, speedSumIn = speedSumIn, speedSumOut = speedSumOut, maximumSpeed = maximumSpeed, weaklyInputActiveVec = activation.weaklyInputActiveVec, weaklyOutputActiveVec = activation.weaklyOutputActiveVec);
+//firing process
+ fire = fire_ and activation.active and not maximumSpeed <= 0;
+//instantaneous speed calculation
+ instantaneousSpeed = min(min(min(decreasingFactorIn), min(decreasingFactorOut))*maximumSpeed, prelimSpeed);
+//****MAIN END****//
+//****ANIMATION BEGIN****//
+ color = if (fire and settings.animateTransition) then {255, 255, 0} else {255, 255, 255};
+//****ANIMATION END****//
+//****ERROR MESSENGES BEGIN****// hier noch Message gleiches Kantengewicht und auch Kante dis Place!!
+ power = sum(arcWeightIn[1:nIn]);
+ for i in 1:nIn loop
+ if disPlaceIn[i] then
+ arcWeightIntIn[i] = integer(arcWeightIn[i]);
+ else
+ arcWeightIntIn[i] = 1;
+ end if;
+ assert((disPlaceIn[i] and arcWeightIn[i] - arcWeightIntIn[i] <= 0.0) or not disPlaceIn[i], "Input arcs connected to discrete places must have integer weights.");
+ assert(arcWeightIn[i] >= 0, "Input arc weights must be positive.");
+ end for;
+ for i in 1:nOut loop
+ if disPlaceOut[i] then
+ arcWeightIntOut[i] = integer(arcWeightOut[i]);
+ else
+ arcWeightIntOut[i] = 1;
+ end if;
+ assert((disPlaceOut[i] and arcWeightOut[i] - arcWeightIntOut[i] <= 0.0) or not disPlaceOut[i], "Output arcs connected to discrete places must have integer weights.");
+ assert(arcWeightOut[i] >= 0, "Output arc weights must be positive.");
+ end for;
+//****ERROR MESSENGES END****//
+ annotation(
+ defaultComponentName = "T1",
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-40, 100}, {40, -100}}), Text(origin = {-3, -1}, extent = {{-151, 67}, {151, -67}}, textString = "~", fontName = "Arial")}),
+ Diagram);
+end EnergeticTransition;
diff --git a/Backend/EnergeticTransitionWithoutActivator.mo b/Backend/EnergeticTransitionWithoutActivator.mo
new file mode 100644
index 0000000000000000000000000000000000000000..c7654d529feac30b0f6d8799ab98f8b525a2b065
--- /dev/null
+++ b/Backend/EnergeticTransitionWithoutActivator.mo
@@ -0,0 +1,101 @@
+within PNRG.Backend;
+
+// The following code is modified Code from PNlib library.
+
+model EnergeticTransitionWithoutActivator
+ parameter Integer nIn(min = 0) = 0 "number of input places" annotation(
+ Dialog(enable = true, group = "Connector sizing"));
+ parameter Integer nOut(min = 0) = 0 "number of output places" annotation(
+ Dialog(enable = true, group = "Connector sizing"));
+ //****MODIFIABLE PARAMETERS AND VARIABLES BEGIN****//
+ Real power;
+ Real maximumSpeed = 1 "maximum speed" annotation(
+ Dialog(enable = true, group = "Maximum Speed"));
+ Real arcWeightOut[nOut] = fill(1, nOut) "arc weights of output places" annotation(
+ Dialog(enable = true, group = "Arc Weights"));
+ Boolean firingCon = true "additional firing condition" annotation(
+ Dialog(enable = true, group = "Firing Condition"));
+ //****MODIFIABLE PARAMETERS AND VARIABLES END****//
+ Boolean fire "Does the transition fire?";
+ Real instantaneousSpeed "instantaneous speed";
+ Real actualSpeed = if fire then instantaneousSpeed else 0.0;
+ Boolean showTransitionName = settings.showTransitionName "only for transition animation and display (Do not change!)";
+ Boolean animateSpeed = settings.animateSpeed "only for transition animation and display (Do not change!)";
+ Real color[3] "only for transition animation and display (Do not change!)";
+ PNlib.Interfaces.TransitionIn[nIn] inPlaces(each active = activation.active, each fire = fire, arcWeight = arcWeightIn[1:nIn], arcWeightint = arcWeightIntIn[1:nIn], each disTransition = false, each instSpeed = instantaneousSpeed, each prelimSpeed = prelimSpeed, each maxSpeed = maximumSpeed, t = tIn[1:nIn], tint = tIntIn[1:nIn], arcType = arcType[1:nIn], minTokens = minTokens[1:nIn], minTokensint = minTokensInt[1:nIn], fed = fed[1:nIn], disPlace = disPlaceIn[1:nIn], enable = enableIn[1:nIn], speedSum = speedSumIn[1:nIn], decreasingFactor = decreasingFactorIn[1:nIn], testValue = testValue[1:nIn], testValueint = testValueInt[1:nIn], normalArc = normalArc[1:nIn]) if nIn > 0 "connector for input places" annotation(
+ Placement(visible = true, transformation(origin = {0, 0}, extent = {{-56, -10}, {-40, 10}}, rotation = 0), iconTransformation(origin = {0, 0}, extent = {{-56, -10}, {-40, 10}}, rotation = 0)));
+ PNlib.Interfaces.TransitionOut[nOut] outPlaces(each active = activation.active, each fire = fire, each enabledByInPlaces = true, arcWeight = arcWeightOut, arcWeightint = arcWeightIntOut, each disTransition = false, each instSpeed = instantaneousSpeed, each prelimSpeed = prelimSpeed, each maxSpeed = maximumSpeed, t = tOut, tint = tIntOut, maxTokens = maxTokens, maxTokensint = maxTokensInt, emptied = emptied, disPlace = disPlaceOut, speedSum = speedSumOut, decreasingFactor = decreasingFactorOut) if nOut > 0 "connector for output places" annotation(
+ Placement(transformation(extent = {{40, -10}, {56, 10}}, rotation = 0)));
+protected
+ outer PNlib.Components.Settings settings "global settings for animation and display";
+ Real arcWeightIn[nIn] "arc weights of input places" annotation(
+ Dialog(enable = true, group = "Arc Weights"));
+ Real prelimSpeed "preliminary speed";
+ Real tIn[nIn] "tokens of input places";
+ Real tOut[nOut] "tokens of output places";
+ Real minTokens[nIn] "minimum tokens of input places";
+ Real maxTokens[nOut] "maximum tokens of output places";
+ Real speedSumIn[nIn] "Input speeds of continuous input places";
+ Real speedSumOut[nOut] "Output speeds of continuous output places";
+ Real decreasingFactorIn[nIn] "decreasing factors of input places";
+ Real decreasingFactorOut[nOut] "decreasing factors of output places";
+ Real testValue[nIn] "test values of test or inhibitor arcs";
+ PNlib.Types.ArcType arcType[nIn] "type of input arcs 1=normal, 2=real test arc, 3=test arc, 4=real inhibitor arc, 5=inhibitor arc, 6=read arc";
+ Integer arcWeightIntIn[nIn] "Integer arc weights of discrete input places (for generating events!)";
+ Integer arcWeightIntOut[nOut] "Integer arc weights of discrete output places (for generating events!)";
+ Integer minTokensInt[nIn] "Integer minimum tokens of input places (for generating events!)";
+ Integer maxTokensInt[nOut] "Integer maximum tokens of output places (for generating events!)";
+ Integer tIntIn[nIn] "integer tokens of input places (for generating events!)";
+ Integer tIntOut[nOut] "integer tokens of output places (for generating events!)";
+ Integer testValueInt[nIn] "Integer test values of input arcs (for generating events!)";
+ Boolean normalArc[nIn] "1=no, 2=yes, i.e. double arc: test and normal arc or inhibitor and normal arc";
+ Boolean fed[nIn] "Are the input places fed by their input transitions?";
+ Boolean emptied[nOut] "Are the output places emptied by their output transitions?";
+ Boolean disPlaceIn[nIn] "Are the input places discrete?";
+ Boolean disPlaceOut[nOut] "Are the output places discrete?";
+ Boolean enableIn[nIn] "Is the transition enabled by all its discrete input transitions?";
+ //****BLOCKS BEGIN****// since no events are generated within functions!!!
+ //activation process
+ PNlib.Blocks.activationCon activation(testValue = testValue, testValueInt = testValueInt, normalArc = normalArc, nIn = nIn, nOut = nOut, tIn = tIn, tOut = tOut, tIntIn = tIntIn, tIntOut = tIntOut, arcType = arcType, arcWeightIn = arcWeightIn, arcWeightOut = arcWeightOut, arcWeightIntIn = arcWeightIntIn, arcWeightIntOut = arcWeightIntOut, minTokens = minTokens, maxTokens = maxTokens, minTokensInt = minTokensInt, maxTokensInt = maxTokensInt, firingCon = firingCon, fed = fed, emptied = emptied, disPlaceIn = disPlaceIn, disPlaceOut = disPlaceOut);
+
+ Boolean fire_ = PNlib.Functions.OddsAndEnds.allTrue(/* hack for Dymola 2017 */PNlib.Functions.OddsAndEnds.boolOr(enableIn, not disPlaceIn));
+ //****BLOCKS END****//
+equation
+//****MAIN BEGIN****//
+//preliminary speed calculation
+ prelimSpeed = PNlib.Functions.preliminarySpeed(nIn = nIn, nOut = nOut, arcWeightIn = arcWeightIn, arcWeightOut = arcWeightOut, speedSumIn = speedSumIn, speedSumOut = speedSumOut, maximumSpeed = maximumSpeed, weaklyInputActiveVec = activation.weaklyInputActiveVec, weaklyOutputActiveVec = activation.weaklyOutputActiveVec);
+//firing process
+ fire = fire_ and activation.active and not maximumSpeed <= 0;
+//instantaneous speed calculation
+ instantaneousSpeed = min(min(min(decreasingFactorIn), min(decreasingFactorOut))*maximumSpeed, prelimSpeed);
+//****MAIN END****//
+//****ANIMATION BEGIN****//
+ color = if (fire and settings.animateTransition) then {255, 255, 0} else {255, 255, 255};
+//****ANIMATION END****//
+//****ERROR MESSENGES BEGIN****// hier noch Message gleiches Kantengewicht und auch Kante dis Place!!
+ power = sum(arcWeightIn[1:nIn]);
+ for i in 1:nIn loop
+ if disPlaceIn[i] then
+ arcWeightIntIn[i] = integer(arcWeightIn[i]);
+ else
+ arcWeightIntIn[i] = 1;
+ end if;
+ assert((disPlaceIn[i] and arcWeightIn[i] - arcWeightIntIn[i] <= 0.0) or not disPlaceIn[i], "Input arcs connected to discrete places must have integer weights.");
+ assert(arcWeightIn[i] >= 0, "Input arc weights must be positive.");
+ end for;
+ for i in 1:nOut loop
+ if disPlaceOut[i] then
+ arcWeightIntOut[i] = integer(arcWeightOut[i]);
+ else
+ arcWeightIntOut[i] = 1;
+ end if;
+ assert((disPlaceOut[i] and arcWeightOut[i] - arcWeightIntOut[i] <= 0.0) or not disPlaceOut[i], "Output arcs connected to discrete places must have integer weights.");
+ assert(arcWeightOut[i] >= 0, "Output arc weights must be positive.");
+ end for;
+//****ERROR MESSENGES END****//
+ annotation(
+ defaultComponentName = "T1",
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-40, 100}, {40, -100}}), Text(origin = {-3, -1}, extent = {{-151, 67}, {151, -67}}, textString = "~", fontName = "Arial")}),
+ Diagram);
+
+end EnergeticTransitionWithoutActivator;
diff --git a/Backend/package.mo b/Backend/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..bfc8ed042fbcd99b16f2baf6d200f477577a16ca
--- /dev/null
+++ b/Backend/package.mo
@@ -0,0 +1,11 @@
+within PNRG;
+
+package Backend
+
+
+
+
+
+ annotation(
+ Icon(graphics = {Ellipse(lineThickness = 1.5, extent = {{-90, 90}, {90, -90}}), Ellipse(origin = {34, 10}, fillPattern = FillPattern.Solid, extent = {{-22, 22}, {22, -22}}), Ellipse(origin = {-34, -16}, fillPattern = FillPattern.Solid, extent = {{-22, 22}, {22, -22}})}));
+end Backend;
diff --git a/Backend/package.order b/Backend/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..5d18024ea8dc736800ad1aac0e412e52a93b4a2d
--- /dev/null
+++ b/Backend/package.order
@@ -0,0 +1,3 @@
+EnergeticFlowPlace
+EnergeticTransition
+EnergeticTransitionWithoutActivator
diff --git a/Distribution/BaseTransmissionLine.mo b/Distribution/BaseTransmissionLine.mo
new file mode 100644
index 0000000000000000000000000000000000000000..a3906e7d7b75271b989473561fb19ef17af19e32
--- /dev/null
+++ b/Distribution/BaseTransmissionLine.mo
@@ -0,0 +1,39 @@
+within PNRG.Distribution;
+
+model BaseTransmissionLine
+ parameter Integer NIn "Number of Inputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer prioOut[NOut] "Priority of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Real Voltage(unit = "V") = 1 "Voltage of grid" annotation(
+ Dialog(enable = true, group = "Grid properties"));
+ Real powerDifference(unit = "kW") "Difference between power suply and consumption";
+ Real powerInput(unit = "kW");
+ Real totalLoad(unit = "kW");
+ PNRG.Interfaces.ElectricalInput electricalInput[NIn] annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.ElectricalOutput electricalOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, nIn = NIn, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace p1(nIn = 1, nOut = NOut) annotation(
+ Placement(visible = true, transformation(origin = {58, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerInput = t1.power;
+ totalLoad = sum(electricalOutput.arcWeight);
+ powerDifference = powerInput - totalLoad;
+ for i in 1:NIn loop
+ connect(electricalInput[i], t1.inPlaces[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ for i in 1:NOut loop
+ connect(electricalOutput[i], p1.outTransition[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-32, 0}, {48, 0}}, thickness = 0.5));
+ annotation(
+ Icon(graphics = {Rectangle(origin = {0, 80}, fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 20}, {100, -20}}), Line(origin = {-76.3724, 71.0217}, points = {{-4, -11.9568}, {-2, 10.0432}, {-12, 10.0432}, {-8, 12.0432}, {8, 12.0432}, {12, 10.0432}, {2, 10.0432}, {4, -11.9568}, {-4, -11.9568}, {4, -7.95679}, {-4, -7.95679}, {4, -3.95679}, {-4, -3.95679}, {2, 0.0432145}, {-2, 0.04321}, {2, 4.04321}, {-2, 4.04321}, {2, 8.04321}, {-2, 8.04321}, {2, 10.0432}, {4, 12.0432}, {6, 10.0432}, {8, 12.0432}, {10, 10.0432}, {-2, 10.0432}, {2, 10.0432}, {0, 12.0432}, {-2, 10.0432}, {-4, 12.0432}, {-6, 10.0432}, {-8, 12.0432}, {-10, 10.0432}, {-2, 10.0432}, {2, 8.04321}, {-2, 8.04321}, {2, 4.04321}, {-2, 4.04321}, {2, 0.0432145}, {-2, 0.0432145}, {4, -3.95679}, {-4, -3.95679}, {4, -7.95679}, {-4, -7.95679}, {4, -11.9568}, {4, -11.9568}}), Line(origin = {75.3321, 71.0217}, points = {{-4, -11.9568}, {-2, 10.0432}, {-12, 10.0432}, {-8, 12.0432}, {8, 12.0432}, {12, 10.0432}, {2, 10.0432}, {4, -11.9568}, {-4, -11.9568}, {4, -7.95679}, {-4, -7.95679}, {4, -3.95679}, {-4, -3.95679}, {2, 0.0432145}, {-2, 0.04321}, {2, 4.04321}, {-2, 4.04321}, {2, 8.04321}, {-2, 8.04321}, {2, 10.0432}, {4, 12.0432}, {6, 10.0432}, {8, 12.0432}, {10, 10.0432}, {-2, 10.0432}, {2, 10.0432}, {0, 12.0432}, {-2, 10.0432}, {-4, 12.0432}, {-6, 10.0432}, {-8, 12.0432}, {-10, 10.0432}, {-2, 10.0432}, {2, 8.04321}, {-2, 8.04321}, {2, 4.04321}, {-2, 4.04321}, {2, 0.0432145}, {-2, 0.0432145}, {4, -3.95679}, {-4, -3.95679}, {4, -7.95679}, {-4, -7.95679}, {4, -11.9568}, {4, -11.9568}}), Line(origin = {-1.89405, 81.123}, points = {{-67, 0}, {67, 0}, {67, 0}}), Line(origin = {0.0696685, 80.3301}, points = {{-84.9637, 0.792893}, {-82.9637, -1.20711}, {81.0363, -1.20711}, {85.0363, 0.792893}, {85.0363, 0.792893}}), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name")}, coordinateSystem(extent = {{-120, 40}, {120, -40}})));
+end BaseTransmissionLine;
diff --git a/Distribution/ConditionalConnection.mo b/Distribution/ConditionalConnection.mo
new file mode 100644
index 0000000000000000000000000000000000000000..7c94a01d5b6667550e75df7a1da198e58ecf8ad8
--- /dev/null
+++ b/Distribution/ConditionalConnection.mo
@@ -0,0 +1,39 @@
+within PNRG.Distribution;
+
+model ConditionalConnection
+ Real power annotation(
+ Dialog(enable = true, group = "General properties"));
+ Real currentPower;
+ Interfaces.ElectricalInput electricalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.ElectricalOutput electricalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {60, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {0, -110}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {0, -110}, extent = {{-10, -10}, {10, 10}}, rotation = 90)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {0, -80}, extent = {{-10, -10}, {10, 10}}, rotation = 90)));
+ PNlib.Components.TC t1(arcWeightIn = {currentPower}, arcWeightOut = {currentPower}, firingCon = logicalInput.t == 1, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t11(arcWeightIn = {2, 2}, firingCon = false, nIn = 2) annotation(
+ Placement(visible = true, transformation(origin = {26, -56}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ currentPower = power*logicalInput.t;
+ connect(p1.outTransition[1], electricalOutput) annotation(
+ Line(points = {{70, 0}, {110, 0}}));
+ connect(logicalInput, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{0, -110}, {0, -90}}));
+ connect(splitLogicalInput.inhibitor_output, t11.inPlaces[1]) annotation(
+ Line(points = {{2, -70}, {2, -56}, {21, -56}}));
+ connect(electricalInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-4, 0}}));
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{4, 0}, {50, 0}}, thickness = 0.5));
+ connect(splitLogicalInput.test_output, t11.inPlaces[2]) annotation(
+ Line(points = {{-2, -70}, {-2, -56}, {21, -56}}));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name"), Rectangle(origin = {-65, 0}, fillColor = {255, 200, 0}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-35, 6}, {35, -6}}), Rectangle(origin = {65, 0}, fillColor = {255, 200, 0}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-35, 6}, {35, -6}}), Rectangle(origin = {-10, 23}, rotation = 45, fillColor = {255, 200, 0}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-35, 6}, {35, -6}})}));
+
+
+end ConditionalConnection;
diff --git a/Distribution/DistributionPowerGrid.mo b/Distribution/DistributionPowerGrid.mo
new file mode 100644
index 0000000000000000000000000000000000000000..c1dc50700fc8118402dd3094b44be0990cdc698d
--- /dev/null
+++ b/Distribution/DistributionPowerGrid.mo
@@ -0,0 +1,40 @@
+within PNRG.Distribution;
+
+model DistributionPowerGrid
+parameter Integer NIn "Number of Inputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer prioOut[NOut] "Priority of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Real Voltage(unit = "V") = 1 "Voltage of grid" annotation(
+ Dialog(enable = true, group = "Grid properties"));
+ Real powerDifference(unit = "kW") "Difference between power suply and consumption";
+ Real powerInput(unit = "kW");
+ Real totalLoad(unit = "kW");
+ PNRG.Interfaces.ElectricalInput electricalInput[NIn] annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.ElectricalOutput electricalOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, nIn = NIn, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p1(enablingPrioOut = prioOut, nIn = 1, nOut = NOut) annotation(
+ Placement(visible = true, transformation(origin = {46, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerInput = t1.power;
+ totalLoad = sum(electricalOutput.arcWeight);
+ powerDifference = powerInput - totalLoad;
+ for i in 1:NIn loop
+ connect(electricalInput[i], t1.inPlaces[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ for i in 1:NOut loop
+ connect(electricalOutput[i], p1.outTransition[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-32, 0}, {36, 0}}, thickness = 0.5));
+ annotation(
+ Icon(graphics = {Rectangle(origin = {0, 80}, fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 20}, {100, -20}}), Line(origin = {-76.3724, 71.0217}, points = {{-4, -11.9568}, {-2, 10.0432}, {-12, 10.0432}, {-8, 12.0432}, {8, 12.0432}, {12, 10.0432}, {2, 10.0432}, {4, -11.9568}, {-4, -11.9568}, {4, -7.95679}, {-4, -7.95679}, {4, -3.95679}, {-4, -3.95679}, {2, 0.0432145}, {-2, 0.04321}, {2, 4.04321}, {-2, 4.04321}, {2, 8.04321}, {-2, 8.04321}, {2, 10.0432}, {4, 12.0432}, {6, 10.0432}, {8, 12.0432}, {10, 10.0432}, {-2, 10.0432}, {2, 10.0432}, {0, 12.0432}, {-2, 10.0432}, {-4, 12.0432}, {-6, 10.0432}, {-8, 12.0432}, {-10, 10.0432}, {-2, 10.0432}, {2, 8.04321}, {-2, 8.04321}, {2, 4.04321}, {-2, 4.04321}, {2, 0.0432145}, {-2, 0.0432145}, {4, -3.95679}, {-4, -3.95679}, {4, -7.95679}, {-4, -7.95679}, {4, -11.9568}, {4, -11.9568}}), Line(origin = {75.3321, 71.0217}, points = {{-4, -11.9568}, {-2, 10.0432}, {-12, 10.0432}, {-8, 12.0432}, {8, 12.0432}, {12, 10.0432}, {2, 10.0432}, {4, -11.9568}, {-4, -11.9568}, {4, -7.95679}, {-4, -7.95679}, {4, -3.95679}, {-4, -3.95679}, {2, 0.0432145}, {-2, 0.04321}, {2, 4.04321}, {-2, 4.04321}, {2, 8.04321}, {-2, 8.04321}, {2, 10.0432}, {4, 12.0432}, {6, 10.0432}, {8, 12.0432}, {10, 10.0432}, {-2, 10.0432}, {2, 10.0432}, {0, 12.0432}, {-2, 10.0432}, {-4, 12.0432}, {-6, 10.0432}, {-8, 12.0432}, {-10, 10.0432}, {-2, 10.0432}, {2, 8.04321}, {-2, 8.04321}, {2, 4.04321}, {-2, 4.04321}, {2, 0.0432145}, {-2, 0.0432145}, {4, -3.95679}, {-4, -3.95679}, {4, -7.95679}, {-4, -7.95679}, {4, -11.9568}, {4, -11.9568}}), Line(origin = {-1.89405, 81.123}, points = {{-67, 0}, {67, 0}, {67, 0}}), Line(origin = {0.0696685, 80.3301}, points = {{-84.9637, 0.792893}, {-82.9637, -1.20711}, {81.0363, -1.20711}, {85.0363, 0.792893}, {85.0363, 0.792893}}), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name")}, coordinateSystem(extent = {{-120, 40}, {120, -40}})));
+
+end DistributionPowerGrid;
diff --git a/Distribution/HydrogenPipe.mo b/Distribution/HydrogenPipe.mo
new file mode 100644
index 0000000000000000000000000000000000000000..3f22655bbe16527a674037a7a0b1c8a79ff16e22
--- /dev/null
+++ b/Distribution/HydrogenPipe.mo
@@ -0,0 +1,43 @@
+within PNRG.Distribution;
+
+model HydrogenPipe
+
+parameter Integer NIn "Number of Inputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer prioOut[NOut] "Priority of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Real Voltage(unit = "V") = 1 "Voltage of grid" annotation(
+ Dialog(enable = true, group = "Grid properties"));
+ Real powerDifference(unit = "kW") "Difference between power suply and consumption";
+ Real powerInput(unit = "kW");
+ Real totalLoad(unit = "kW");
+ PNRG.Interfaces.HydrogenInput hydrogenInput[NIn] annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.HydrogenOutput hydrogenOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, nIn = NIn, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p1(enablingPrioOut = prioOut, nIn = 1, nOut = NOut) annotation(
+ Placement(visible = true, transformation(origin = {46, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerInput = t1.power;
+ totalLoad = sum(hydrogenOutput.arcWeight);
+ powerDifference = powerInput - totalLoad;
+ for i in 1:NIn loop
+ connect(hydrogenInput[i], t1.inPlaces[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ for i in 1:NOut loop
+ connect(hydrogenOutput[i], p1.outTransition[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-32, 0}, {36, 0}}, thickness = 0.5));
+ annotation(
+ Icon(graphics = {Rectangle(origin = {0, 80}, fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 20}, {100, -20}}), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name"), Ellipse(origin = {-82, 79}, fillColor = {106, 168, 79}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Rectangle(origin = {-2, 79}, fillColor = {106, 168, 79}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-80, 15}, {80, -15}}), Ellipse(origin = {78, 79}, fillColor = {106, 168, 79}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Ellipse(origin = {78, 79}, fillColor = {83, 130, 61}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-8, 13}, {8, -13}}), Line(origin = {-2, 94}, points = {{-80, 0}, {80, 0}, {80, 0}}), Line(origin = {-2, 64}, points = {{80, 0}, {-80, 0}})}, coordinateSystem(extent = {{-120, 40}, {120, -40}})));
+
+
+
+end HydrogenPipe;
diff --git a/Distribution/OxygenPipe.mo b/Distribution/OxygenPipe.mo
new file mode 100644
index 0000000000000000000000000000000000000000..60579bc26d2c7cd3d35c941fcb9aa78ccfbe1038
--- /dev/null
+++ b/Distribution/OxygenPipe.mo
@@ -0,0 +1,41 @@
+within PNRG.Distribution;
+
+model OxygenPipe
+parameter Integer NIn "Number of Inputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer prioOut[NOut] "Priority of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Real Voltage(unit = "V") = 1 "Voltage of grid" annotation(
+ Dialog(enable = true, group = "Grid properties"));
+ Real powerDifference(unit = "kW") "Difference between power suply and consumption";
+ Real powerInput(unit = "kW");
+ Real totalLoad(unit = "kW");
+ PNRG.Interfaces.OxygenInput oxygenInput[NIn] annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.OxygenOutput oxygenOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, nIn = NIn, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p1(enablingPrioOut = prioOut, nIn = 1, nOut = NOut) annotation(
+ Placement(visible = true, transformation(origin = {46, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerInput = t1.power;
+ totalLoad = sum(oxygenOutput.arcWeight);
+ powerDifference = powerInput - totalLoad;
+ for i in 1:NIn loop
+ connect(oxygenInput[i], t1.inPlaces[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ for i in 1:NOut loop
+ connect(oxygenOutput[i], p1.outTransition[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-32, 0}, {36, 0}}, thickness = 0.5));
+ annotation(
+ Icon(graphics = {Rectangle(origin = {0, 80}, fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 20}, {100, -20}}), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name"), Ellipse(origin = {-82, 79}, fillColor = {11, 83, 148}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Rectangle(origin = {-2, 79}, fillColor = {11, 83, 148}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-80, 15}, {80, -15}}), Ellipse(origin = {78, 79}, fillColor = {11, 83, 148}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Ellipse(origin = {78, 79}, fillColor = {7, 56, 99}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-8, 13}, {8, -13}}), Line(origin = {-2, 94}, points = {{-80, 0}, {80, 0}, {80, 0}}), Line(origin = {-2, 64}, points = {{80, 0}, {-80, 0}})}, coordinateSystem(extent = {{-120, 40}, {120, -40}})));
+
+
+end OxygenPipe;
diff --git a/Distribution/Transformer.mo b/Distribution/Transformer.mo
new file mode 100644
index 0000000000000000000000000000000000000000..0cbc5bbbd962e5d0b2f6442dca9d1c9d0ee7cce8
--- /dev/null
+++ b/Distribution/Transformer.mo
@@ -0,0 +1,27 @@
+within PNRG.Distribution;
+
+model Transformer
+ Real inputPower(unit = "kW");
+ Real outputPower(unit = "kW");
+ parameter Real efficiency "Energy conversion effiency" annotation(
+ Dialog(enable = true, group = "General properties"));
+ Interfaces.ElectricalInput electricalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.ElectricalOutput electricalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {60, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticTransitionWithoutActivator t11(arcWeightOut = {outputPower}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {2, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ inputPower = t11.power;
+ outputPower = inputPower*efficiency;
+ connect(t11.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{6, 0}, {50, 0}}, thickness = 0.5));
+ connect(p1.outTransition[1], electricalOutput) annotation(
+ Line(points = {{70, 0}, {110, 0}}));
+ connect(t11.inPlaces[1], electricalInput) annotation(
+ Line(points = {{-2, 0}, {-110, 0}}));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Ellipse(origin = {-40, -1}, lineColor = {255, 200, 0}, lineThickness = 5, extent = {{-56, 55}, {56, -55}}), Ellipse(origin = {40, -3}, lineColor = {255, 200, 0}, lineThickness = 5, extent = {{-56, 55}, {56, -55}}), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name")}));
+end Transformer;
diff --git a/Distribution/WaterPipe.mo b/Distribution/WaterPipe.mo
new file mode 100644
index 0000000000000000000000000000000000000000..4b5fa8b89b920aa6fc079449c3db4e351e085da7
--- /dev/null
+++ b/Distribution/WaterPipe.mo
@@ -0,0 +1,42 @@
+within PNRG.Distribution;
+
+model WaterPipe
+
+parameter Integer NIn "Number of Inputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer prioOut[NOut] "Priority of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Real Voltage(unit = "V") = 1 "Voltage of grid" annotation(
+ Dialog(enable = true, group = "Grid properties"));
+ Real powerDifference(unit = "kW") "Difference between power suply and consumption";
+ Real powerInput(unit = "kW");
+ Real totalLoad(unit = "kW");
+ PNRG.Interfaces.WaterInput waterInput[NIn] annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.WaterOutput waterOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, nIn = NIn, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p1(enablingPrioOut = prioOut, nIn = 1, nOut = NOut) annotation(
+ Placement(visible = true, transformation(origin = {46, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerInput = t1.power;
+ totalLoad = sum(waterOutput.arcWeight);
+ powerDifference = powerInput - totalLoad;
+ for i in 1:NIn loop
+ connect(waterInput[i], t1.inPlaces[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ for i in 1:NOut loop
+ connect(waterOutput[i], p1.outTransition[i]) annotation(
+ Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
+ end for;
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-32, 0}, {36, 0}}, thickness = 0.5));
+ annotation(
+ Icon(graphics = {Rectangle(origin = {0, 80}, fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 20}, {100, -20}}), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name"), Ellipse(origin = {-82, 79}, fillColor = {61, 133, 198}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Rectangle(origin = {-2, 79}, fillColor = {61, 133, 198}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-80, 15}, {80, -15}}), Ellipse(origin = {78, 79}, fillColor = {61, 133, 198}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Ellipse(origin = {78, 79}, fillColor = {42, 92, 136}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-8, 13}, {8, -13}}), Line(origin = {-2, 94}, points = {{-80, 0}, {80, 0}, {80, 0}}), Line(origin = {-2, 64}, points = {{80, 0}, {-80, 0}})}, coordinateSystem(extent = {{-120, 40}, {120, -40}})));
+
+
+end WaterPipe;
diff --git a/Distribution/package.mo b/Distribution/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..a2632a522fe7ccb3b2040c87c2aa1a5bdf2a1a0f
--- /dev/null
+++ b/Distribution/package.mo
@@ -0,0 +1,13 @@
+within PNRG;
+
+package Distribution
+
+
+
+
+
+
+
+ annotation(
+ Icon(graphics = {Bitmap(origin = {-1, -3}, extent = {{-139, -149}, {139, 149}}, imageSource = "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")}));
+end Distribution;
diff --git a/Distribution/package.order b/Distribution/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..86de37af6e73a9a66934a8dc6bdc46c55d2ade99
--- /dev/null
+++ b/Distribution/package.order
@@ -0,0 +1,7 @@
+BaseTransmissionLine
+Transformer
+DistributionPowerGrid
+ConditionalConnection
+WaterPipe
+HydrogenPipe
+OxygenPipe
diff --git a/EnergyConsumer/ElectricityConsumer.mo b/EnergyConsumer/ElectricityConsumer.mo
new file mode 100644
index 0000000000000000000000000000000000000000..7e5177ba82c631ba870558ef868dc4f6be69a6b4
--- /dev/null
+++ b/EnergyConsumer/ElectricityConsumer.mo
@@ -0,0 +1,27 @@
+within PNRG.EnergyConsumer;
+
+model ElectricityConsumer
+ Real powerConsumption(unit = "kW") "Consumption of electrical energy";
+ Real cumulativeEnergyConsumption(unit = "kWh") "Cumulative consumption of electrical energy";
+ PNlib.Components.TC t1(arcWeightIn = {powerConsumption}, arcWeightOut = {powerConsumption}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.ElectricalInput electricalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.FileInput fileInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p1(nIn = 1) annotation(
+ Placement(visible = true, transformation(origin = {50, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticTransitionWithoutActivator t11(nIn = 1) annotation(
+ Placement(visible = true, transformation(origin = {0, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerConsumption = t11.power;
+ cumulativeEnergyConsumption = p1.t;
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{4, 0}, {40, 0}}, thickness = 0.5));
+ connect(electricalInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-4, 0}}));
+ connect(fileInput, t11.inPlaces[1]) annotation(
+ Line(points = {{-110, 60}, {-4, 60}}));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {1, -56}, extent = {{-99, -44}, {99, 44}}, imageSource = 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"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")}));
+end ElectricityConsumer;
diff --git a/EnergyConsumer/HeatConsumer.mo b/EnergyConsumer/HeatConsumer.mo
new file mode 100644
index 0000000000000000000000000000000000000000..f3c39c4a207f83857f9770f5ec60d4a6c4ac0e8f
--- /dev/null
+++ b/EnergyConsumer/HeatConsumer.mo
@@ -0,0 +1,27 @@
+within PNRG.EnergyConsumer;
+
+model HeatConsumer
+ Real powerConsumption(unit = "kW") "Consumption of electrical energy";
+ Real cumulativeEnergyConsumption(unit = "kWh") "Cumulative consumption of electrical energy";
+ PNlib.Components.TC t1(arcWeightIn = {powerConsumption}, arcWeightOut = {powerConsumption}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.ElectricalInput electricalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.FileInput fileInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p1(nIn = 1) annotation(
+ Placement(visible = true, transformation(origin = {50, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticTransitionWithoutActivator t11(nIn = 1) annotation(
+ Placement(visible = true, transformation(origin = {0, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerConsumption = t11.power;
+ cumulativeEnergyConsumption = p1.t;
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{4, 0}, {40, 0}}, thickness = 0.5));
+ connect(electricalInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-4, 0}}));
+ connect(fileInput, t11.inPlaces[1]) annotation(
+ Line(points = {{-110, 60}, {-4, 60}}));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {1, -56}, extent = {{-99, -44}, {99, 44}}, imageSource = 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"), Ellipse(origin = {-80, 0}, extent = {{-20, 20}, {20, -20}})}));
+end HeatConsumer;
diff --git a/EnergyConsumer/package.mo b/EnergyConsumer/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..4157fd792d0aa96751c4f0d4fb8e1d4fe4020c94
--- /dev/null
+++ b/EnergyConsumer/package.mo
@@ -0,0 +1,9 @@
+within PNRG;
+
+package EnergyConsumer
+
+
+
+ annotation(
+ Icon(graphics = {Bitmap(origin = {0, 1}, extent = {{-100, -91}, {100, 91}}, imageSource = 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")}));
+end EnergyConsumer;
diff --git a/EnergyConsumer/package.order b/EnergyConsumer/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..0367810ea56524c33a867053ff66d4ab2eef10b5
--- /dev/null
+++ b/EnergyConsumer/package.order
@@ -0,0 +1,2 @@
+ElectricityConsumer
+HeatConsumer
diff --git a/Examples/EnergyPark.mo b/Examples/EnergyPark.mo
new file mode 100644
index 0000000000000000000000000000000000000000..17b555239144ea91221705923953492a099ebd9b
--- /dev/null
+++ b/Examples/EnergyPark.mo
@@ -0,0 +1,72 @@
+within PNRG.Examples;
+
+model EnergyPark
+ PNRG.PowerToX.Electrolyser electrolyser annotation(
+ Placement(visible = true, transformation(origin = {-18, -10}, extent = {{-16, -16}, {16, 16}}, rotation = 0)));
+ PNRG.PowerPlants.HydrogenCHPPlant hydrogenCHPPlant annotation(
+ Placement(visible = true, transformation(origin = {72, -10}, extent = {{-16, -16}, {16, 16}}, rotation = 0)));
+ PNRG.Storage.WaterTank waterTank annotation(
+ Placement(visible = true, transformation(origin = {30, -44}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.Clock clock(periodDuration = 10) annotation(
+ Placement(visible = true, transformation(origin = {-60, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Storage.H2Tank h2Tank annotation(
+ Placement(visible = true, transformation(origin = {30, -4}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Storage.O2Tank o2Tank annotation(
+ Placement(visible = true, transformation(origin = {30, -24}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput(NOut = 2, fileName = "P:/Programs/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-96, -10}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput1(NOut = 2, fileName = "P:/Programs/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-96, 20}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.WindPowerPlant windPowerPlant(number = 5) annotation(
+ Placement(visible = true, transformation(origin = {-60, 20}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.PVPowerPlant pVPowerPlant(areaPV = 10) annotation(
+ Placement(visible = true, transformation(origin = {-60, -10}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.STEPowerPlant sTEPowerPlant annotation(
+ Placement(visible = true, transformation(origin = {-60, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.Clock clock1(periodDuration = 5) annotation(
+ Placement(visible = true, transformation(origin = {30, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ EnergyConsumer.HeatConsumer heatConsumer annotation(
+ Placement(visible = true, transformation(origin = {130, -10}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ EnergyConsumer.ElectricityConsumer electricityConsumer annotation(
+ Placement(visible = true, transformation(origin = {130, 10}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(hydrogenCHPPlant.WaterOut, waterTank.waterInput) annotation(
+ Line(points = {{90, -20}, {90, -20.5}, {110, -20.5}, {110, -44}, {41, -44}}, color = {61, 133, 198}));
+ connect(waterTank.waterOutput, electrolyser.WaterIn) annotation(
+ Line(points = {{19, -44}, {-40, -44}, {-40, -19}, {-36, -19}}, color = {61, 133, 198}));
+ connect(h2Tank.hydrogenOutput, hydrogenCHPPlant.H2In) annotation(
+ Line(points = {{41, -4}, {48, -4}, {48, -10}, {54, -10}}, color = {106, 168, 79}));
+ connect(o2Tank.oxygenOutput, hydrogenCHPPlant.O2In) annotation(
+ Line(points = {{41, -24}, {48, -24}, {48, -20}, {54, -20}}, color = {11, 83, 148}));
+ connect(electrolyser.O2Out, o2Tank.oxygenInput) annotation(
+ Line(points = {{0, -16}, {12, -16}, {12, -24}, {19, -24}}, color = {11, 83, 148}));
+ connect(electrolyser.H2Out, h2Tank.hydrogenInput) annotation(
+ Line(points = {{0, -4}, {19, -4}}, color = {106, 168, 79}));
+ connect(fileToTransitionOutput.fileOutput[1], pVPowerPlant.fileInput) annotation(
+ Line(points = {{-84, -10}, {-70, -10}}));
+ connect(pVPowerPlant.electricalOutput[1], electrolyser.EnergyIn) annotation(
+ Line(points = {{-48, -10}, {-36, -10}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput1.fileOutput[1], windPowerPlant.fileInput) annotation(
+ Line(points = {{-85, 20}, {-71, 20}}, color = {150, 150, 150}));
+ connect(fileToTransitionOutput.fileOutput[2], sTEPowerPlant.fileInput) annotation(
+ Line(points = {{-84, -10}, {-80, -10}, {-80, -60}, {-70, -60}}, color = {150, 150, 150}));
+ connect(clock.logicalOutput, electrolyser.activation) annotation(
+ Line(points = {{-48, 60}, {-40, 60}, {-40, 0}, {-36, 0}}, color = {53, 28, 117}));
+ connect(sTEPowerPlant.heatOutput[1], heatConsumer.heatInput[2]) annotation(
+ Line(points = {{-48, -60}, {114, -60}, {114, -10}, {120, -10}}, color = {255, 80, 50}, thickness = 0.5));
+ connect(hydrogenCHPPlant.heatOutput, heatConsumer.heatInput[1]) annotation(
+ Line(points = {{90, -10}, {120, -10}}, color = {255, 80, 50}));
+ connect(hydrogenCHPPlant.electricalOutput, electricityConsumer.electricalInput[1]) annotation(
+ Line(points = {{90, 0}, {106, 0}, {106, 10}, {120, 10}}, color = {255, 200, 0}));
+ connect(pVPowerPlant.electricalOutput[2], electricityConsumer.electricalInput[2]) annotation(
+ Line(points = {{-48, -10}, {-44, -10}, {-44, 10}, {120, 10}}, color = {255, 200, 0}, thickness = 0.5));
+ connect(clock1.logicalOutput, hydrogenCHPPlant.activation) annotation(
+ Line(points = {{42, 60}, {50, 60}, {50, 0}, {54, 0}}, color = {53, 28, 117}));
+ connect(windPowerPlant.electricalOutput, electricityConsumer.electricalInput) annotation(
+ Line(points = {{-48, 20}, {106, 20}, {106, 10}, {120, 10}}, color = {255, 200, 0}));
+protected
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Diagram(coordinateSystem(extent = {{-120, 80}, {140, -80}})),
+ version = "");
+end EnergyPark;
diff --git a/Examples/MicrogridSimple.bak-mo b/Examples/MicrogridSimple.bak-mo
new file mode 100644
index 0000000000000000000000000000000000000000..b9f146523b59539998a538319cf64b94574b97e5
--- /dev/null
+++ b/Examples/MicrogridSimple.bak-mo
@@ -0,0 +1,185 @@
+within PNRG.Examples;
+
+model MicrogridSimple
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput111(NOut = 1, fileName = "P:/Programs/PNRG/data3.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-360, -54}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression3(NOut = 1, expression = testController.N < testController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {-474, -146}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid(NIn = 3, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {-441, -7}, extent = {{-29, 9.66666}, {29, 29}}, rotation = 0)));
+ PNRG.PowerPlants.WindPowerPlant windPowerPlant(efficiencyTurbine = 0.4, number = testController.N, rotorLength = 3) annotation(
+ Placement(visible = true, transformation(origin = {-592, 24}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid1(NIn = 4, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {-427, -95}, extent = {{-29, 9.66666}, {29, 29}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer2(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-552, -90}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.PowerPlants.WindPowerPlant windPowerPlant1(efficiencyTurbine = 0.4, number = integerController.N, rotorLength = 1.5) annotation(
+ Placement(visible = true, transformation(origin = {-578, -66}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression4(NOut = 1, expression = testController.N == testController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {-506, -158}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput4(NOut = 1, fileName = "P:/Programs/PNRG/data2.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-626, 24}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd2 annotation(
+ Placement(visible = true, transformation(origin = {-582, -174}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput(NOut = 1, fileName = "P:/Programs/PNRG/data2.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-626, -66}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.EnergyConsumer.ElectricityConsumer electricityConsumer annotation(
+ Placement(visible = true, transformation(origin = {-360, -76}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Storage.Battery battery1(power = 10) annotation(
+ Placement(visible = true, transformation(origin = {-430, -108}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression10(NOut = 1, expression = electricityConsumer.powerConsumption + battery1.power - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption + battery.power - windPowerPlant.currentPower) annotation(
+ Placement(visible = true, transformation(origin = {-416, -48}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Storage.Battery battery(power = 10) annotation(
+ Placement(visible = true, transformation(origin = {-440, 40}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression6(NOut = 1, expression = not (electricityConsumer.powerConsumption + battery1.power - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption + battery.power - windPowerPlant.currentPower)) annotation(
+ Placement(visible = true, transformation(origin = {-416, -14}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd annotation(
+ Placement(visible = true, transformation(origin = {-550, 100}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.IntegerController integerController(NChange = 1, NMax = 10, NStart = 10, delay = 0.1) annotation(
+ Placement(visible = true, transformation(origin = {-578, -202}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression5(NOut = 2, expression = windPowerPlant1.currentPower + conditionalConnection1.currentPower > electricityConsumer.powerConsumption + battery1.power) annotation(
+ Placement(visible = true, transformation(origin = {-522, -124}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.PVPowerPlant pVPowerPlant(areaPV = 200, efficiency_PV = 0.3) annotation(
+ Placement(visible = true, transformation(origin = {-592, -16}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression1(NOut = 2, expression = windPowerPlant.currentPower + conditionalConnection.currentPower <= electricityConsumer1.powerConsumption + battery.power) annotation(
+ Placement(visible = true, transformation(origin = {-510, 104}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid2(NIn = 1, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {-532, -34}, extent = {{-26, 8.66667}, {26, 26}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd3 annotation(
+ Placement(visible = true, transformation(origin = {-492, -192}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression8(NOut = 2, expression = windPowerPlant1.currentPower + pVPowerPlant1.currentPower + conditionalConnection1.currentPower <= electricityConsumer.powerConsumption + battery1.power) annotation(
+ Placement(visible = true, transformation(origin = {-542, -170}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression(NOut = 2, expression = windPowerPlant.currentPower + conditionalConnection.currentPower > electricityConsumer1.powerConsumption + battery.power) annotation(
+ Placement(visible = true, transformation(origin = {-490, 150}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-566, 24}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Distribution.ConditionalConnection conditionalConnection(power = distributionPowerGrid2.powerInput) annotation(
+ Placement(visible = true, transformation(origin = {-488, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer4(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-552, -66}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.PowerPlants.PVPowerPlant pVPowerPlant1(areaPV = 30) annotation(
+ Placement(visible = true, transformation(origin = {-578, -90}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalOr logicalOr1 annotation(
+ Placement(visible = true, transformation(origin = {-450, -182}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput2(NOut = 1, fileName = "P:/Programs/PNRG_test/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-626, -16}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression2(NOut = 1, expression = testController.N < testController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {-442, 128}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression111(NOut = 1, expression = testController.N == testController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {-474, 116}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd1 annotation(
+ Placement(visible = true, transformation(origin = {-460, 82}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression11(NOut = 1, expression = windPowerPlant.currentPower + conditionalConnection.currentPower - windPowerPlant.singlePower > electricityConsumer1.powerConsumption + battery.power) annotation(
+ Placement(visible = true, transformation(origin = {-580, 130}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalOr logicalOr annotation(
+ Placement(visible = true, transformation(origin = {-418, 92}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.EnergyConsumer.ElectricityConsumer electricityConsumer1 annotation(
+ Placement(visible = true, transformation(origin = {-360, 12}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression7(NOut = 1, expression = windPowerPlant1.currentPower + conditionalConnection1.currentPower - windPowerPlant1.singlePower > electricityConsumer.powerConsumption + battery1.power) annotation(
+ Placement(visible = true, transformation(origin = {-612, -144}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer1(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-568, -16}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Logics.IntegerController testController(NChange = 1, NMax = 10, NStart = 10, delay = 0.1) annotation(
+ Placement(visible = true, transformation(origin = {-546, 72}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput11(NOut = 1, fileName = "P:/Programs/PNRG/data4.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-360, 34}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput1(NOut = 1, fileName = "P:/Programs/PNRG_test/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-626, -90}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.ConditionalConnection conditionalConnection1(power = distributionPowerGrid2.powerInput) annotation(
+ Placement(visible = true, transformation(origin = {-488, -32}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(logicalExpression.logicalOutput[2], logicalAnd.logicalInput1) annotation(
+ Line(points = {{-479, 150}, {-471, 150}, {-471, 130}, {-546, 130}, {-546, 111}}, color = {53, 28, 117}));
+ connect(distributionPowerGrid1.electricalOutput[2], battery1.electricalInput) annotation(
+ Line(points = {{-400.417, -75.6667}, {-390.584, -75.6667}, {-390.584, -108.333}, {-418.583, -108.333}}, color = {255, 200, 0}));
+ connect(logicalExpression1.logicalOutput[1], logicalAnd1.logicalInput2) annotation(
+ Line(points = {{-499, 104}, {-464, 104}, {-464, 94}}, color = {53, 28, 117}));
+ connect(windPowerPlant1.electricalOutput, transformer4.electricalInput) annotation(
+ Line(points = {{-567, -66}, {-559, -66}}, color = {255, 200, 0}));
+ connect(transformer2.electricalOutput, distributionPowerGrid1.electricalInput[2]) annotation(
+ Line(points = {{-545.4, -90}, {-477.4, -90}, {-477.4, -76}, {-454, -76}}, color = {255, 200, 0}));
+ connect(logicalExpression7.logicalOutput[1], logicalAnd2.logicalInput2) annotation(
+ Line(points = {{-601, -144}, {-587, -144}, {-587, -163}}, color = {53, 28, 117}));
+ connect(pVPowerPlant1.electricalOutput, transformer2.electricalInput) annotation(
+ Line(points = {{-567, -90}, {-559, -90}}, color = {255, 200, 0}));
+ connect(battery.electricalOutput, distributionPowerGrid.electricalInput[3]) annotation(
+ Line(points = {{-451, 40}, {-471, 40}, {-471, 12}, {-468, 12}}, color = {255, 200, 0}));
+ connect(logicalExpression2.logicalOutput[1], logicalOr.logicalInput1) annotation(
+ Line(points = {{-431, 128}, {-421, 128}, {-421, 104}}, color = {53, 28, 117}));
+ connect(logicalAnd.logicalOutput, testController.logicalInput1) annotation(
+ Line(points = {{-550, 89}, {-550.5, 89}, {-550.5, 83}, {-551, 83}}, color = {53, 28, 117}));
+ connect(logicalExpression6.logicalOutput[1], conditionalConnection.logicalInput) annotation(
+ Line(points = {{-427, -14}, {-489, -14}, {-489, -10}}, color = {53, 28, 117}));
+ connect(logicalExpression10.logicalOutput[1], conditionalConnection1.logicalInput) annotation(
+ Line(points = {{-427, -48}, {-488, -48}, {-488, -43}}, color = {53, 28, 117}));
+ connect(fileToTransitionOutput11.fileOutput[1], electricityConsumer1.fileInput) annotation(
+ Line(points = {{-371, 34}, {-377, 34}, {-377, 18}, {-371, 18}}, color = {150, 150, 150}));
+ connect(logicalOr1.logicalOutput, battery1.logicalInput) annotation(
+ Line(points = {{-450, -193}, {-450, -199}, {-404, -199}, {-404, -103}, {-418, -103}}, color = {53, 28, 117}));
+ connect(transformer4.electricalOutput, distributionPowerGrid1.electricalInput[1]) annotation(
+ Line(points = {{-545.4, -66}, {-477.4, -66}, {-477.4, -76}, {-454, -76}}, color = {255, 200, 0}));
+ connect(transformer1.electricalOutput, distributionPowerGrid2.electricalInput[1]) annotation(
+ Line(points = {{-561.4, -16}, {-555.4, -16}}, color = {255, 200, 0}));
+ connect(logicalExpression3.logicalOutput[1], logicalOr1.logicalInput1) annotation(
+ Line(points = {{-463, -146}, {-453, -146}, {-453, -170}}, color = {53, 28, 117}));
+ connect(conditionalConnection.electricalOutput, distributionPowerGrid.electricalInput[2]) annotation(
+ Line(points = {{-477, 0}, {-473, 0}, {-473, 12}, {-468, 12}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid.electricalOutput[2], battery.electricalInput) annotation(
+ Line(points = {{-414.417, 12.3333}, {-408.584, 12.3333}, {-408.584, 40.0003}, {-429, 40.0003}}, color = {255, 200, 0}));
+ connect(logicalAnd1.logicalOutput, battery.logicalInput1) annotation(
+ Line(points = {{-460, 71}, {-460, 45}, {-450, 45}}, color = {53, 28, 117}));
+ connect(logicalAnd2.logicalOutput, integerController.logicalInput1) annotation(
+ Line(points = {{-582, -185}, {-582.5, -185}, {-582.5, -191}, {-583, -191}}, color = {53, 28, 117}));
+ connect(fileToTransitionOutput4.fileOutput[1], windPowerPlant.fileInput) annotation(
+ Line(points = {{-615, 24}, {-602, 24}}, color = {150, 150, 150}));
+ connect(fileToTransitionOutput111.fileOutput[1], electricityConsumer.fileInput) annotation(
+ Line(points = {{-371, -54}, {-377, -54}, {-377, -70}, {-371, -70}}, color = {150, 150, 150}));
+ connect(conditionalConnection1.electricalOutput, distributionPowerGrid1.electricalInput[3]) annotation(
+ Line(points = {{-477, -32}, {-471, -32}, {-471, -76}, {-454, -76}}, color = {255, 200, 0}));
+ connect(logicalExpression11.logicalOutput[1], logicalAnd.logicalInput2) annotation(
+ Line(points = {{-569, 130}, {-555, 130}, {-555, 111}}, color = {53, 28, 117}));
+ connect(distributionPowerGrid.electricalOutput[1], electricityConsumer1.electricalInput) annotation(
+ Line(points = {{-414.417, 12.3333}, {-411.668, 12.3333}, {-411.668, 12.6673}, {-371.584, 12.6673}}, color = {255, 200, 0}));
+ connect(logicalExpression4.logicalOutput[1], logicalAnd3.logicalInput1) annotation(
+ Line(points = {{-495, -158}, {-495, -159}, {-487, -159}, {-487, -180}, {-486, -180}}, color = {53, 28, 117}));
+ connect(logicalExpression8.logicalOutput[1], logicalAnd3.logicalInput2) annotation(
+ Line(points = {{-531, -170}, {-496, -170}, {-496, -180}}, color = {53, 28, 117}));
+ connect(distributionPowerGrid1.electricalOutput[1], electricityConsumer.electricalInput) annotation(
+ Line(points = {{-400.417, -75.6667}, {-371.417, -75.6667}}, color = {255, 200, 0}));
+ connect(logicalExpression111.logicalOutput[1], logicalAnd1.logicalInput1) annotation(
+ Line(points = {{-463, 116}, {-463, 115}, {-455, 115}, {-455, 94}, {-454, 94}}, color = {53, 28, 117}));
+ connect(battery1.electricalOutput, distributionPowerGrid1.electricalInput[4]) annotation(
+ Line(points = {{-441, -108}, {-461, -108}, {-461, -76}, {-454, -76}}, color = {255, 200, 0}));
+ connect(transformer.electricalOutput, distributionPowerGrid.electricalInput[1]) annotation(
+ Line(points = {{-559.4, 24}, {-473.4, 24}, {-473.4, 12}, {-468, 12}}, color = {255, 200, 0}));
+ connect(logicalExpression5.logicalOutput[1], logicalOr1.logicalInput) annotation(
+ Line(points = {{-511, -124}, {-447, -124}, {-447, -170}}, color = {53, 28, 117}));
+ connect(logicalExpression1.logicalOutput[2], testController.logicalInput) annotation(
+ Line(points = {{-499, 104}, {-489, 104}, {-489, 88}, {-541, 88}, {-541, 83}}, color = {53, 28, 117}));
+ connect(logicalExpression.logicalOutput[1], logicalOr.logicalInput) annotation(
+ Line(points = {{-479, 150}, {-415, 150}, {-415, 104}}, color = {53, 28, 117}));
+ connect(logicalExpression8.logicalOutput[2], integerController.logicalInput) annotation(
+ Line(points = {{-531, -170}, {-521, -170}, {-521, -186}, {-573, -186}, {-573, -191}}, color = {53, 28, 117}));
+ connect(logicalAnd3.logicalOutput, battery1.logicalInput1) annotation(
+ Line(points = {{-492, -203}, {-454, -203}, {-454, -103}, {-440, -103}}, color = {53, 28, 117}));
+ connect(logicalOr.logicalOutput, battery.logicalInput) annotation(
+ Line(points = {{-418, 81}, {-418, 45}, {-428, 45}}, color = {53, 28, 117}));
+ connect(distributionPowerGrid2.electricalOutput[1], conditionalConnection.electricalInput) annotation(
+ Line(points = {{-508.167, -16.6667}, {-502.167, -16.6667}, {-502.167, 0.333335}, {-498.167, 0.333335}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput2.fileOutput[1], pVPowerPlant.fileInput) annotation(
+ Line(points = {{-615, -16}, {-603, -16}}, color = {150, 150, 150}));
+ connect(logicalExpression5.logicalOutput[2], logicalAnd2.logicalInput1) annotation(
+ Line(points = {{-511, -124}, {-503, -124}, {-503, -144}, {-578, -144}, {-578, -163}}, color = {53, 28, 117}));
+ connect(fileToTransitionOutput.fileOutput[1], windPowerPlant1.fileInput) annotation(
+ Line(points = {{-615, -66}, {-589, -66}}, color = {150, 150, 150}));
+ connect(windPowerPlant.electricalOutput, transformer.electricalInput) annotation(
+ Line(points = {{-581, 24}, {-573, 24}}, color = {255, 200, 0}));
+ connect(pVPowerPlant.electricalOutput, transformer1.electricalInput) annotation(
+ Line(points = {{-581, -16}, {-575, -16}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput1.fileOutput[1], pVPowerPlant1.fileInput) annotation(
+ Line(points = {{-615, -90}, {-589, -90}}, color = {150, 150, 150}));
+ connect(distributionPowerGrid2.electricalOutput[2], conditionalConnection1.electricalInput) annotation(
+ Line(points = {{-508.167, -16.6667}, {-508.167, -16.3327}, {-502.167, -16.3327}, {-502.167, -31.9997}, {-499, -31.9997}}, color = {255, 200, 0}));
+ annotation(
+ Diagram(coordinateSystem(extent = {{-660, 180}, {-340, -260}})));
+end MicrogridSimple;
diff --git a/Examples/Microgrids.mo b/Examples/Microgrids.mo
new file mode 100644
index 0000000000000000000000000000000000000000..1712c99af491b511149b8c1fb9cd69d888e2f093
--- /dev/null
+++ b/Examples/Microgrids.mo
@@ -0,0 +1,205 @@
+within PNRG.Examples;
+
+model Microgrids
+ PNRG.Logics.LogicalAnd logicalAnd annotation(
+ Placement(visible = true, transformation(origin = {-578, 14}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput1(NOut = 1, fileName = "P:/Programs/PNRG_test/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-652, -204}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression11(NOut = 1, expression = windPowerPlant.currentPower + conditionalConnection.currentPower - windPowerPlant.singlePower > electricityConsumer1.powerConsumption + battery.power) annotation(
+ Placement(visible = true, transformation(origin = {-606, 38}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput(NOut = 1, fileName = "P:/Programs/PNRG/data2.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-652, -180}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression(NOut = 2, expression = windPowerPlant.currentPower + conditionalConnection.currentPower > electricityConsumer1.powerConsumption + battery.power) annotation(
+ Placement(visible = true, transformation(origin = {-516, 58}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression10(NOut = 1, expression = electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower) annotation(
+ Placement(visible = true, transformation(origin = {-442, -162}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalOr logicalOr annotation(
+ Placement(visible = true, transformation(origin = {-444, 0}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression2(NOut = 1, expression = testController.N < testController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {-468, 36}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.ConditionalConnection conditionalConnection1(power = distributionPowerGrid2.powerInput) annotation(
+ Placement(visible = true, transformation(origin = {-514, -146}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.PVPowerPlant pVPowerPlant(areaPV = 200, efficiency_PV = 0.3) annotation(
+ Placement(visible = true, transformation(origin = {-618, -130}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression3(NOut = 1, expression = integerController.N < integerController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {-500, -260}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.ConditionalConnection conditionalConnection(power = distributionPowerGrid2.powerInput) annotation(
+ Placement(visible = true, transformation(origin = {-514, -114}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd3 annotation(
+ Placement(visible = true, transformation(origin = {-518, -314}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput111(NOut = 1, fileName = "P:/Programs/PNRG/data3.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-386, -168}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.PVPowerPlant pVPowerPlant1(areaPV = 30) annotation(
+ Placement(visible = true, transformation(origin = {-604, -204}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.EnergyConsumer.ElectricityConsumer electricityConsumer1 annotation(
+ Placement(visible = true, transformation(origin = {-386, -102}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Storage.Battery battery1(power = 10) annotation(
+ Placement(visible = true, transformation(origin = {-456, -222}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid(NIn = 3, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {-467, -121}, extent = {{-29, 9.66666}, {29, 29}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd2 annotation(
+ Placement(visible = true, transformation(origin = {-608, -288}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid1(NIn = 4, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {-453, -209}, extent = {{-29, 9.66666}, {29, 29}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd1 annotation(
+ Placement(visible = true, transformation(origin = {-486, -10}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression12(NOut = 1, expression = not (not (electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower) and distributionPowerGrid.powerDifference < 10)) annotation(
+ Placement(visible = true, transformation(origin = {-622, -14}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd4 annotation(
+ Placement(visible = true, transformation(origin = {-584, -30}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid2(NIn = 1, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {-558, -148}, extent = {{-26, 8.66667}, {26, 26}}, rotation = 0)));
+ PNRG.PowerPlants.WindPowerPlant windPowerPlant(efficiencyTurbine = 0.4, number = testController.N, rotorLength = 3) annotation(
+ Placement(visible = true, transformation(origin = {-618, -90}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalOr logicalOr1 annotation(
+ Placement(visible = true, transformation(origin = {-464, -296}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression4(NOut = 1, expression = integerController.N == integerController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {-532, -272}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput2(NOut = 1, fileName = "P:/Programs/PNRG_test/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-652, -130}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Storage.Battery battery(power = 10) annotation(
+ Placement(visible = true, transformation(origin = {-466, -74}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.IntegerController integerController(NChange = 1, NMax = 8, NStart = 8, delay = 0.1) annotation(
+ Placement(visible = true, transformation(origin = {-604, -316}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalOr logicalOr2 annotation(
+ Placement(visible = true, transformation(origin = {-560, -36}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression1(NOut = 1, expression = windPowerPlant.currentPower + conditionalConnection.currentPower <= electricityConsumer1.powerConsumption) annotation(
+ Placement(visible = true, transformation(origin = {-524, 12}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput11(NOut = 1, fileName = "P:/Programs/PNRG/data4.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-386, -80}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer1(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-594, -130}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer2(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-578, -204}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression14(NOut = 1, expression = windPowerPlant.currentPower + conditionalConnection.currentPower <= electricityConsumer1.powerConsumption + battery.power) annotation(
+ Placement(visible = true, transformation(origin = {-548, 12}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.IntegerController testController(NChange = 1, NMax = 10, NStart = 10, delay = 0.1) annotation(
+ Placement(visible = true, transformation(origin = {-566, -70}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression111(NOut = 1, expression = testController.N == testController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {-500, 24}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression7(NOut = 1, expression = windPowerPlant1.currentPower + conditionalConnection1.currentPower - windPowerPlant1.singlePower > electricityConsumer.powerConsumption + battery1.power) annotation(
+ Placement(visible = true, transformation(origin = {-638, -258}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.EnergyConsumer.ElectricityConsumer electricityConsumer annotation(
+ Placement(visible = true, transformation(origin = {-386, -190}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression6(NOut = 1, expression = not (electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower)) annotation(
+ Placement(visible = true, transformation(origin = {-442, -128}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-592, -90}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.PowerPlants.WindPowerPlant windPowerPlant1(efficiencyTurbine = 0.4, number = integerController.N, rotorLength = 2) annotation(
+ Placement(visible = true, transformation(origin = {-604, -180}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer4(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-578, -180}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression5(NOut = 2, expression = windPowerPlant1.currentPower + conditionalConnection1.currentPower + pVPowerPlant1.currentPower > electricityConsumer.powerConsumption + battery1.power) annotation(
+ Placement(visible = true, transformation(origin = {-548, -238}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression13(NOut = 1, expression = windPowerPlant1.currentPower + pVPowerPlant1.currentPower + conditionalConnection1.currentPower <= electricityConsumer.powerConsumption + battery1.power) annotation(
+ Placement(visible = true, transformation(origin = {-572, -294}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput4(NOut = 1, fileName = "P:/Programs/PNRG/data2.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-652, -90}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression8(NOut = 1, expression = windPowerPlant1.currentPower + pVPowerPlant1.currentPower + conditionalConnection1.currentPower <= electricityConsumer.powerConsumption) annotation(
+ Placement(visible = true, transformation(origin = {-550, -294}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression9(NOut = 1, expression = not (electricityConsumer.powerConsumption + battery1.power - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption + battery.power - windPowerPlant.currentPower) and distributionPowerGrid1.powerDifference < 0) annotation(
+ Placement(visible = true, transformation(origin = {-510, -14}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+equation
+ connect(logicalExpression5.logicalOutput[2], logicalAnd2.logicalInput1) annotation(
+ Line(points = {{-537, -238}, {-529, -238}, {-529, -258}, {-604, -258}, {-604, -277}}, color = {53, 28, 117}));
+ connect(logicalExpression10.logicalOutput[1], conditionalConnection1.logicalInput) annotation(
+ Line(points = {{-453, -162}, {-514, -162}, {-514, -157}}, color = {53, 28, 117}));
+ connect(logicalOr1.logicalOutput, battery1.logicalInput) annotation(
+ Line(points = {{-464, -307}, {-464, -312}, {-430, -312}, {-430, -216}, {-444, -216}}, color = {53, 28, 117}));
+ connect(transformer4.electricalOutput, distributionPowerGrid1.electricalInput[1]) annotation(
+ Line(points = {{-571.4, -180}, {-503.4, -180}, {-503.4, -190}, {-480, -190}}, color = {255, 200, 0}));
+ connect(transformer.electricalOutput, distributionPowerGrid.electricalInput[1]) annotation(
+ Line(points = {{-585.4, -90}, {-499.4, -90}, {-499.4, -102}, {-494, -102}}, color = {255, 200, 0}));
+ connect(pVPowerPlant.electricalOutput, transformer1.electricalInput) annotation(
+ Line(points = {{-607, -130}, {-601, -130}}, color = {255, 200, 0}));
+ connect(logicalExpression111.logicalOutput[1], logicalAnd1.logicalInput1) annotation(
+ Line(points = {{-489, 24}, {-489, 23}, {-481, 23}, {-481, 2}, {-480, 2}}, color = {53, 28, 117}));
+ connect(fileToTransitionOutput111.fileOutput[1], electricityConsumer.fileInput) annotation(
+ Line(points = {{-397, -168}, {-403, -168}, {-403, -184}, {-397, -184}}, color = {150, 150, 150}));
+ connect(logicalExpression1.logicalOutput[1], logicalAnd1.logicalInput2) annotation(
+ Line(points = {{-513, 12}, {-490, 12}, {-490, 2}}, color = {53, 28, 117}));
+ connect(logicalExpression4.logicalOutput[1], logicalAnd3.logicalInput1) annotation(
+ Line(points = {{-521, -272}, {-521, -273}, {-513, -273}, {-513, -303}}, color = {53, 28, 117}));
+ connect(logicalOr2.logicalOutput, testController.logicalInput) annotation(
+ Line(points = {{-560, -47}, {-560, -58}}, color = {53, 28, 117}));
+ connect(logicalOr.logicalOutput, battery.logicalInput) annotation(
+ Line(points = {{-444, -11}, {-444, -68}, {-454, -68}}, color = {53, 28, 117}));
+ connect(pVPowerPlant1.electricalOutput, transformer2.electricalInput) annotation(
+ Line(points = {{-593, -204}, {-585, -204}}, color = {255, 200, 0}));
+ connect(windPowerPlant1.electricalOutput, transformer4.electricalInput) annotation(
+ Line(points = {{-593, -180}, {-585, -180}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid.electricalOutput[1], electricityConsumer1.electricalInput) annotation(
+ Line(points = {{-440.417, -101.667}, {-437.668, -101.667}, {-437.668, -101.333}, {-397.584, -101.333}}, color = {255, 200, 0}));
+ connect(logicalExpression9.logicalOutput[1], logicalOr2.logicalInput) annotation(
+ Line(points = {{-521, -14}, {-557, -14}, {-557, -25}}, color = {53, 28, 117}));
+ connect(logicalExpression8.logicalOutput[1], logicalAnd3.logicalInput2) annotation(
+ Line(points = {{-539, -294}, {-522.5, -294}, {-522.5, -303}, {-523, -303}}, color = {53, 28, 117}));
+ connect(logicalAnd2.logicalOutput, integerController.logicalInput1) annotation(
+ Line(points = {{-608, -299}, {-608.5, -299}, {-608.5, -305}, {-609, -305}}, color = {53, 28, 117}));
+ connect(fileToTransitionOutput1.fileOutput[1], pVPowerPlant1.fileInput) annotation(
+ Line(points = {{-641, -204}, {-615, -204}}, color = {150, 150, 150}));
+ connect(fileToTransitionOutput2.fileOutput[1], pVPowerPlant.fileInput) annotation(
+ Line(points = {{-641, -130}, {-629, -130}}, color = {150, 150, 150}));
+ connect(logicalExpression11.logicalOutput[1], logicalAnd.logicalInput2) annotation(
+ Line(points = {{-595, 38}, {-583, 38}, {-583, 25}}, color = {53, 28, 117}));
+ connect(battery1.electricalOutput, distributionPowerGrid1.electricalInput[4]) annotation(
+ Line(points = {{-467, -222}, {-487, -222}, {-487, -190}, {-480, -190}}, color = {255, 200, 0}));
+ connect(conditionalConnection1.electricalOutput, distributionPowerGrid1.electricalInput[3]) annotation(
+ Line(points = {{-503, -146}, {-497, -146}, {-497, -190}, {-480, -190}}, color = {255, 200, 0}));
+ connect(logicalExpression14.logicalOutput[1], logicalOr2.logicalInput1) annotation(
+ Line(points = {{-559, 12}, {-563, 12}, {-563, -24}}, color = {53, 28, 117}));
+ connect(battery.electricalOutput, distributionPowerGrid.electricalInput[3]) annotation(
+ Line(points = {{-477, -74}, {-497, -74}, {-497, -102}, {-494, -102}}, color = {255, 200, 0}));
+ connect(logicalExpression12.logicalOutput[1], logicalAnd4.logicalInput2) annotation(
+ Line(points = {{-611, -14}, {-589, -14}, {-589, -18}}, color = {53, 28, 117}));
+ connect(distributionPowerGrid2.electricalOutput[2], conditionalConnection1.electricalInput) annotation(
+ Line(points = {{-534.167, -130.667}, {-534.167, -130.333}, {-528.167, -130.333}, {-528.167, -146}, {-525, -146}}, color = {255, 200, 0}));
+ connect(logicalAnd.logicalOutput, logicalAnd4.logicalInput1) annotation(
+ Line(points = {{-578, 3}, {-578, -18}}, color = {53, 28, 117}));
+ connect(logicalAnd3.logicalOutput, battery1.logicalInput1) annotation(
+ Line(points = {{-518, -325}, {-519, -325}, {-519, -334}, {-480, -334}, {-480, -216}, {-466, -216}}, color = {53, 28, 117}));
+ connect(distributionPowerGrid1.electricalOutput[2], battery1.electricalInput) annotation(
+ Line(points = {{-426.417, -189.667}, {-416.584, -189.667}, {-416.584, -222.333}, {-444.583, -222.333}}, color = {255, 200, 0}));
+ connect(logicalExpression.logicalOutput[2], logicalAnd.logicalInput1) annotation(
+ Line(points = {{-505, 58}, {-497, 58}, {-497, 38}, {-573, 38}, {-573, 25}}, color = {53, 28, 117}));
+ connect(logicalExpression.logicalOutput[1], logicalOr.logicalInput) annotation(
+ Line(points = {{-505, 58}, {-441, 58}, {-441, 12}}, color = {53, 28, 117}));
+ connect(transformer2.electricalOutput, distributionPowerGrid1.electricalInput[2]) annotation(
+ Line(points = {{-571.4, -204}, {-503.4, -204}, {-503.4, -190}, {-480, -190}}, color = {255, 200, 0}));
+ connect(windPowerPlant.electricalOutput, transformer.electricalInput) annotation(
+ Line(points = {{-607, -90}, {-599, -90}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput11.fileOutput[1], electricityConsumer1.fileInput) annotation(
+ Line(points = {{-397, -80}, {-403, -80}, {-403, -96}, {-397, -96}}, color = {150, 150, 150}));
+ connect(logicalExpression13.logicalOutput[1], integerController.logicalInput) annotation(
+ Line(points = {{-583, -294}, {-599, -294}, {-599, -304}}, color = {53, 28, 117}));
+ connect(logicalExpression5.logicalOutput[1], logicalOr1.logicalInput) annotation(
+ Line(points = {{-537, -238}, {-461, -238}, {-461, -285}}, color = {53, 28, 117}));
+ connect(logicalExpression2.logicalOutput[1], logicalOr.logicalInput1) annotation(
+ Line(points = {{-457, 36}, {-447, 36}, {-447, 12}}, color = {53, 28, 117}));
+ connect(fileToTransitionOutput.fileOutput[1], windPowerPlant1.fileInput) annotation(
+ Line(points = {{-641, -180}, {-615, -180}}, color = {150, 150, 150}));
+ connect(logicalExpression3.logicalOutput[1], logicalOr1.logicalInput1) annotation(
+ Line(points = {{-489, -260}, {-467, -260}, {-467, -285}}, color = {53, 28, 117}));
+ connect(logicalAnd1.logicalOutput, battery.logicalInput1) annotation(
+ Line(points = {{-486, -21}, {-486, -68}, {-476, -68}}, color = {53, 28, 117}));
+ connect(logicalExpression6.logicalOutput[1], conditionalConnection.logicalInput) annotation(
+ Line(points = {{-453, -128}, {-515, -128}, {-515, -124}}, color = {53, 28, 117}));
+ connect(conditionalConnection.electricalOutput, distributionPowerGrid.electricalInput[2]) annotation(
+ Line(points = {{-503, -114}, {-499, -114}, {-499, -102}, {-494, -102}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput4.fileOutput[1], windPowerPlant.fileInput) annotation(
+ Line(points = {{-641, -90}, {-628, -90}}, color = {150, 150, 150}));
+ connect(distributionPowerGrid1.electricalOutput[1], electricityConsumer.electricalInput) annotation(
+ Line(points = {{-426.417, -189.667}, {-397.417, -189.667}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid2.electricalOutput[1], conditionalConnection.electricalInput) annotation(
+ Line(points = {{-534.167, -130.667}, {-528.167, -130.667}, {-528.167, -113.667}, {-524.167, -113.667}}, color = {255, 200, 0}));
+ connect(transformer1.electricalOutput, distributionPowerGrid2.electricalInput[1]) annotation(
+ Line(points = {{-587.4, -130}, {-581.4, -130}}, color = {255, 200, 0}));
+ connect(logicalExpression7.logicalOutput[1], logicalAnd2.logicalInput2) annotation(
+ Line(points = {{-627, -258}, {-613, -258}, {-613, -277}}, color = {53, 28, 117}));
+ connect(logicalAnd4.logicalOutput, testController.logicalInput1) annotation(
+ Line(points = {{-584, -41}, {-584, -51}, {-570, -51}, {-570, -59}}, color = {53, 28, 117}));
+ connect(distributionPowerGrid.electricalOutput[2], battery.electricalInput) annotation(
+ Line(points = {{-440.417, -101.667}, {-434.584, -101.667}, {-434.584, -73.9997}, {-455, -73.9997}}, color = {255, 200, 0}));
+ annotation(
+ Diagram(coordinateSystem(extent = {{-660, 80}, {-380, -340}})));
+end Microgrids;
diff --git a/Examples/package.mo b/Examples/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..8fb38372cb7a4430e81dfcb81fb2a0301b702b00
--- /dev/null
+++ b/Examples/package.mo
@@ -0,0 +1,13 @@
+within PNRG;
+
+package Examples
+
+
+
+
+
+
+
+ annotation(
+ Icon(graphics = {Text(origin = {-8, 2}, extent = {{-144, 114}, {144, -114}}, textString = "Ex")}));
+end Examples;
diff --git a/Examples/package.order b/Examples/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..97a8bd8a7462072b5fa0b24634100c50c1bdb80e
--- /dev/null
+++ b/Examples/package.order
@@ -0,0 +1,4 @@
+EnergyPark
+simpleExample
+Microgrids
+test
diff --git a/Examples/simpleExample.mo b/Examples/simpleExample.mo
new file mode 100644
index 0000000000000000000000000000000000000000..9858708430f2f853caa764e2afaaa0f2ab94e7e0
--- /dev/null
+++ b/Examples/simpleExample.mo
@@ -0,0 +1,95 @@
+within PNRG.Examples;
+
+model simpleExample
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput(NOut = 1, fileName = "P:/Programs/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-130, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput1(NOut = 1, fileName = "P:/Programs/PNRG/data2.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-130, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput111(NOut = 1, fileName = "P:/Programs/PNRG/data3.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {110, 80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-54, -20}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer1(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-54, -44}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.PowerPlants.PVPowerPlant pVPowerPlant(areaPV = 1) annotation(
+ Placement(visible = true, transformation(origin = {-80, -44}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.EnergyConsumer.ElectricityConsumer electricityConsumer annotation(
+ Placement(visible = true, transformation(origin = {150, -14}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.WindPowerPlant windPowerPlant(efficiencyTurbine = 0.5, number = testController.N, rotorLength = 3) annotation(
+ Placement(visible = true, transformation(origin = {-80, -20}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Storage.Battery battery(power = 10) annotation(
+ Placement(visible = true, transformation(origin = {64, -12}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression(NOut = 2, expression = distributionPowerGrid.powerInput > electricityConsumer.powerConsumption + electricityConsumer1.powerConsumption + battery.power) annotation(
+ Placement(visible = true, transformation(origin = {10, 98}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression1(NOut = 2, expression = windPowerPlant.currentPower + pVPowerPlant.currentPower <= electricityConsumer.powerConsumption + electricityConsumer1.powerConsumption+ battery.currentInputPower) annotation(
+ Placement(visible = true, transformation(origin = {-10, 52}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.IntegerController testController(NChange = 1, NMax = 10, NStart = 10, delay = 0.1) annotation(
+ Placement(visible = true, transformation(origin = {-46, 20}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Logics.LogicalExpression logicalExpression11(NOut = 1, expression = windPowerPlant.currentPower + pVPowerPlant.currentPower - windPowerPlant.singlePower > electricityConsumer.powerConsumption + electricityConsumer1.powerConsumption + battery.power) annotation(
+ Placement(visible = true, transformation(origin = {-80, 78}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression111(NOut = 1, expression = testController.N == testController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {26, 64}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd1 annotation(
+ Placement(visible = true, transformation(origin = {40, 30}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid(NIn = 3, NOut = 3, prioOut = {1, 2, 3}) annotation(
+ Placement(visible = true, transformation(origin = {64, -54}, extent = {{-30, 10}, {30, 30}}, rotation = 0)));
+ Sources.FileToTransitionOutput fileToTransitionOutput11(NOut = 1, fileName = "P:/Programs/PNRG/data4.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {110, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ EnergyConsumer.ElectricityConsumer electricityConsumer1 annotation(
+ Placement(visible = true, transformation(origin = {150, -44}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalAnd logicalAnd annotation(
+ Placement(visible = true, transformation(origin = {-50, 48}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ Logics.LogicalOr logicalOr annotation(
+ Placement(visible = true, transformation(origin = {82, 40}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ Logics.LogicalExpression logicalExpression2(NOut = 1, expression = testController.N < testController.NMax) annotation(
+ Placement(visible = true, transformation(origin = {58, 76}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(fileToTransitionOutput.fileOutput[1], pVPowerPlant.fileInput) annotation(
+ Line(points = {{-119, 50}, {-119, 50.6875}, {-109, 50.6875}, {-109, -44}, {-91, -44}}, color = {150, 150, 150}));
+ connect(pVPowerPlant.electricalOutput, transformer1.electricalInput) annotation(
+ Line(points = {{-69, -44}, {-61, -44}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput1.fileOutput[1], windPowerPlant.fileInput) annotation(
+ Line(points = {{-119, 80}, {-100, 80}, {-100, -20}, {-91, -20}}, color = {150, 150, 150}));
+ connect(windPowerPlant.electricalOutput, transformer.electricalInput) annotation(
+ Line(points = {{-69, -20}, {-61, -20}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput111.fileOutput[1], electricityConsumer.fileInput) annotation(
+ Line(points = {{121, 80}, {132, 80}, {132, -8}, {139, -8}}));
+ connect(logicalAnd1.logicalOutput, battery.logicalInput1) annotation(
+ Line(points = {{40, 19}, {40, -6}, {53, -6}}, color = {53, 28, 117}));
+ connect(logicalExpression111.logicalOutput[1], logicalAnd1.logicalInput1) annotation(
+ Line(points = {{37, 64}, {37, 63}, {45, 63}, {45, 42}, {46, 42}}, color = {53, 28, 117}));
+ connect(logicalExpression1.logicalOutput[1], logicalAnd1.logicalInput2) annotation(
+ Line(points = {{1, 52}, {36, 52}, {36, 42}}, color = {53, 28, 117}));
+ connect(logicalExpression1.logicalOutput[2], testController.logicalInput) annotation(
+ Line(points = {{1, 52}, {11, 52}, {11, 36}, {-41, 36}, {-41, 31}}, color = {53, 28, 117}));
+ connect(transformer.electricalOutput, distributionPowerGrid.electricalInput[1]) annotation(
+ Line(points = {{-48, -20}, {0, -20}, {0, -34}, {36.5, -34}}, color = {255, 200, 0}));
+ connect(transformer1.electricalOutput, distributionPowerGrid.electricalInput[2]) annotation(
+ Line(points = {{-48, -44}, {0, -44}, {0, -34}, {36.5, -34}}, color = {255, 200, 0}));
+ connect(battery.electricalOutput, distributionPowerGrid.electricalInput[3]) annotation(
+ Line(points = {{53, -12}, {18, -12}, {18, -34}, {36.5, -34}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid.electricalOutput[2], electricityConsumer.electricalInput) annotation(
+ Line(points = {{92, -34}, {112, -34}, {112, -14}, {140, -14}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid.electricalOutput[3], battery.electricalInput) annotation(
+ Line(points = {{92, -34}, {112, -34}, {112, -12}, {75, -12}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid.electricalOutput[1], electricityConsumer1.electricalInput) annotation(
+ Line(points = {{92, -34}, {112, -34}, {112, -44}, {140, -44}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput11.fileOutput[1], electricityConsumer1.fileInput) annotation(
+ Line(points = {{121, 50}, {128, 50}, {128, -38}, {139, -38}}, color = {150, 150, 150}));
+ connect(logicalAnd.logicalOutput, testController.logicalInput1) annotation(
+ Line(points = {{-50, 37}, {-50.5, 37}, {-50.5, 31}, {-51, 31}}, color = {53, 28, 117}));
+ connect(logicalExpression.logicalOutput[2], logicalAnd.logicalInput1) annotation(
+ Line(points = {{22, 98}, {30, 98}, {30, 78}, {-45, 78}, {-45, 59}}, color = {53, 28, 117}));
+ connect(logicalExpression11.logicalOutput[1], logicalAnd.logicalInput2) annotation(
+ Line(points = {{-69, 78}, {-55, 78}, {-55, 59}}, color = {53, 28, 117}));
+ connect(logicalOr.logicalOutput, battery.logicalInput) annotation(
+ Line(points = {{82, 30}, {82, -6}, {76, -6}}, color = {53, 28, 117}));
+ connect(logicalExpression.logicalOutput[1], logicalOr.logicalInput) annotation(
+ Line(points = {{22, 98}, {86, 98}, {86, 52}}, color = {53, 28, 117}));
+ connect(logicalExpression2.logicalOutput[1], logicalOr.logicalInput1) annotation(
+ Line(points = {{70, 76}, {80, 76}, {80, 52}}, color = {53, 28, 117}));
+ annotation(
+ Diagram(coordinateSystem(extent = {{-160, 120}, {180, -60}})),
+ version = "",
+ uses(PNlib(version = "2.2")));
+end simpleExample;
diff --git a/Examples/test.mo b/Examples/test.mo
new file mode 100644
index 0000000000000000000000000000000000000000..6c058a9715be27bcc8baa061306bdae3e540170c
--- /dev/null
+++ b/Examples/test.mo
@@ -0,0 +1,201 @@
+within PNRG.Examples;
+
+model test
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid(NIn = 3, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {33, 101}, extent = {{-29, 9.66666}, {29, 29}}, rotation = 0)));
+ PNRG.PowerPlants.PVPowerPlant pVPowerPlant(areaPV = 200, efficiency_PV = 0.3) annotation(
+ Placement(visible = true, transformation(origin = {-118, 92}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid1(NIn = 4, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {47, 13}, extent = {{-29, 9.66666}, {29, 29}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput2(NOut = 1, fileName = "P:/Programs/PNRG_test/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-152, 92}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput111(NOut = 1, fileName = "P:/Programs/PNRG/data3.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {114, 54}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer1(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-94, 92}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Distribution.ConditionalConnection conditionalConnection(power = distributionPowerGrid2.powerInput) annotation(
+ Placement(visible = true, transformation(origin = {-14, 108}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.EnergyConsumer.ElectricityConsumer electricityConsumer annotation(
+ Placement(visible = true, transformation(origin = {114, 32}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-92, 132}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.EnergyConsumer.ElectricityConsumer electricityConsumer1 annotation(
+ Placement(visible = true, transformation(origin = {114, 120}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.ConditionalConnection conditionalConnection1(power = distributionPowerGrid2.powerInput) annotation(
+ Placement(visible = true, transformation(origin = {-14, 76}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression10(NOut = 1, expression = electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower) annotation(
+ Placement(visible = true, transformation(origin = {58, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.WindPowerPlant windPowerPlant1(efficiencyTurbine = 0.4, number = integerController.N, rotorLength = 2) annotation(
+ Placement(visible = true, transformation(origin = {-104, 42}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput11(NOut = 1, fileName = "P:/Programs/PNRG/data4.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {114, 142}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Distribution.DistributionPowerGrid distributionPowerGrid2(NIn = 1, NOut = 2, Voltage = 240, prioOut = {1, 2}) annotation(
+ Placement(visible = true, transformation(origin = {-58, 74}, extent = {{-26, 8.66667}, {26, 26}}, rotation = 0)));
+ PNRG.Storage.Battery battery(power = 10) annotation(
+ Placement(visible = true, transformation(origin = {34, 148}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.PVPowerPlant pVPowerPlant1(areaPV = 30) annotation(
+ Placement(visible = true, transformation(origin = {-104, 18}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.LogicalExpression logicalExpression6(NOut = 1, expression = not (electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower)) annotation(
+ Placement(visible = true, transformation(origin = {58, 94}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput1(NOut = 1, fileName = "P:/Programs/PNRG_test/PNRG/data.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-152, 18}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.PowerPlants.WindPowerPlant windPowerPlant(efficiencyTurbine = 0.4, number = testController.N, rotorLength = 3) annotation(
+ Placement(visible = true, transformation(origin = {-118, 132}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer4(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-78, 42}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Storage.Battery battery1(power = 10) annotation(
+ Placement(visible = true, transformation(origin = {44, 0}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Distribution.Transformer transformer2(efficiency = 0.999) annotation(
+ Placement(visible = true, transformation(origin = {-78, 18}, extent = {{-6, -6}, {6, 6}}, rotation = 0)));
+ PNRG.Logics.IntegerController testController(NChange = 1, NMax = 10, NStart = 10, delay = 0.1) annotation(
+ Placement(visible = true, transformation(origin = {-66, 152}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput(NOut = 1, fileName = "P:/Programs/PNRG/data2.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-152, 42}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Sources.FileToTransitionOutput fileToTransitionOutput4(NOut = 1, fileName = "P:/Programs/PNRG/data2.txt", tableName = "tab1") annotation(
+ Placement(visible = true, transformation(origin = {-152, 132}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.IntegerController integerController(NChange = 1, NMax = 8, NStart = 8, delay = 0.1) annotation(
+ Placement(visible = true, transformation(origin = {-104, -14}, extent = {{-10, 10}, {10, -10}}, rotation = 90)));
+ PNlib.Components.PD Battery1Charge(nIn = 2, nOut = 2) annotation(
+ Placement(visible = true, transformation(origin = {-610, 140}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD Battery1Idle(nIn = 4, nOut = 4, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-502, 140}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD Battery1Discharge(nIn = 2, nOut = 2) annotation(
+ Placement(visible = true, transformation(origin = {-408, 140}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD Battery2Charge(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-628, -66}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD Battery2Discharge(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-418, -68}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD Battery2Idle(nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-518, -68}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD IncreaseActiveTurbines1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-632, -116}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD DecreaseActiveTurbines1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-404, -120}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD DecreaseActiveTurbines2(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-392, -170}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD IncreaseActiveTurbines2(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-630, -166}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD ActiveTurbines1Idle(nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-530, -118}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD ActiveTurbines2Idle(nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-518, -170}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD Connect(nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-560, 310}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD Disconnect(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-460, 310}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t1(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-516, 310}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t11(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = not (electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower), nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-516, 346}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = not (electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower), nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-558, 172}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t2(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-558, 120}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t3(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-456, 120}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t4(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = not (electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower), nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-456, 156}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t5(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-558, 92}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t6(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = not (electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower), nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-558, 202}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t7(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-456, 90}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t8(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = electricityConsumer.powerConsumption + battery1.currentInputPower - battery1.currentOutputPower - windPowerPlant1.currentPower - pVPowerPlant1.currentPower > electricityConsumer1.powerConsumption - windPowerPlant.currentPower + battery.currentInputPower - battery.currentOutputPower, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-456, 188}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+equation
+ connect(distributionPowerGrid1.electricalOutput[2], battery1.electricalInput) annotation(
+ Line(points = {{73.5833, 32.3333}, {83.4163, 32.3333}, {83.4163, -0.33264}, {55.4173, -0.33264}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput4.fileOutput[1], windPowerPlant.fileInput) annotation(
+ Line(points = {{-141, 132}, {-128, 132}}, color = {150, 150, 150}));
+ connect(distributionPowerGrid1.electricalOutput[1], electricityConsumer.electricalInput) annotation(
+ Line(points = {{73.5833, 32.3333}, {102.583, 32.3333}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput2.fileOutput[1], pVPowerPlant.fileInput) annotation(
+ Line(points = {{-141, 92}, {-129, 92}}, color = {150, 150, 150}));
+ connect(distributionPowerGrid2.electricalOutput[2], conditionalConnection1.electricalInput) annotation(
+ Line(points = {{-34.1667, 91.3333}, {-34.1667, 91.6673}, {-28.1667, 91.6673}, {-28.1667, 76.0003}, {-24.9997, 76.0003}}, color = {255, 200, 0}));
+ connect(logicalExpression6.logicalOutput[1], conditionalConnection.logicalInput) annotation(
+ Line(points = {{47, 94}, {-15, 94}, {-15, 98}}, color = {53, 28, 117}));
+ connect(transformer4.electricalOutput, distributionPowerGrid1.electricalInput[1]) annotation(
+ Line(points = {{-71.4, 42}, {-3.4, 42}, {-3.4, 32}, {20, 32}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput11.fileOutput[1], electricityConsumer1.fileInput) annotation(
+ Line(points = {{103, 142}, {97, 142}, {97, 126}, {103, 126}}, color = {150, 150, 150}));
+ connect(windPowerPlant.electricalOutput, transformer.electricalInput) annotation(
+ Line(points = {{-107, 132}, {-99, 132}}, color = {255, 200, 0}));
+ connect(battery1.electricalOutput, distributionPowerGrid1.electricalInput[4]) annotation(
+ Line(points = {{33, 0}, {13, 0}, {13, 32}, {20, 32}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput1.fileOutput[1], pVPowerPlant1.fileInput) annotation(
+ Line(points = {{-141, 18}, {-115, 18}}, color = {150, 150, 150}));
+ connect(conditionalConnection.electricalOutput, distributionPowerGrid.electricalInput[2]) annotation(
+ Line(points = {{-3, 108}, {1, 108}, {1, 120}, {6, 120}}, color = {255, 200, 0}));
+ connect(transformer.electricalOutput, distributionPowerGrid.electricalInput[1]) annotation(
+ Line(points = {{-85.4, 132}, {0.6, 132}, {0.6, 120}, {6, 120}}, color = {255, 200, 0}));
+ connect(windPowerPlant1.electricalOutput, transformer4.electricalInput) annotation(
+ Line(points = {{-93, 42}, {-85, 42}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput111.fileOutput[1], electricityConsumer.fileInput) annotation(
+ Line(points = {{103, 54}, {97, 54}, {97, 38}, {103, 38}}, color = {150, 150, 150}));
+ connect(transformer1.electricalOutput, distributionPowerGrid2.electricalInput[1]) annotation(
+ Line(points = {{-87.4, 92}, {-81.4, 92}}, color = {255, 200, 0}));
+ connect(battery.electricalOutput, distributionPowerGrid.electricalInput[3]) annotation(
+ Line(points = {{23, 148}, {3, 148}, {3, 120}, {6, 120}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid.electricalOutput[2], battery.electricalInput) annotation(
+ Line(points = {{59.5833, 120.333}, {65.4163, 120.333}, {65.4163, 148.001}, {45.0003, 148.001}}, color = {255, 200, 0}));
+ connect(fileToTransitionOutput.fileOutput[1], windPowerPlant1.fileInput) annotation(
+ Line(points = {{-141, 42}, {-115, 42}}, color = {150, 150, 150}));
+ connect(logicalExpression10.logicalOutput[1], conditionalConnection1.logicalInput) annotation(
+ Line(points = {{47, 60}, {-14, 60}, {-14, 65}}, color = {53, 28, 117}));
+ connect(transformer2.electricalOutput, distributionPowerGrid1.electricalInput[2]) annotation(
+ Line(points = {{-71.4, 18}, {-3.4, 18}, {-3.4, 32}, {20, 32}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid2.electricalOutput[1], conditionalConnection.electricalInput) annotation(
+ Line(points = {{-34.1667, 91.3333}, {-28.1667, 91.3333}, {-28.1667, 108.333}, {-24.1667, 108.333}}, color = {255, 200, 0}));
+ connect(conditionalConnection1.electricalOutput, distributionPowerGrid1.electricalInput[3]) annotation(
+ Line(points = {{-3, 76}, {3, 76}, {3, 32}, {20, 32}}, color = {255, 200, 0}));
+ connect(pVPowerPlant.electricalOutput, transformer1.electricalInput) annotation(
+ Line(points = {{-107, 92}, {-101, 92}}, color = {255, 200, 0}));
+ connect(pVPowerPlant1.electricalOutput, transformer2.electricalInput) annotation(
+ Line(points = {{-93, 18}, {-85, 18}}, color = {255, 200, 0}));
+ connect(distributionPowerGrid.electricalOutput[1], electricityConsumer1.electricalInput) annotation(
+ Line(points = {{59.5833, 120.333}, {62.3323, 120.333}, {62.3323, 120.667}, {102.416, 120.667}}, color = {255, 200, 0}));
+ connect(Connect.outTransition[1], t1.inPlaces[1]) annotation(
+ Line(points = {{-549.2, 310}, {-519.2, 310}}, thickness = 0.5));
+ connect(t1.outPlaces[1], Disconnect.inTransition[1]) annotation(
+ Line(points = {{-511.2, 310}, {-469.2, 310}}, thickness = 0.5));
+ connect(Disconnect.outTransition[1], t11.inPlaces[1]) annotation(
+ Line(points = {{-449.2, 310}, {-433.2, 310}, {-433.2, 346}, {-511.2, 346}}, thickness = 0.5));
+ connect(t11.outPlaces[1], Connect.inTransition[1]) annotation(
+ Line(points = {{-520.8, 346}, {-586.8, 346}, {-586.8, 310}, {-570.8, 310}}, thickness = 0.5));
+ connect(Battery1Charge.outTransition[1], t2.inPlaces[1]) annotation(
+ Line(points = {{-600, 140}, {-580, 140}, {-580, 120}, {-562, 120}}, thickness = 0.5));
+ connect(t2.outPlaces[1], Battery1Idle.inTransition[1]) annotation(
+ Line(points = {{-554, 120}, {-530, 120}, {-530, 140}, {-512, 140}}, thickness = 0.5));
+ connect(Battery1Charge.outTransition[2], t5.inPlaces[1]) annotation(
+ Line(points = {{-600, 140}, {-580, 140}, {-580, 92}, {-562, 92}}, thickness = 0.5));
+ connect(t5.outPlaces[1], Battery1Idle.inTransition[2]) annotation(
+ Line(points = {{-554, 92}, {-530, 92}, {-530, 140}, {-512, 140}}, thickness = 0.5));
+ connect(Battery1Idle.outTransition[1], t3.inPlaces[1]) annotation(
+ Line(points = {{-492, 140}, {-480, 140}, {-480, 120}, {-460, 120}}, thickness = 0.5));
+ connect(Battery1Idle.outTransition[2], t7.inPlaces[1]) annotation(
+ Line(points = {{-492, 140}, {-480, 140}, {-480, 90}, {-460, 90}}, thickness = 0.5));
+ connect(t3.outPlaces[1], Battery1Discharge.inTransition[1]) annotation(
+ Line(points = {{-452, 120}, {-440, 120}, {-440, 140}, {-418, 140}}, thickness = 0.5));
+ connect(t7.outPlaces[1], Battery1Discharge.inTransition[2]) annotation(
+ Line(points = {{-452, 90}, {-440, 90}, {-440, 140}, {-418, 140}}, thickness = 0.5));
+ connect(Battery1Discharge.outTransition[1], t4.inPlaces[1]) annotation(
+ Line(points = {{-398, 140}, {-380, 140}, {-380, 156}, {-452, 156}}, thickness = 0.5));
+ connect(Battery1Discharge.outTransition[2], t8.inPlaces[1]) annotation(
+ Line(points = {{-398, 140}, {-380, 140}, {-380, 188}, {-452, 188}}, thickness = 0.5));
+ connect(t4.outPlaces[1], Battery1Idle.inTransition[3]) annotation(
+ Line(points = {{-460, 156}, {-530, 156}, {-530, 140}, {-512, 140}}, thickness = 0.5));
+ connect(t8.outPlaces[1], Battery1Idle.inTransition[4]) annotation(
+ Line(points = {{-460, 188}, {-530, 188}, {-530, 140}, {-512, 140}}, thickness = 0.5));
+ connect(Battery1Idle.outTransition[3], t.inPlaces[1]) annotation(
+ Line(points = {{-492, 140}, {-480, 140}, {-480, 172}, {-554, 172}}, thickness = 0.5));
+ connect(Battery1Idle.outTransition[4], t6.inPlaces[1]) annotation(
+ Line(points = {{-492, 140}, {-480, 140}, {-480, 202}, {-554, 202}}, thickness = 0.5));
+ connect(t.outPlaces[1], Battery1Charge.inTransition[1]) annotation(
+ Line(points = {{-562, 172}, {-630, 172}, {-630, 140}, {-620, 140}}, thickness = 0.5));
+ connect(t6.outPlaces[1], Battery1Charge.inTransition[2]) annotation(
+ Line(points = {{-562, 202}, {-630, 202}, {-630, 140}, {-620, 140}}, thickness = 0.5));
+ annotation(
+ Diagram(coordinateSystem(extent = {{-700, 380}, {180, -220}})));
+end test;
diff --git a/Interfaces/ElectricalInput.mo b/Interfaces/ElectricalInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..231bea6e1ec62a8c96e70edd7749ca0232eebe4f
--- /dev/null
+++ b/Interfaces/ElectricalInput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector ElectricalInput
+ extends PNlib.Interfaces.TransitionIn;
+ annotation(
+ Icon(graphics = {Polygon(lineColor = {255, 200, 0}, fillColor = {255, 200, 0}, fillPattern = FillPattern.Solid, points = {{-100, 100}, {98, 0}, {-100, -100}, {-100, 100}}), Polygon(origin = {10, -26}, fillColor = {255, 255, 255}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, points = {{-80, 82}, {34, 26}, {-80, -32}, {-80, 82}})}));
+end ElectricalInput;
\ No newline at end of file
diff --git a/Interfaces/ElectricalOutput.mo b/Interfaces/ElectricalOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..67dd50ffc2abd541c825db24a5fb897da72604a1
--- /dev/null
+++ b/Interfaces/ElectricalOutput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector ElectricalOutput
+ extends PNlib.Interfaces.PlaceOut;
+ annotation(
+ Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {255, 200, 0}, fillColor = {255, 200, 0}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end ElectricalOutput;
\ No newline at end of file
diff --git a/Interfaces/FileInput.mo b/Interfaces/FileInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..45c39a413662dbe10c9694dd3e1923e6f733f01c
--- /dev/null
+++ b/Interfaces/FileInput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector FileInput
+ extends PNlib.Interfaces.TransitionIn;
+ annotation(
+ Icon(graphics = {Polygon(lineColor = {150, 150, 150}, fillColor = {150, 150, 150}, fillPattern = FillPattern.Solid, points = {{-100, 100}, {98, 0}, {-100, -100}, {-100, 100}}), Polygon(origin = {10, -26}, fillColor = {255, 255, 255}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, points = {{-80, 82}, {34, 26}, {-80, -32}, {-80, 82}})}));
+end FileInput;
\ No newline at end of file
diff --git a/Interfaces/FileOutput.mo b/Interfaces/FileOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..2bd3310d7f14f1c88a552be7f47638435c33aea1
--- /dev/null
+++ b/Interfaces/FileOutput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector FileOutput
+ extends PNlib.Interfaces.PlaceOut;
+ annotation(
+ Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {150, 150, 150}, fillColor = {150, 150, 150}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end FileOutput;
\ No newline at end of file
diff --git a/Interfaces/HeatInput.mo b/Interfaces/HeatInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..d1451d160f8aa7fb044b96a610df183a86afe144
--- /dev/null
+++ b/Interfaces/HeatInput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector HeatInput
+ extends PNlib.Interfaces.TransitionIn;
+ annotation(
+ Icon(graphics = {Polygon(lineColor = {255, 80, 50}, fillColor = {255, 80, 50}, fillPattern = FillPattern.Solid, points = {{-100, 100}, {98, 0}, {-100, -100}, {-100, 100}}), Polygon(origin = {10, -26}, fillColor = {255, 255, 255}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, points = {{-80, 82}, {34, 26}, {-80, -32}, {-80, 82}})}));
+end HeatInput;
\ No newline at end of file
diff --git a/Interfaces/HeatOutput.mo b/Interfaces/HeatOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..24cc0bd84e436674e805f986662cde51d7a8f45c
--- /dev/null
+++ b/Interfaces/HeatOutput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector HeatOutput
+ extends PNlib.Interfaces.PlaceOut;
+ annotation(
+ Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {255, 80, 50}, fillColor = {255, 80, 50}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end HeatOutput;
\ No newline at end of file
diff --git a/Interfaces/HydrogenInput.mo b/Interfaces/HydrogenInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..4fcd6ea4ad9efc6ef7dea5d9e74fa7739678ca2f
--- /dev/null
+++ b/Interfaces/HydrogenInput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector HydrogenInput
+ extends PNlib.Interfaces.TransitionIn;
+ annotation(
+ Icon(graphics = {Polygon(lineColor = {106, 168, 79}, fillColor = {106, 168, 79}, fillPattern = FillPattern.Solid, points = {{-100, 100}, {98, 0}, {-100, -100}, {-100, 100}}), Polygon(origin = {10, -26}, fillColor = {255, 255, 255}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, points = {{-80, 82}, {34, 26}, {-80, -32}, {-80, 82}})}));
+end HydrogenInput;
\ No newline at end of file
diff --git a/Interfaces/HydrogenOutput.mo b/Interfaces/HydrogenOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..4637b57501f6050bb8d3048322429c4f52c29de2
--- /dev/null
+++ b/Interfaces/HydrogenOutput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector HydrogenOutput
+ extends PNlib.Interfaces.PlaceOut;
+ annotation(
+ Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {106, 168, 79}, fillColor = {106, 168, 79}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end HydrogenOutput;
\ No newline at end of file
diff --git a/Interfaces/LogicalInput.mo b/Interfaces/LogicalInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..0852c1a219021f574bfc061a40dfa46b022fa560
--- /dev/null
+++ b/Interfaces/LogicalInput.mo
@@ -0,0 +1,95 @@
+within PNRG.Interfaces;
+
+connector LogicalInput
+ import PNlib.Types.ArcType;
+ output Real t "Markings of input places" annotation(
+ HideResult = true);
+ output Integer tint "Integer Markings of input places" annotation(
+ HideResult = true);
+ output Real minTokens "Minimum capacites of input places" annotation(
+ HideResult = true);
+ output Integer minTokensint "Integer minimum capacites of input places" annotation(
+ HideResult = true);
+ output Boolean enable "Is the transition enabled by input places?" annotation(
+ HideResult = true);
+ output Real decreasingFactor "Factor of continuous input places for decreasing the speed" annotation(
+ HideResult = true);
+ output Boolean disPlace "Types of input places (discrete or continuous)" annotation(
+ HideResult = true);
+ output ArcType arcType "Types of input arcs (normal, test, inhibition, or read)" annotation(
+ HideResult = true);
+ output Boolean fed "Are the continuous input places fed?" annotation(
+ HideResult = true);
+ output Real speedSum "Input speeds of continuous input places" annotation(
+ HideResult = true);
+ output Boolean tokenInOut "Do the input places have a discrete token change?" annotation(
+ HideResult = true);
+ output Real testValue "Test value of a test or inhibitor arc" annotation(
+ HideResult = true);
+ output Integer testValueint "Integer test value of a test or inhibitor arc" annotation(
+ HideResult = true);
+ output Boolean normalArc "Double arc: test and normal arc or inhibitor and normal arc" annotation(
+ HideResult = true);
+ input Boolean active "Is the transition active?" annotation(
+ HideResult = true);
+ input Boolean fire "Does the transition fire?" annotation(
+ HideResult = true);
+ input Real arcWeight "Input arc weights of the transition" annotation(
+ HideResult = true);
+ input Integer arcWeightint "Integer input arc weights of the transition" annotation(
+ HideResult = true);
+ input Boolean disTransition "Type of the transition(discrete/stochastic or continuous)" annotation(
+ HideResult = true);
+ input Real instSpeed "Instantaneous speed of a continuous transition" annotation(
+ HideResult = true);
+ input Real prelimSpeed "Preliminary speed of a continuous transition" annotation(
+ HideResult = true);
+ input Real maxSpeed "Maximum speed of a continuous transition" annotation(
+ HideResult = true);
+ output Real t_inhibitor "Markings of input places" annotation(
+ HideResult = true);
+ output Integer tint_inhibitor "Integer Markings of input places" annotation(
+ HideResult = true);
+ output Real minTokens_inhibitor "Minimum capacites of input places" annotation(
+ HideResult = true);
+ output Integer minTokensint_inhibitor "Integer minimum capacites of input places" annotation(
+ HideResult = true);
+ output Boolean enable_inhibitor "Is the transition enabled by input places?" annotation(
+ HideResult = true);
+ output Real decreasingFactor_inhibitor "Factor of continuous input places for decreasing the speed" annotation(
+ HideResult = true);
+ output Boolean disPlace_inhibitor "Types of input places (discrete or continuous)" annotation(
+ HideResult = true);
+ output ArcType arcType_inhibitor "Types of input arcs (normal, test, inhibition, or read)" annotation(
+ HideResult = true);
+ output Boolean fed_inhibitor "Are the continuous input places fed?" annotation(
+ HideResult = true);
+ output Real speedSum_inhibitor "Input speeds of continuous input places" annotation(
+ HideResult = true);
+ output Boolean tokenInOut_inhibitor "Do the input places have a discrete token change?" annotation(
+ HideResult = true);
+ output Real testValue_inhibitor "Test value of a test or inhibitor arc" annotation(
+ HideResult = true);
+ output Integer testValueint_inhibitor "Integer test value of a test or inhibitor arc" annotation(
+ HideResult = true);
+ output Boolean normalArc_inhibitor "Double arc: test and normal arc or inhibitor and normal arc" annotation(
+ HideResult = true);
+ input Boolean active_inhibitor "Is the transition active?" annotation(
+ HideResult = true);
+ input Boolean fire_inhibitor "Does the transition fire?" annotation(
+ HideResult = true);
+ input Real arcWeight_inhibitor "Input arc weights of the transition" annotation(
+ HideResult = true);
+ input Integer arcWeightint_inhibitor "Integer input arc weights of the transition" annotation(
+ HideResult = true);
+ input Boolean disTransition_inhibitor "Type of the transition(discrete/stochastic or continuous)" annotation(
+ HideResult = true);
+ input Real instSpeed_inhibitor "Instantaneous speed of a continuous transition" annotation(
+ HideResult = true);
+ input Real prelimSpeed_inhibitor "Preliminary speed of a continuous transition" annotation(
+ HideResult = true);
+ input Real maxSpeed_inhibitor "Maximum speed of a continuous transition" annotation(
+ HideResult = true);
+ annotation(
+ Icon(graphics = {Polygon(lineColor = {53, 28, 117}, fillColor = {53, 28, 117}, fillPattern = FillPattern.Solid, points = {{-100, 100}, {98, 0}, {-100, -100}, {-100, 100}}), Polygon(origin = {10, -24}, fillColor = {255, 255, 255}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, points = {{-80, 82}, {34, 26}, {-80, -32}, {-80, 82}})}));
+end LogicalInput;
\ No newline at end of file
diff --git a/Interfaces/LogicalOutput.mo b/Interfaces/LogicalOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..49f135a8160d8dfdc8390eedbc715a3d6b96c484
--- /dev/null
+++ b/Interfaces/LogicalOutput.mo
@@ -0,0 +1,95 @@
+within PNRG.Interfaces;
+
+connector LogicalOutput
+ import PNlib.Types.ArcType;
+ output Boolean active "Are the output transitions active?" annotation(
+ HideResult = true);
+ output Boolean fire "Do the output transitions fire?" annotation(
+ HideResult = true);
+ output Real arcWeight "Arc weights of output transitions" annotation(
+ HideResult = true);
+ output Integer arcWeightint "Integer arc weights of output transitions" annotation(
+ HideResult = true);
+ output Boolean disTransition "Are the output transitions discrete?" annotation(
+ HideResult = true);
+ output Real instSpeed "Instantaneous speeds of continuous output transitions" annotation(
+ HideResult = true);
+ output Real prelimSpeed "Preliminary speeds of continuous output transitions" annotation(
+ HideResult = true);
+ output Real maxSpeed "Maximum speeds of continuous output transitions" annotation(
+ HideResult = true);
+ input Real t "Marking of the place" annotation(
+ HideResult = true);
+ input Integer tint "Integer marking of the place" annotation(
+ HideResult = true);
+ input Real minTokens "Minimum capacity of the place" annotation(
+ HideResult = true);
+ input Integer minTokensint "Integer minimum capacity of the place" annotation(
+ HideResult = true);
+ input Boolean enable "Which of the output transitions are enabled by the place?" annotation(
+ HideResult = true);
+ input Real decreasingFactor "Factor for decreasing the speed of continuous input transitions" annotation(
+ HideResult = true);
+ input Boolean disPlace "Type of the place (discrete or continuous)" annotation(
+ HideResult = true);
+ input ArcType arcType "Type of output arcs (normal, test, inhibition, or read)" annotation(
+ HideResult = true);
+ input Boolean fed "Is the continuous place fed by input transitions?" annotation(
+ HideResult = true);
+ input Real speedSum "Input speed of a continuous place" annotation(
+ HideResult = true);
+ input Boolean tokenInOut "Does the place have a discrete token change?" annotation(
+ HideResult = true);
+ input Real testValue "Test value of a test or inhibitor arc" annotation(
+ HideResult = true);
+ input Integer testValueint "Integer test value of a test or inhibitor arc" annotation(
+ HideResult = true);
+ input Boolean normalArc "Double arc: test and normal arc or inhibitor and normal arc" annotation(
+ HideResult = true);
+ output Boolean active_inhibitor "Are the output transitions active?" annotation(
+ HideResult = true);
+ output Boolean fire_inhibitor "Do the output transitions fire?" annotation(
+ HideResult = true);
+ output Real arcWeight_inhibitor "Arc weights of output transitions" annotation(
+ HideResult = true);
+ output Integer arcWeightint_inhibitor "Integer arc weights of output transitions" annotation(
+ HideResult = true);
+ output Boolean disTransition_inhibitor "Are the output transitions discrete?" annotation(
+ HideResult = true);
+ output Real instSpeed_inhibitor "Instantaneous speeds of continuous output transitions" annotation(
+ HideResult = true);
+ output Real prelimSpeed_inhibitor "Preliminary speeds of continuous output transitions" annotation(
+ HideResult = true);
+ output Real maxSpeed_inhibitor "Maximum speeds of continuous output transitions" annotation(
+ HideResult = true);
+ input Real t_inhibitor "Marking of the place" annotation(
+ HideResult = true);
+ input Integer tint_inhibitor "Integer marking of the place" annotation(
+ HideResult = true);
+ input Real minTokens_inhibitor "Minimum capacity of the place" annotation(
+ HideResult = true);
+ input Integer minTokensint_inhibitor "Integer minimum capacity of the place" annotation(
+ HideResult = true);
+ input Boolean enable_inhibitor "Which of the output transitions are enabled by the place?" annotation(
+ HideResult = true);
+ input Real decreasingFactor_inhibitor "Factor for decreasing the speed of continuous input transitions" annotation(
+ HideResult = true);
+ input Boolean disPlace_inhibitor "Type of the place (discrete or continuous)" annotation(
+ HideResult = true);
+ input ArcType arcType_inhibitor "Type of output arcs (normal, test, inhibition, or read)" annotation(
+ HideResult = true);
+ input Boolean fed_inhibitor "Is the continuous place fed by input transitions?" annotation(
+ HideResult = true);
+ input Real speedSum_inhibitor "Input speed of a continuous place" annotation(
+ HideResult = true);
+ input Boolean tokenInOut_inhibitor "Does the place have a discrete token change?" annotation(
+ HideResult = true);
+ input Real testValue_inhibitor "Test value of a test or inhibitor arc" annotation(
+ HideResult = true);
+ input Integer testValueint_inhibitor "Integer test value of a test or inhibitor arc" annotation(
+ HideResult = true);
+ input Boolean normalArc_inhibitor "Double arc: test and normal arc or inhibitor and normal arc" annotation(
+ HideResult = true);
+ annotation(
+ Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {53, 28, 117}, fillColor = {53, 28, 117}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end LogicalOutput;
\ No newline at end of file
diff --git a/Interfaces/NaturalGasInput.mo b/Interfaces/NaturalGasInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..b65fec665993cf88f9b922ccba3c9231cd0d05df
--- /dev/null
+++ b/Interfaces/NaturalGasInput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector NaturalGasInput
+ extends PNlib.Interfaces.TransitionIn;
+ annotation(
+ Icon(graphics = {Polygon(lineColor = {126, 58, 0}, fillColor = {126, 58, 0}, fillPattern = FillPattern.Solid, points = {{-100, 100}, {98, 0}, {-100, -100}, {-100, 100}}), Polygon(origin = {10, -26}, fillColor = {255, 255, 255}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, points = {{-80, 82}, {34, 26}, {-80, -32}, {-80, 82}})}));
+end NaturalGasInput;
\ No newline at end of file
diff --git a/Interfaces/NaturalGasOutput.mo b/Interfaces/NaturalGasOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..6e12b75c218cea79bf19b85e6cb602714884c757
--- /dev/null
+++ b/Interfaces/NaturalGasOutput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector NaturalGasOutput
+ extends PNlib.Interfaces.PlaceOut;
+ annotation(
+ Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {126, 58, 0}, fillColor = {126, 58, 0}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end NaturalGasOutput;
\ No newline at end of file
diff --git a/Interfaces/OxygenInput.mo b/Interfaces/OxygenInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..3f87637bf4d9de3dda488df37686e639875bd868
--- /dev/null
+++ b/Interfaces/OxygenInput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector OxygenInput
+ extends PNlib.Interfaces.TransitionIn;
+ annotation(
+ Icon(graphics = {Polygon(lineColor = {11, 83, 148}, fillColor = {11, 83, 148}, fillPattern = FillPattern.Solid, points = {{-100, 100}, {98, 0}, {-100, -100}, {-100, 100}}), Polygon(origin = {10, -26}, fillColor = {255, 255, 255}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, points = {{-80, 82}, {34, 26}, {-80, -32}, {-80, 82}})}));
+end OxygenInput;
\ No newline at end of file
diff --git a/Interfaces/OxygenOutput.mo b/Interfaces/OxygenOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..1e8749834242ea9d6cb59ea7c6399d82df9369a6
--- /dev/null
+++ b/Interfaces/OxygenOutput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector OxygenOutput
+ extends PNlib.Interfaces.PlaceOut;
+ annotation(
+ Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {11, 83, 148}, fillColor = {11, 83, 148}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end OxygenOutput;
\ No newline at end of file
diff --git a/Interfaces/WaterInput.mo b/Interfaces/WaterInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..3309b6da8c26c14d75db51e36f665b59312c319c
--- /dev/null
+++ b/Interfaces/WaterInput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector WaterInput
+ extends PNlib.Interfaces.TransitionIn;
+ annotation(
+ Icon(graphics = {Polygon(lineColor = {61, 133, 198}, fillColor = {61, 133, 198}, fillPattern = FillPattern.Solid, points = {{-100, 100}, {98, 0}, {-100, -100}, {-100, 100}}), Polygon(origin = {10, -26}, fillColor = {255, 255, 255}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, points = {{-80, 82}, {34, 26}, {-80, -32}, {-80, 82}})}));
+end WaterInput;
\ No newline at end of file
diff --git a/Interfaces/WaterOutput.mo b/Interfaces/WaterOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..c535d150bdfd2c9a3c30ecf04c0364b214bc0297
--- /dev/null
+++ b/Interfaces/WaterOutput.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector WaterOutput
+ extends PNlib.Interfaces.PlaceOut;
+ annotation(
+ Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {61, 133, 198}, fillColor = {61, 133, 198}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end WaterOutput;
\ No newline at end of file
diff --git a/Interfaces/package.mo b/Interfaces/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..990c26b835ff047329c79043a65f99c7c5541ea5
--- /dev/null
+++ b/Interfaces/package.mo
@@ -0,0 +1,37 @@
+within PNRG;
+
+package Interfaces
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ annotation(
+ Icon(graphics = {Polygon(origin = {-60, 0}, fillPattern = FillPattern.Solid, points = {{-30, 60}, {-30, -60}, {30, 0}, {-30, 60}}), Polygon(origin = {62, 0}, fillPattern = FillPattern.Solid, points = {{-30, 60}, {-30, -60}, {30, 0}, {-30, 60}}), Rectangle(fillPattern = FillPattern.Solid, extent = {{-38, 4}, {38, -4}})}));
+end Interfaces;
diff --git a/Interfaces/package.order b/Interfaces/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..1105ae5642d740b3c3419f5e90bc27448fb7249a
--- /dev/null
+++ b/Interfaces/package.order
@@ -0,0 +1,16 @@
+WaterOutput
+WaterInput
+OxygenOutput
+OxygenInput
+LogicalOutput
+LogicalInput
+HydrogenOutput
+HydrogenInput
+HeatOutput
+HeatInput
+ElectricalOutput
+ElectricalInput
+NaturalGasInput
+NaturalGasOutput
+FileInput
+FileOutput
diff --git a/Logics/Clock.mo b/Logics/Clock.mo
new file mode 100644
index 0000000000000000000000000000000000000000..13788fac15c8eba363e5b9e42b8095042533690d
--- /dev/null
+++ b/Logics/Clock.mo
@@ -0,0 +1,47 @@
+within PNRG.Logics;
+
+model Clock
+ //parameter Integer NOut "Number of Outputs" annotation(Dialog(enable = true, group = "General properties"));
+ Real periodDuration(unit = "s") "duration of oscillatory period" annotation(
+ Dialog(enable = true, group = "Clock properties"));
+ PNRG.Interfaces.LogicalOutput logicalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.CombineLogicalOutput combineLogicalOutput annotation(
+ Placement(visible = true, transformation(origin = {72, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD p1(maxTokens = 1, nIn = 1, nOut = 3) annotation(
+ Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD p11(maxTokens = 1, nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-68, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TD t1(arcWeightIn = {1}, arcWeightOut = {1}, delay = periodDuration/2, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, -30}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.TD td(arcWeightIn = {1}, arcWeightOut = {1}, delay = periodDuration/2, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TA ta1(realTestArc = false) annotation(
+ Placement(visible = true, transformation(origin = {40, 2}, extent = {{-9.8, 0.4}, {2.8, 4.8}}, rotation = 0)));
+ PNlib.Components.IA ia1 annotation(
+ Placement(visible = true, transformation(origin = {40, -6}, extent = {{-9.8, 0.4}, {2.8, 4.8}}, rotation = 0)));
+equation
+ connect(combineLogicalOutput.logicalInput, logicalOutput) annotation(
+ Line(points = {{84, 0}, {110, 0}}, color = {53, 28, 117}));
+ connect(p11.outTransition[1], td.inPlaces[1]) annotation(
+ Line(points = {{-58, 0}, {-41, 0}}, thickness = 0.5));
+ connect(td.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-31, 0}, {-10, 0}}, thickness = 0.5));
+ connect(p1.outTransition[1], t1.inPlaces[1]) annotation(
+ Line(points = {{10, 0}, {20, 0}, {20, -30}, {-32, -30}}, thickness = 0.5));
+ connect(t1.outPlaces[1], p11.inTransition[1]) annotation(
+ Line(points = {{-40, -30}, {-86, -30}, {-86, 0}, {-78, 0}}, thickness = 0.5));
+ connect(ta1.outTransition, combineLogicalOutput.test_input) annotation(
+ Line(points = {{44, 5}, {58, 5}, {58, 2}, {62, 2}}));
+ connect(ia1.outTransition, combineLogicalOutput.inhibitor_input) annotation(
+ Line(points = {{44, -4}, {58, -4}, {58, -2}, {62, -2}}));
+ connect(p1.outTransition[2], ta1.inPlace) annotation(
+ Line(points = {{10, 0}, {22, 0}, {22, 5}, {29, 5}}));
+ connect(p1.outTransition[3], ia1.inPlace) annotation(
+ Line(points = {{10, 0}, {22, 0}, {22, -4}, {30, -4}}));
+protected
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Diagram,
+ Icon(graphics = {Rectangle(extent = {{-100, 100}, {100, -100}}), Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(rotation = 270, extent = {{91, -97}, {-91, 97}}, imageSource = 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"), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name")}));
+end Clock;
\ No newline at end of file
diff --git a/Logics/CombineLogicalOutput.mo b/Logics/CombineLogicalOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..9823ceccfb16380e9c7f84851e9a36e58b5f45b9
--- /dev/null
+++ b/Logics/CombineLogicalOutput.mo
@@ -0,0 +1,12 @@
+within PNRG.Logics;
+
+block CombineLogicalOutput
+ PNlib.Interfaces.TransitionIn test_input(t = logicalInput.t, tint = logicalInput.tint, minTokens = logicalInput.minTokens, minTokensint = logicalInput.minTokensint, enable = logicalInput.enable, fed = logicalInput.fed, decreasingFactor = logicalInput.decreasingFactor, disPlace = logicalInput.disPlace, arcType = logicalInput.arcType, speedSum = logicalInput.speedSum, tokenInOut = logicalInput.tokenInOut, fire = logicalInput.fire, disTransition = logicalInput.disTransition, active = logicalInput.active, arcWeight = logicalInput.arcWeight, arcWeightint = logicalInput.arcWeightint, instSpeed = logicalInput.instSpeed, maxSpeed = logicalInput.maxSpeed, prelimSpeed = logicalInput.prelimSpeed, testValue = logicalInput.testValue, testValueint = logicalInput.testValueint, normalArc = logicalInput.normalArc) "connector for input transitions" annotation(
+ Placement(visible = true, transformation(origin = {-2, 30}, extent = {{-114, -10}, {-98, 10}}, rotation = 0), iconTransformation(origin = {0, 20}, extent = {{-116, -10}, {-100, 10}}, rotation = 0)));
+ PNlib.Interfaces.TransitionIn inhibitor_input(t = logicalInput.t_inhibitor, tint = logicalInput.tint_inhibitor, minTokens = logicalInput.minTokens_inhibitor, minTokensint = logicalInput.minTokensint_inhibitor, enable = logicalInput.enable_inhibitor, fed = logicalInput.fed_inhibitor, decreasingFactor = logicalInput.decreasingFactor_inhibitor, disPlace = logicalInput.disPlace_inhibitor, arcType = logicalInput.arcType_inhibitor, speedSum = logicalInput.speedSum_inhibitor, tokenInOut = logicalInput.tokenInOut_inhibitor, fire = logicalInput.fire_inhibitor, disTransition = logicalInput.disTransition_inhibitor, active = logicalInput.active_inhibitor, arcWeight = logicalInput.arcWeight_inhibitor, arcWeightint = logicalInput.arcWeightint_inhibitor, instSpeed = logicalInput.instSpeed_inhibitor, maxSpeed = logicalInput.maxSpeed_inhibitor, prelimSpeed = logicalInput.prelimSpeed_inhibitor, testValue = logicalInput.testValue_inhibitor, testValueint = logicalInput.testValueint_inhibitor, normalArc = logicalInput.normalArc_inhibitor) "connector for input transitions" annotation(
+ Placement(visible = true, transformation(origin = {-2, -30}, extent = {{-114, -10}, {-98, 10}}, rotation = 0), iconTransformation(origin = {0, -20}, extent = {{-116, -10}, {-100, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ annotation(
+ Icon(graphics = {Line(origin = {3, 10}, points = {{-103, 10}, {77, 10}, {77, -10}, {103, -10}, {103, -10}}), Line(origin = {4.94721, -7.82918}, points = {{-104.947, -12.1708}, {75.0528, -12.1708}, {75.0528, 7.82918}, {105.053, 7.82918}, {103.053, 11.8292}})}));
+end CombineLogicalOutput;
\ No newline at end of file
diff --git a/Logics/InputToNOutputs.bak-mo b/Logics/InputToNOutputs.bak-mo
new file mode 100644
index 0000000000000000000000000000000000000000..ea09d1d3a473046076419c7d7227253f571508d1
--- /dev/null
+++ b/Logics/InputToNOutputs.bak-mo
@@ -0,0 +1,11 @@
+within PNRG.Logics;
+
+model InputToNOutputs
+ parameter Integer NOut "Number of Outputs" annotation(Dialog(enable = true, group = "General properties"));
+ Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.LogicalOutput logicalOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+
+end InputToNOutputs;
diff --git a/Logics/IntegerController.mo b/Logics/IntegerController.mo
new file mode 100644
index 0000000000000000000000000000000000000000..ab269e2c858f62ddb40ac8cde89dc74b9503c5fa
--- /dev/null
+++ b/Logics/IntegerController.mo
@@ -0,0 +1,56 @@
+within PNRG.Logics;
+
+model IntegerController
+ parameter Integer NStart "Start Number" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer NChange "Change of Number when active" annotation(
+ Dialog(enable = true, group = "General properties"));
+ parameter Integer NMax "Maximum Number" annotation(
+ Dialog(enable = true, group = "General properties"));
+ Integer N "Number";
+ parameter Real delay "Minimum delay between Changes" annotation(
+ Dialog(enable = true, group = "General properties"));
+ PNlib.Components.PD p1(startTokens = NStart, maxTokens = NMax, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {26, 20}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD p11(startTokens = NMax - NStart, maxTokens = NMax, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-42, 20}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TD t1(arcWeightIn = {NChange, 1}, arcWeightOut = {NChange}, delay = delay, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-8, 20}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TD t11(arcWeightIn = {NChange, 1}, arcWeightOut = {NChange}, delay = delay, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-8, -22}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-70, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {-110, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {-64, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TD t12(arcWeightIn = {1, 1}, delay = delay, nIn = 2, nOut = 0) annotation(
+ Placement(visible = true, transformation(origin = {84, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ N = p1.t;
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-3.2, 20}, {16.8, 20}}, thickness = 0.5));
+ connect(p1.outTransition[1], t11.inPlaces[1]) annotation(
+ Line(points = {{36.8, 20}, {54.8, 20}, {54.8, -22}, {-2.2, -22}}, thickness = 0.5));
+ connect(t11.outPlaces[1], p11.inTransition[1]) annotation(
+ Line(points = {{-12.8, -22}, {-59.8, -22}, {-59.8, 20}, {-51.8, 20}}, thickness = 0.5));
+ connect(p11.outTransition[1], t1.inPlaces[1]) annotation(
+ Line(points = {{-31.2, 20}, {-11.2, 20}}, thickness = 0.5));
+ connect(logicalInput, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 50}, {-81, 50}}));
+ connect(splitLogicalInput.test_output, t1.inPlaces[2]) annotation(
+ Line(points = {{-59, 52}, {-20, 52}, {-20, 20}, {-13, 20}}));
+ connect(logicalInput1, splitLogicalInput1.logicalInput) annotation(
+ Line(points = {{-110, -50}, {-75, -50}}));
+ connect(splitLogicalInput.inhibitor_output, t12.inPlaces[1]) annotation(
+ Line(points = {{-59, 48}, {64, 48}, {64, 0}, {79, 0}}));
+ connect(splitLogicalInput1.inhibitor_output, t12.inPlaces[2]) annotation(
+ Line(points = {{-53, -52}, {64, -52}, {64, 0}, {79, 0}}));
+ connect(splitLogicalInput1.test_output, t11.inPlaces[2]) annotation(
+ Line(points = {{-54, -48}, {8, -48}, {8, -22}, {-4, -22}}));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{100, 100}, {-100, -100}}), Text(origin = {-67, 57}, extent = {{-59, 43}, {59, -43}}, textString = "+"), Text(origin = {-67, -39}, extent = {{-123, 81}, {123, -81}}, textString = "-"), Text(origin = {29, 0}, extent = {{-71, 100}, {71, -100}}, textString = "N=%N")}));
+end IntegerController;
\ No newline at end of file
diff --git a/Logics/LogicalAnd.mo b/Logics/LogicalAnd.mo
new file mode 100644
index 0000000000000000000000000000000000000000..b0d09363b45a1f26e7e6ff1306d8dafc84cd6df0
--- /dev/null
+++ b/Logics/LogicalAnd.mo
@@ -0,0 +1,69 @@
+within PNRG.Logics;
+
+model LogicalAnd
+ PNlib.Components.PD p1(enablingType = PNlib.Types.EnablingType.Priority, maxTokens = 1, nIn = 1, nOut = 3) annotation(
+ Placement(visible = true, transformation(origin = {12, -40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.IA ia annotation(
+ Placement(visible = true, transformation(origin = {62.6667, -44.2857}, extent = {{-14.4667, 0.590476}, {4.13333, 7.08571}}, rotation = 0)));
+ PNlib.Components.TA ta(realTestArc = false) annotation(
+ Placement(visible = true, transformation(origin = {64.0101, -22.9437}, extent = {{-15.8101, 0.64531}, {4.51717, 7.74372}}, rotation = 0)));
+ PNlib.Components.T t3(arcWeightIn = {1, 1}, arcWeightOut = {1}, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-28, -40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t2(arcWeightIn = {1}, arcWeightOut = {1}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-28, -2}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t(arcWeightIn = {1}, arcWeightOut = {1}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-28, 24}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD p2(enablingType = PNlib.Types.EnablingType.Priority, maxTokens = 1, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {14, 8}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalOutput logicalOutput annotation(
+ Placement(visible = true, transformation(origin = {120, -20}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {-108, 32}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput2 annotation(
+ Placement(visible = true, transformation(origin = {-108, -68}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-80, 32}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {-78, -68}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t1(arcWeightIn = {1, 1}, nIn = 2) annotation(
+ Placement(visible = true, transformation(origin = {42, 8}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.CombineLogicalOutput combineLogicalOutput annotation(
+ Placement(visible = true, transformation(origin = {88, -20}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(t3.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-23.2, -40}, {0.8, -40}}, thickness = 0.5));
+ connect(logicalInput2, splitLogicalInput1.logicalInput) annotation(
+ Line(points = {{-108, -68}, {-88, -68}}));
+ connect(logicalInput1, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-108, 32}, {-90, 32}}));
+ connect(splitLogicalInput.test_output, t3.inPlaces[1]) annotation(
+ Line(points = {{-69.2, 34}, {-57.2, 34}, {-57.2, -40}, {-31.2, -40}}));
+ connect(splitLogicalInput1.test_output, t3.inPlaces[2]) annotation(
+ Line(points = {{-67.2, -66}, {-57.2, -66}, {-57.2, -40}, {-31.2, -40}}));
+ connect(splitLogicalInput1.inhibitor_output, t2.inPlaces[1]) annotation(
+ Line(points = {{-67.2, -70}, {-37.2, -70}, {-37.2, -2}, {-31.2, -2}}));
+ connect(splitLogicalInput.inhibitor_output, t.inPlaces[1]) annotation(
+ Line(points = {{-69.2, 30}, {-37.2, 30}, {-37.2, 24}, {-31.2, 24}}));
+ connect(t.outPlaces[1], p2.inTransition[1]) annotation(
+ Line(points = {{-23.2, 24}, {-9.2, 24}, {-9.2, 8}, {4.8, 8}}, thickness = 0.5));
+ connect(t2.outPlaces[1], p2.inTransition[2]) annotation(
+ Line(points = {{-23.2, -2}, {-9.2, -2}, {-9.2, 8}, {4.8, 8}}, thickness = 0.5));
+ connect(p2.outTransition[1], t1.inPlaces[1]) annotation(
+ Line(points = {{24.8, 8}, {38.8, 8}}, thickness = 0.5));
+ connect(combineLogicalOutput.logicalInput, logicalOutput) annotation(
+ Line(points = {{99, -20}, {119, -20}}, color = {53, 28, 117}));
+ connect(p1.outTransition[1], t1.inPlaces[2]) annotation(
+ Line(points = {{22.8, -40}, {30.8, -40}, {30.8, 8}, {38.8, 8}}, thickness = 0.5));
+ connect(p1.outTransition[2], ta.inPlace) annotation(
+ Line(points = {{22.8, -40}, {38.8, -40}, {38.8, -18}, {46.8, -18}}));
+ connect(p1.outTransition[3], ia.inPlace) annotation(
+ Line(points = {{22.8, -40}, {47.8, -40}}));
+ connect(ia.outTransition, combineLogicalOutput.inhibitor_input) annotation(
+ Line(points = {{68.2762, -40.4476}, {74.2762, -40.4476}, {74.2762, -22.4476}, {78.2762, -22.4476}}));
+ connect(ta.outTransition, combineLogicalOutput.test_input) annotation(
+ Line(points = {{70.1405, -18.7492}, {78.1405, -18.7492}}));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Text(origin = {7, 37}, extent = {{-109, 97}, {109, -97}}, textString = "&")}),
+ Diagram(coordinateSystem(extent = {{-140, 80}, {140, -80}})));
+end LogicalAnd;
diff --git a/Logics/LogicalExpression.mo b/Logics/LogicalExpression.mo
new file mode 100644
index 0000000000000000000000000000000000000000..a9bdfa86ea8349e6f1da42e44a09c61334551d36
--- /dev/null
+++ b/Logics/LogicalExpression.mo
@@ -0,0 +1,47 @@
+within PNRG.Logics;
+
+model LogicalExpression
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ Modelica.Blocks.Interfaces.BooleanOutput expression = false "Boolen value of expression" annotation(
+ Dialog(group = "General properties"));
+ PNRG.Interfaces.LogicalOutput logicalOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.CombineLogicalOutput[NOut] combineLogicalOutput annotation(
+ Placement(visible = true, transformation(origin = {72, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD p1(maxTokens = 1, nIn = 1, nOut = 1 + 2*NOut) annotation(
+ Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD p11(maxTokens = 1, nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-68, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TA ta1[NOut](each realTestArc = false) annotation(
+ Placement(visible = true, transformation(origin = {40, 2}, extent = {{-9.8, 0.4}, {2.8, 4.8}}, rotation = 0)));
+ PNlib.Components.IA ia1[NOut] annotation(
+ Placement(visible = true, transformation(origin = {40, -6}, extent = {{-9.8, 0.4}, {2.8, 4.8}}, rotation = 0)));
+ PNlib.Components.T t1(arcWeightIn = {if not expression then 1 else 2}, arcWeightOut = {1}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, -30}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t11(arcWeightIn = {if expression then 1 else 2}, arcWeightOut = {1}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ for i in 1:NOut loop
+ connect(combineLogicalOutput[i].logicalInput, logicalOutput[i]) annotation(
+ Line(points = {{84, 0}, {110, 0}}, color = {53, 28, 117}));
+ connect(ta1[i].outTransition, combineLogicalOutput[i].test_input) annotation(
+ Line(points = {{44, 5}, {58, 5}, {58, 2}, {62, 2}}));
+ connect(ia1[i].outTransition, combineLogicalOutput[i].inhibitor_input) annotation(
+ Line(points = {{44, -4}, {58, -4}, {58, -2}, {62, -2}}));
+ connect(p1.outTransition[2*i], ta1[i].inPlace) annotation(
+ Line(points = {{10, 0}, {22, 0}, {22, 5}, {29, 5}}));
+ connect(p1.outTransition[2*i + 1], ia1[i].inPlace) annotation(
+ Line(points = {{10, 0}, {22, 0}, {22, -4}, {30, -4}}));
+ end for;
+ connect(p11.outTransition[1], t11.inPlaces[1]) annotation(
+ Line(points = {{-58, 0}, {-41, 0}}, thickness = 0.5));
+ connect(t11.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{-31, 0}, {-10, 0}}, thickness = 0.5));
+ connect(p1.outTransition[1], t1.inPlaces[1]) annotation(
+ Line(points = {{10, 0}, {16, 0}, {16, -30}, {-31, -30}}, thickness = 0.5));
+ connect(t1.outPlaces[1], p11.inTransition[1]) annotation(
+ Line(points = {{-41, -30}, {-86, -30}, {-86, 0}, {-78, 0}}, thickness = 0.5));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Text(origin = {-45, 119}, extent = {{-53, 23}, {53, -23}}, textString = "%name"), Text(origin = {-45, -61}, extent = {{-53, 23}, {53, -23}}, textString = "%expression"), Bitmap(origin = {0, 34}, extent = {{-100, -62}, {100, 62}}, imageSource = "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")}));
+end LogicalExpression;
\ No newline at end of file
diff --git a/Logics/LogicalNot.mo b/Logics/LogicalNot.mo
new file mode 100644
index 0000000000000000000000000000000000000000..0c45da8ea0498d9c7ce15c2d8b0623cab8e325ce
--- /dev/null
+++ b/Logics/LogicalNot.mo
@@ -0,0 +1,24 @@
+within PNRG.Logics;
+
+model LogicalNot
+ PNRG.Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalOutput logicalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-70, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ CombineLogicalOutput combineLogicalOutput annotation(
+ Placement(visible = true, transformation(origin = {70, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(logicalInput, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 0}, {-80, 0}}));
+ connect(combineLogicalOutput.logicalInput, logicalOutput) annotation(
+ Line(points = {{82, 0}, {110, 0}}, color = {53, 28, 117}));
+ connect(splitLogicalInput.test_output, combineLogicalOutput.inhibitor_input) annotation(
+ Line(points = {{-60, 2}, {4, 2}, {4, -2}, {60, -2}}));
+ connect(splitLogicalInput.inhibitor_output, combineLogicalOutput.test_input) annotation(
+ Line(points = {{-60, -2}, {-4, -2}, {-4, 2}, {60, 2}}));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Polygon(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, lineThickness = 2.25, points = {{-80, 75}, {20, 0}, {-80, -75}, {-80, 75}}), Ellipse(origin = {47, 1}, lineThickness = 2.25, extent = {{25, 25}, {-25, -25}})}));
+end LogicalNot;
\ No newline at end of file
diff --git a/Logics/LogicalOr.mo b/Logics/LogicalOr.mo
new file mode 100644
index 0000000000000000000000000000000000000000..92d2f3654310807528305d30cb0f3d903daf8958
--- /dev/null
+++ b/Logics/LogicalOr.mo
@@ -0,0 +1,61 @@
+within PNRG.Logics;
+
+model LogicalOr
+ PNlib.Components.T t13(arcWeightIn = {1}, arcWeightOut = {1}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-28, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t16(arcWeightIn = {1, 1, 1}, nIn = 3) annotation(
+ Placement(visible = true, transformation(origin = {42, 48}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t12(arcWeightIn = {1}, arcWeightOut = {1}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-28, -30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD p14(enablingType = PNlib.Types.EnablingType.Priority, maxTokens = 1, nIn = 2, nOut = 3) annotation(
+ Placement(visible = true, transformation(origin = {14, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.IA ia12 annotation(
+ Placement(visible = true, transformation(origin = {66.6667, -18.2857}, extent = {{-14.4667, 0.590476}, {4.13333, 7.08571}}, rotation = 0)));
+ PNlib.Components.TA ta1(realTestArc = false) annotation(
+ Placement(visible = true, transformation(origin = {66.0101, 5.0563}, extent = {{-15.8101, 0.64531}, {4.51717, 7.74372}}, rotation = 0)));
+ CombineLogicalOutput combineLogicalOutput annotation(
+ Placement(visible = true, transformation(origin = {88, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-80, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ SplitLogicalInput splitLogicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {-80, -30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {-110, -30}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, -30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalOutput logicalOutput annotation(
+ Placement(visible = true, transformation(origin = {118, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(t12.outPlaces[1], p14.inTransition[2]) annotation(
+ Line(points = {{-23, -30}, {-11.4, -30}, {-11.4, 0}, {2.8, 0}}, thickness = 0.5));
+ connect(t13.outPlaces[1], p14.inTransition[1]) annotation(
+ Line(points = {{-23, 30}, {-11.2, 30}, {-11.2, 0}, {3, 0}}, thickness = 0.5));
+ connect(p14.outTransition[3], ia12.inPlace) annotation(
+ Line(points = {{24.8, 0}, {38.8, 0}, {38.8, -14}, {51, -14}}));
+ connect(p14.outTransition[2], ta1.inPlace) annotation(
+ Line(points = {{24.8, 0}, {38.8, 0}, {38.8, 9}, {49.8, 9}}));
+ connect(p14.outTransition[1], t16.inPlaces[1]) annotation(
+ Line(points = {{24.8, 0}, {31.8, 0}, {31.8, 48}, {36.8, 48}}, thickness = 0.5));
+ connect(logicalInput, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 30}, {-90, 30}}));
+ connect(logicalInput1, splitLogicalInput1.logicalInput) annotation(
+ Line(points = {{-110, -30}, {-90, -30}}));
+ connect(combineLogicalOutput.logicalInput, logicalOutput) annotation(
+ Line(points = {{100, 0}, {118, 0}}, color = {53, 28, 117}));
+ connect(ta1.outTransition, combineLogicalOutput.test_input) annotation(
+ Line(points = {{72, 10}, {78, 10}, {78, 2}}));
+ connect(ia12.outTransition, combineLogicalOutput.inhibitor_input) annotation(
+ Line(points = {{72, -14}, {78, -14}, {78, -2}}));
+ connect(splitLogicalInput1.test_output, t12.inPlaces[1]) annotation(
+ Line(points = {{-70, -28}, {-32, -28}, {-32, -30}}));
+ connect(splitLogicalInput.test_output, t13.inPlaces[1]) annotation(
+ Line(points = {{-70, 32}, {-32, 32}, {-32, 30}}));
+ connect(splitLogicalInput.inhibitor_output, t16.inPlaces[2]) annotation(
+ Line(points = {{-70, 28}, {-52, 28}, {-52, 48}, {38, 48}}));
+ connect(splitLogicalInput1.inhibitor_output, t16.inPlaces[3]) annotation(
+ Line(points = {{-70, -32}, {-52, -32}, {-52, 48}, {38, 48}}));
+protected
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Text(origin = {0, 46}, extent = {{-100, 70}, {100, -70}}, textString = ">=1")}));
+end LogicalOr;
\ No newline at end of file
diff --git a/Logics/MultiplyLogicSignal.bak-mo b/Logics/MultiplyLogicSignal.bak-mo
new file mode 100644
index 0000000000000000000000000000000000000000..5a75a0dc2f0cc0d686635d8511d5ebe13df49a9e
--- /dev/null
+++ b/Logics/MultiplyLogicSignal.bak-mo
@@ -0,0 +1,6 @@
+within PNRG.Logics;
+
+model MultiplyLogicSignal
+equation
+
+end MultiplyLogicSignal;
diff --git a/Logics/NLogicalAnd.mo b/Logics/NLogicalAnd.mo
new file mode 100644
index 0000000000000000000000000000000000000000..194cc4760ace31d683c6c947e55b2f7515cc536d
--- /dev/null
+++ b/Logics/NLogicalAnd.mo
@@ -0,0 +1,23 @@
+within PNRG.Logics;
+
+model NLogicalAnd
+ parameter Integer NIn "Number of Inputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ Interfaces.LogicalInput logicalInput[NIn] annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.LogicalOutput logicalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ LogicalAnd logicalAnd[NIn - 1] annotation(
+ Placement(visible = true, transformation(origin = {-2, -2}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(logicalInput[1], logicalAnd[1].logicalInput1);
+ connect(logicalInput[2], logicalAnd[1].logicalInput);
+ if NIn > 2 then
+ for i in 3:NIn loop
+ connect(logicalAnd[i - 2].logicalOutput, logicalAnd[i - 1].logicalInput1);
+ connect(logicalInput[i], logicalAnd[i - 1].logicalInput);
+ end for;
+ end if;
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Text(origin = {-7, 45}, extent = {{-109, 97}, {109, -97}}, textString = "N&")}));
+end NLogicalAnd;
diff --git a/Logics/SplitLogicalInput.mo b/Logics/SplitLogicalInput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..f92b7ea7d98ca49d5de80b756409e24b8580b0ae
--- /dev/null
+++ b/Logics/SplitLogicalInput.mo
@@ -0,0 +1,14 @@
+within PNRG.Logics;
+
+block SplitLogicalInput
+ PNlib.Interfaces.PlaceOut test_output(t = logicalInput.t, tint = logicalInput.tint, minTokens = logicalInput.minTokens, minTokensint = logicalInput.minTokensint, enable = logicalInput.enable, fed = logicalInput.fed, decreasingFactor = logicalInput.decreasingFactor, disPlace = logicalInput.disPlace, arcType = logicalInput.arcType, speedSum = logicalInput.speedSum, tokenInOut = logicalInput.tokenInOut, fire = logicalInput.fire, disTransition = logicalInput.disTransition, active = logicalInput.active, arcWeight = logicalInput.arcWeight, arcWeightint = logicalInput.arcWeightint, instSpeed = logicalInput.instSpeed, maxSpeed = logicalInput.maxSpeed, prelimSpeed = logicalInput.prelimSpeed, testValue = logicalInput.testValue, testValueint = logicalInput.testValueint, normalArc = logicalInput.normalArc) "connector for input transitions" annotation(
+ Placement(visible = true, transformation(origin = {198, 10}, extent = {{-114, -10}, {-98, 10}}, rotation = 0), iconTransformation(origin = {0, 20}, extent = {{100, -10}, {116, 10}}, rotation = 0)));
+ PNlib.Interfaces.PlaceOut inhibitor_output(t = logicalInput.t_inhibitor, tint = logicalInput.tint_inhibitor, minTokens = logicalInput.minTokens_inhibitor, minTokensint = logicalInput.minTokensint_inhibitor, enable = logicalInput.enable_inhibitor, fed = logicalInput.fed_inhibitor, decreasingFactor = logicalInput.decreasingFactor_inhibitor, disPlace = logicalInput.disPlace_inhibitor, arcType = logicalInput.arcType_inhibitor, speedSum = logicalInput.speedSum_inhibitor, tokenInOut = logicalInput.tokenInOut_inhibitor, fire = logicalInput.fire_inhibitor, disTransition = logicalInput.disTransition_inhibitor, active = logicalInput.active_inhibitor, arcWeight = logicalInput.arcWeight_inhibitor, arcWeightint = logicalInput.arcWeightint_inhibitor, instSpeed = logicalInput.instSpeed_inhibitor, maxSpeed = logicalInput.maxSpeed_inhibitor, prelimSpeed = logicalInput.prelimSpeed_inhibitor, testValue = logicalInput.testValue_inhibitor, testValueint = logicalInput.testValueint_inhibitor, normalArc = logicalInput.normalArc_inhibitor) "connector for input transitions" annotation(
+ Placement(visible = true, transformation(origin = {198, -30}, extent = {{-114, -10}, {-98, 10}}, rotation = 0), iconTransformation(origin = {0, -20}, extent = {{100, -10}, {116, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalOutput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Diagram(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}})}),
+ Icon(graphics = {Line(origin = {-4.62826, 8.01988}, rotation = 180, points = {{-104.947, -12.1708}, {75.0528, -12.1708}, {75.0528, 7.82918}, {105.053, 7.82918}, {103.053, 11.8292}}), Line(origin = {-2.9434, -9.81131}, rotation = 180, points = {{-103, 10}, {77, 10}, {77, -10}, {103, -10}, {103, -10}})}));
+end SplitLogicalInput;
\ No newline at end of file
diff --git a/Logics/package.mo b/Logics/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..99ccaa9610cb34b4ac11ff964acefed19d1f8dda
--- /dev/null
+++ b/Logics/package.mo
@@ -0,0 +1,23 @@
+within PNRG;
+
+package Logics
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ annotation(
+ Icon(graphics = {Bitmap(extent = {{-86, -86}, {86, 86}}, imageSource = 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")}));
+end Logics;
diff --git a/Logics/package.order b/Logics/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..afd86ddcbf93cedc0ec2634ff3512b3f7b1961ac
--- /dev/null
+++ b/Logics/package.order
@@ -0,0 +1,9 @@
+SplitLogicalInput
+LogicalOr
+LogicalNot
+LogicalAnd
+CombineLogicalOutput
+Clock
+LogicalExpression
+NLogicalAnd
+IntegerController
diff --git a/PowerPlants/HydrogenCHPPlant.mo b/PowerPlants/HydrogenCHPPlant.mo
new file mode 100644
index 0000000000000000000000000000000000000000..1428c2086d936d3a6c4a2a18a3273a468c0522fd
--- /dev/null
+++ b/PowerPlants/HydrogenCHPPlant.mo
@@ -0,0 +1,70 @@
+within PNRG.PowerPlants;
+
+model HydrogenCHPPlant
+ PNlib.Components.TC CHP(arcWeightIn = {1.1, 8, 1}, arcWeightOut = {0.5*39.4*0.7, 0.45*39.4*0.7, 9.1}, maximumSpeed = 1, nIn = 3, nOut = 3) annotation(
+ Placement(visible = true, transformation(origin = {0, -16}, extent = {{-10, -10}, {10, 10}}, rotation = 90)));
+ PNRG.Interfaces.LogicalInput activation annotation(
+ Placement(visible = true, transformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.HydrogenInput H2In annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.OxygenInput O2In annotation(
+ Placement(visible = true, transformation(origin = {-110, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-78, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.WaterOutput WaterOut annotation(
+ Placement(visible = true, transformation(origin = {110, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.HeatOutput heatOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.ElectricalOutput electricalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t1(arcWeightIn = {2}, nIn = 1) annotation(
+ Placement(visible = true, transformation(origin = {-48, 44}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {80, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator energeticTransitionWithoutActivator(arcWeightOut = {energeticTransitionWithoutActivator.power}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-68, -80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace energeticFlowPlace(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {80, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator energeticTransitionWithoutActivator1(arcWeightOut = {energeticTransitionWithoutActivator1.power}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-68, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace energeticFlowPlace1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {80, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p11 annotation(
+ Placement(visible = true, transformation(origin = {-30, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p12 annotation(
+ Placement(visible = true, transformation(origin = {-30, -80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(activation, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 60}, {-89, 60}}));
+ connect(splitLogicalInput.test_output, CHP.inPlaces[3]) annotation(
+ Line(points = {{-67, 62}, {-28, 62}, {-28, -34}, {0, -34}, {0, -21}}));
+ connect(splitLogicalInput.inhibitor_output, t1.inPlaces[1]) annotation(
+ Line(points = {{-68, 58}, {-62, 58}, {-62, 44}, {-52, 44}}));
+ connect(H2In, energeticTransitionWithoutActivator1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-84, 0}, {-84, -50}, {-72, -50}}));
+ connect(O2In, energeticTransitionWithoutActivator.inPlaces[1]) annotation(
+ Line(points = {{-110, -60}, {-86, -60}, {-86, -80}, {-72, -80}}));
+ connect(energeticFlowPlace1.outTransition[1], electricalOutput) annotation(
+ Line(points = {{90, 60}, {110, 60}}));
+ connect(energeticFlowPlace.outTransition[1], heatOutput) annotation(
+ Line(points = {{90, 0}, {110, 0}}));
+ connect(p1.outTransition[1], WaterOut) annotation(
+ Line(points = {{90, -60}, {110, -60}}));
+ connect(CHP.outPlaces[1], energeticFlowPlace1.inTransition[1]) annotation(
+ Line(points = {{0, -12}, {0, 60}, {70, 60}}, thickness = 0.5));
+ connect(CHP.outPlaces[3], p1.inTransition[1]) annotation(
+ Line(points = {{0, -12}, {0, -6}, {60, -6}, {60, -60}, {70, -60}}, thickness = 0.5));
+ connect(CHP.outPlaces[2], energeticFlowPlace.inTransition[1]) annotation(
+ Line(points = {{0, -12}, {0, 0}, {70, 0}}, thickness = 0.5));
+ connect(energeticTransitionWithoutActivator1.outPlaces[1], p11.inTransition[1]) annotation(
+ Line(points = {{-64, -50}, {-40, -50}}, thickness = 0.5));
+ connect(energeticTransitionWithoutActivator.outPlaces[1], p12.inTransition[1]) annotation(
+ Line(points = {{-64, -80}, {-40, -80}}, thickness = 0.5));
+ connect(p11.outTransition[1], CHP.inPlaces[1]) annotation(
+ Line(points = {{-20, -50}, {0, -50}, {0, -20}}, thickness = 0.5));
+ connect(p12.outTransition[1], CHP.inPlaces[2]) annotation(
+ Line(points = {{-20, -80}, {0, -80}, {0, -20}}, thickness = 0.5));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Ellipse(origin = {-80, 60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, 0}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, 60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, -60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, 0}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, 60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, 60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, -60}, extent = {{-20, 20}, {20, -20}}), Bitmap(origin = {0, 60}, extent = {{-40, -38}, {40, 38}}, imageSource = "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VGp1oyvMHEIj2l+OwdAdS/nCXM4ByLN7VY/GemfwvxPLakdddmX9cr5wDAJ7TjfWxuwFcXHJTIJ9rTB4qZwFFLN7VY/GemfzHwOS1I+/789J9JxbLWQDgqWZjYoF74f+65GZAnse73jPD4l09Fu/OWpFdLq8bhZL9zaELs3vJmQDgmcMW2Lu5F/15xZsABRPvenfE4l09Fu/O3HX6srxuFFL2/Ob+k3vJuQDgifHxNbu6F/s3iy9+Cine9e6Mxbt6LN7Ta6qsJa8ZhZg9e4Vat7ucDwBDbvXI6p3ci/zTxRc9BRnvek+Lxbt6LN7TcwvXl+Q1o2D7/KqRqZ3ljAAYYqky7yt5sVOg8a739Fi8q8fivWOJsqm8XhR8p7vRmCdnBcAQcg/AN5a8yCn0eNd7h1i8q8fivWOpMl+U14soUeZtclYADBl3g7fyxU2Ux7veO8biXT0W73I6ntDu+myV14soL1HZq+TMABgS+YNPc4On6eJd71Is3tVj8S6XKPsFea2Its/NyHFybgDUXBJnj3cv4M3yBU20fbzrXY7Fu3os3kVpbBPNmyHUuS3u9fMEOT8AaqrZMMvcC/fGkhczUTHe9S5g8a4ei3eRuy6fl9eJaAfd1BqdaMoZAlAzK9S6yL1gLyl5EROVF5mvyTkKHYt39Vi87yxfojTvdtOsspfqxvpYzhKAmjh4YbZH/k1YxRcv0fQlDXOYnKeQsXhXj8X7znRs/ldeI6LOZT/Nv3FazhOAgZuanyrz2eKLlmgG8a73nbB4V4/F+5/aP/7Hu900p/JfyM2/BE/OFYABcovTm+SLlWg28a73P7F4V4/F+5/c/flz8voQzSY+4xuoEa2yY+SLlGjW8a73P7B4V4/F+3ZJY/1Szbvd1IX4mEGgBnRkH+JekJvkC5RoLvGu9+1YvKvH4n07fgSQutjmRE08Ss4YgD5pNiYOdC/Ea0penERzi3e9t2Hxrh6LN+92U0+6vqWyg+WsAegxt3QvcC/A35e8KIkqxbveLN7diMU7/5ZK8xl5XYi60J/0mFkk5w1Aj4yPr9k1jbLvlLwYiarHu94s3l0o9MXbvY4O0bzbTb3rh6vGpnaTcwegB7Sy7y55ERJ1rdDf9Wbxrl7wi7cyn5bXhKibJcq+V84dgC7TsX2ufPERdb3A3/Vm8a5eyIt3Gk0+UPNuN/WhVJkXyPkD0CXtX9T5u3zhEfWikN/1ZvGuXsiLt1bZp+T1IOpRN6exTeQMAqhohVq3t3uB/aHkRUfUm2LzdTmHoWDxrl6oi3f+iRPuz79FXg+iHvan/AMX5CwCmLOp+VrZL5W82Ih6WnN0cqWcxhCweFcv1MXb/dk/Ka8FUc/b9kbJ1Hw5jwDmIFV2qvAiI+pHgb7rzeJdvRAX76aafIDm3W4aVHH2OjmTAGYpUeZIzY2cBliI73qzeFcvxMXb/Zk/Ia8DUR/bmsb2KDmXAGYoXZzt515IV5e8uIj6V4DverN4Vy+0xTuJs4M0b5LQ4Ls2jdbfR84ngA7yD8bXyp5f8qIi6nuhvevN4l290BZv9xfUj8trQDSgfn7wwmwPOaMApqEjc1rJi4loMAX2rjeLd/VCWrz16OQSzbvdVKci81E5pwB2QCt7bOFFRDTgQnrXm8W7ekEt3rH9mPzzEw06dx97qZxVAEJrdOL+mi/JoToW0LveLN7VC2Xxbt+zebeb6tjN+e8eyJkF0DY+vmbXRJkLSl48RLUolHe9WbyrF8rinf+TvvyzE9Won+e/MybnFsDIti9eOKnkRUNUnwJ515vFu3ohLN68201D0ilydoHguRfGI11bS14wRLUqhHe9WbyrF8LirSN7hvxzE9WwrWlkHiPnFwjWoQuze7kXxmUlLxaiOvYNOcO+YfGunu+L9/LF9n6ad7tpeLqiuXjtvnKOgSAlyn6h5EVCVNvS2Bwu59gnLN7V833xTpX5iPwzE9W6yHzNje48OctAUNzNe03hxUFU/7x+15vFu3o+L97NxsSB7s+4Wf6ZiWpfbDI5z0Aw2l8xfHPhhUE0BPn8rjeLd/V8Xrzdn+/D8s9LNCTd2myYZXKmAe/d/pXw5mclLwqiYcnbd71ZvKvn6+KdNOx9Ne9203B3IV8pj+Akyryt5MVANFT5+q43i3f1fF283Z/tQ/LPSjSEvUfONuAtt3QfqfnoQBr+zkuUTeV8+4DFu3reLt6N7KHuz/dD+eclGrbS2B4l5xvwziFja++ulb1UvgCIhqhfJ5F9spxtn7B4V8/XxfsO7j7+RPfn/KX8cxMNUZetUOv2lrMNeEVH5rSS4Scahv6UL6SrR1bvJOfaNyze1fN98c7lr4U0ss929/U/yj8/0TDkXqcflHMNeCONJx8hh55oCLoyicy68fE1u8qZ9hWLd/VCWLzvsGTJ1F0SZY9zC/hf5XUgqntplD1azjQw9A5bYO+WKnORHHiiGvd313/msyvn2Xcs3tULafG+Q/5JEYnKXuX+/DfJ60FU4/7c3H9yLznPwFDTkXlnybAT1TK3NH1i6b4Ti+Uch4LFu3ohLt53aDYmR909/6PymhDVOD7lBP5wA32Ea0vJoBPVrZ+3IvtgOcOhYfGuXsiL9x30tnt/9lN5bYhq2NZk1D5MzjAwdFaodbu7B9BvS4acqE5dnSqzZtXI1M5yhkPE4l09Fu/btX8B80XumvxNXiOiWhWZPy7ZZ+qucoaBoZJE9s2F4SaqT/k38L0rjTbeU85uyFi8q8fifWcrRzfcw12Tt7trc5u8VkQ16hQ5u8DQaEV2uearhammJcpcoOMJLecWLN7diMW7XGt0oumuz0/k9SKqSfmPxR4h5xaovfyj1zRfrkD17JY0zv692Tx2Fzm3uB2Ld/VYvHcs/5Eud402uG6W141o0OU/Hpv/mKycW6DWksi+Vg4z0aBLo+w7ScPeV84r7ozFu3os3p0tUxvG3bU6S147okGX/5isnFegtloqO9gN7iY5yEQD7Dod25e48Zwn5xVFLN7VY/GeOa2yY9w1u0ZeQ6IBtjmNJh8oZxWoJa3s2SVDTDSoPq8b62M5p9gxFu/qsXjPTnPx2n3ddfu0vI5EAys23x3hzRrUnVbZMwvDSzSYrs8XSDmj6IzFu3os3nPjlp3nuOt3vbyeRIMojeyz5YwCtdEaX7OnVvZSObhE/S5V5pxmbA6QM4qZYfGuHov33KWLs/20yr4nrylR34vMXw8ZW3t3OaNALaSxeUthaIn6222pslN8EU41LN7VY/GuJv/iHR2bV2h+X4gGXKLM2+R8AgOnRyeXaG6QNNh+n392vJxNzB6Ld/VYvLsj/6x9rexv5PUl6mObdWQOkbMJDJQbzDNLhpWoP0XZ+w9bYO8m5xJzw+JdPRbv7jl4YbZHGptT5TUm6lv8oiXqxD2kn1YYUqI+5JabG/L5kzOJali8q8fi3X1u+Xmq5hcvaVDF5jlyJoG+y99ldAP5l8KAEvW+X7dGJ+4vZxLVsXhXj8W7N5qNiQPd9f2FvN5EfehyftESA+cG8cSS4STqbbH92JJ9pu4q5xHdweJdPRbv3sl/9MRd4w/La07U69zr+u1yHoG+Wb7Y3k/zC5XU3/J5e7mcRXQXi3f1WLx7L/82Wnetb5XXnqiH8YuWGBw3gN8sGUqiXnWJbkweKucQ3cfiXT0W7/7IP/UkVeYief2Jetbtv2gJ9Fca26MKw0jUu848dGF2LzmH6A0W7+qxePdPGm28p+aNIOpjiTJPknMI9Ez+5SRu8H4tB5GoFyXKvrfZPHYXOYfoHRbv6rF491f7ufQueQ5Evcn+hucS+sYN3LHFISTqept1bDI5f+g9Fu/qsXgPhrv2L9f5vaPkTIi6WmxfIucP6Lrbf5vcXloYQKIuln8+t25kj5Xzh/5g8a4ei/fgJGriUe4MrpNnQtTlLueL29BzOs5eWTJ8RN3sTy2VHSxnD/3D4l09Fu/B0qOTS9w5/EGeC1FXi+yr5ewBXbN00cZ9NN8aRj0sVeYcPWYWydlDf7F4V4/Fe/DyZ1YaZd+RZ0PUxW5sLl67r5w9oCvyD44vGTqibvXpVWNTu8m5Q/+xeFePxbsexsfX7OrO45PyfIi62Lvk3AGVLVMbxjVfVEA9Kv/kktUjq3eSc4fBYPGuHot3nUzNT2Nzqjwjoi51W/6FgnLqgEryr+guGTaibnSCnDcMFot39Vi860dH5jXynIi6UarMZ+W8AXOWKJu6wdoqB42oYluTyKyT84bBY/GuHot3Pbnn2XHufLbI8yKqWhqbw+W8AXPiBupMOWBEFbstieyz5KyhHli8q8fiXV9JnB2t+dFJ6n4/kLMGzFoaZY8uGS6iKt2Uz5WcNdQHi3f1WLzrLRm1D9v2fQElZ0c01/gqeVQ1zw3ST+RgEc21/EHHP8fVH4t39Vi86y9pmMM0H5FLXcwt3he40ZonZw2YkSS2j5NDRTTX8qW7OTq5Us4Z6ofFu3os3sMhjbIVmm+5pK5mnyjnDJgRN0DnFQeKaE5dn7+7JGcM9cTiXT0W7+HRiuxyd2bXyjMkmlv2/BHe9cZsJcocWRwmojnF0j1kWLyrx+I9XJoqa2mWb+pS+U8MyBkDpuUG5wdykIjm0PW6MXmonC/UG4t39Vi8h0/7o3OvkWdJNPvsj+R8ATuU/7Z3cYiIZt11LN3DicW7eizew0nHE9qd39XyPIlmG5/ehRlzw/IdOUBEs+xGlu7hxeJdPRbv4ZW/881HDVIXOlfOFlDQiuyDS4aHaDbdmsaTj5CzheHB4l09Fu/hphvZQ9053iLPlWiWPVLOFnAnmm+ppGpt0bF5qpwrDBcW7+qxeA+//GPh3FlulmdLNIu+L+cK+If8i01KhoZoxqWRfZGcKwwfFu/qsXj7IT9Hd55b5fkSzbT89+bkXAHb6Mh8TQ4M0YyLzSvkTGE4sXhXj8XbH4myRp4v0UzLf29OzhRwx7d3FQaGaEbF9q1ypjC8WLyrx+LtF62y18szJppp+e/PyZlC4NxgfF4OCtFMckva/4zwLV1eYfGuHou3fxJl3yvPmWiGfVnOEwKWNOx9df5LccVBIerUN5vNY3eRM4XhxuJdPRZv/6wamdqZH8mkOba1NTpxfzlTCJS7kbyzZEiIOnXhytEN95DzhOHH4l09Fm8/Nfef3Mud7y/leRN1zr5bzhMCtEKt29sNxE3FASGatr8tUxvG5TzBDyze1WPx9le6ONvPnfHl8syJOvT3pYs27iPnCYFxg7ChZDiIpusWHU88SM4S/MHiXT0Wb7+1P5DgZnnuRNMWZ6+Us4SA5D+b6wbhksJgEE1TGpl/k7MEv7B4V4/F239JnB2t+Yxvml2XjY+v2VXOEgKhlX1GyVAQ7bAksq+VcwT/sHhXj8U7DPk7mPLsiaYtNs+Rc4RAuAE4rzAQRDvILRKfGOFjA4PA4l09Fu9w6MieIc+faEclylwgZwgB4OvhaTYlkfnVkn2m7irnCH5i8a4ei3c4Dl6Y7eHO/OdyBoh2WCN7qJwjeE6r7FOFQSAq7/pmY+JAOUPwF4t39Vi8w5JG6+/jzv1aOQdEZaXKfFHOEDymRzfs7w5+sxwEopK2JpF9spwh+I3Fu3os3uFJ4uzxml+2pJm1lTe0AuIO/KSSISAqFpk3yfmB/1i8q8fiHSYdZ6+Ts0BUVhqbU+X8wEPtb926Tg4AUUln5V+RLGcI/mPxrh6Ld6im5vO18jTDbuLbnwOgY/uSksMnkv2ltd/xC+X8IAws3tVj8Q5XGm28p5uBP8mZIJKlyqyR8wPPaGXPlwdPJLqtOTq5Us4OwsHiXT0W77C1IrvczcEmORdEop/J2YFH0tgmJYdOdKcSlb1Kzg7CwuJdPRZvpHH273IuiGT5X9Lk7MAT+Q/yywMnulOx+e7qkdU7ydlBWFi8q8fijW0/763MWXI2iLYvVeZ9cnLggfYH/PNLlTRd1y4f2zAmZwfhYfGuHos3crqxPnbzcLWcD6I7cveKG1rja/aUs4Mhp2P7XHnYRNvnXvxPl3ODMLF4V4/FG3fIvwtBzgfR9iVR9kI5Nxhy7mC/Lw+aaLtOlzODcLF4V4/FG9tLlH2vnBGif2Z/JGcGQ0yPTi4pHjLRP/o9/8yF7bF4V4/FG9tr/7jnhXJOiO6o2TDL5NxgSOnYvlUeMFG7zWmUrZAzg7CxeFePxRuSjie0m43b5KwQ5bn77n/LmcEQGh9fs6s70L/JAybKSyL7ZjkzAIt39Vi8USZR5g1yVojaXZf/y4icGQwZ9wB9WsnhEuWLwW9XqHW7y5kBWLyrx+KNMqvGpnbT/MgJ7aD83itnBkPG/e36W/JgiVxbXEfIeQFyLN7VY/HGjujG5KH69ntwYW4o+L4v5wVDpBmbA9whbi05WAq9yLxTzgtwBxbv6rF4YzpuRk6SM0OUl0br7yPnBUNCx+YV8kCJUmUuOmyBvZucF+AOLN7VY/HGdNqfcvI7OTdEicpeJecFQ8Id4C/lgRKl8eQj5KwA22Pxrh6LNzpxC9Yqzb9KU7EL5axgCLRUdnDJYVLoReY0OSuAxOJdPRZvzISblXfJ2SHiM72HkDu4E+RBUvBd1WxMLJCzAkgs3tVj8cZMrFDr9nbzcqWcHwq+E+WsoN7muUP7Q8lBUsjF5sVyUIAyLN7VY/HGTKXKvEDODwXfxW405slZQU21Iru85BAp5GLz45GRqflyVoAyLN7VY/HGLMxzy/c5coYo8OKJB8lBQU1pZU8uHCCF3Ba+Fh6zweJdPRZvzEZrdKKp+Wxv2i6+Qn5IrB5ZvZNbvC+VB0gBF2Xvl3MCTIfFu3os3pitfNGSc0RBd2Wzeewuck5QMzqyDyk5PAq3a5Yu2riPnBNgOize1WPxxmzxi5YkS5Q5Us4JasYd1HvkwVG4JcoeJ2cE6GTJPlN3XT62YYzmXn4N5XUFOuEXLUl0upwR1MiSJVN3cYd0VcnBUZj9PP/RIzknAIC6mpqvVfbTkvs5hdn1K9S63eWUoCZ0I3tsyaFRoKWReYycEQBAveXfLizv5xRwUfYUOSOoCXdAHyocGIXaWXI+AADDQcfm6yX3dQqxyHxUzgdqYNXI1M7ugK4uHBiF2NamylpyRgAAwyH/ynDNxwvS7V3Lp5vUUKKyVSWHRSEWm4/L+QAADBfNv2LTHTWyh8r5wIAlkX1z4aAoxDYtUxvG5XwAAIZL/uk47p5+S8l9noLLniznAwPmDub/igdFAXaKnA0AwHDSkXlTyX2eQisyf5SzgQFqNiYOLBwShdj1hy7M7iXnAwAwnNpfqnNtyf2eAqupJh8g5wMDkihr5AFRgMXZ6+RsAACGm47sqwv3ewquJDLHy9nAgGhlz5YHRMF1Y7MxsUDOBgBguLXf9b6u5L5PAZUqc46cDQxAGm28pzuQzfKAKLhOlLMBAPBD/i+aJfd9CqsteswskrOBPtMqe2bJ4VBY3dTa7/iFcjYAAH7I32RLVXZDyf2fAsrNwPPlbKDPdGw/Jg+GAiu2b5VzAQDwS6LMGwr3fwqryHxOzgX6KP8mI81vO4fezUtj25CzAQDwy9JFG/dx9/wbS54DFE43rVDrdpezgT7Jv8mo5FAooFKVvV3OBQDAT3yuN7nd77FyLtAn7gBOKhwIhdSturE+lnMBAPBT/st1mm+zDLsoe4ecC/SJO4CfFw6EQup0ORMAAL/pyJxW8jygcPq1nAn0Qf4pFu7iby05EAqkZsMsk3MBAPBbS2UHa57/IbeV3+0agCTOji45DAome7acCQBAGHRsvl58LlA4Zc+UM4Eecxf+XcWDoFBKY3uUnAkAQBgSZY6UzwUKqCh7v5wJ9Fiqst8WDoJC6XcjI1Pz5UwAAIIxzz0Lfl3yfKAASpW5SA4Eeij/JAt5CBRS2cvkTAAAwpJE2QuLzwcKpWZsDpAzgR5JI/tseQAUTNcctsDeTc4EACAs+RepuGfClSXPCQqi7Bg5E+gRHWcfKB4AhVAS2TfLeQAAhMk9F06QzwkKpMieIecBPeIu+MWFA6AQ2po07H3lPAAAwpT/uEH+bCh5XpDvReavbgTmyZlAly1TG8YLF58CiY8QBADcmXs+fKP4vKAQSuLsIDkP6DK3fB0rLzyFURLZZ8l5AACETcfmqfJ5QaHEhy30nI7MR4sXngLo6vwXaeQ8AADCNj6+ZlfNL1kGWarMZ+U8oLvm5T/TIy88+V+qsrfLYQAAIJf/4r18blAQXT3Cz3n3TkuZe5dcdAqgNJp8oJwHAAByzcbEgZpfsgyy/OzlPKBLtLLPkBecguhcOQsAAGzPPSu+XfL8IM/Lv9tFzgK6JFHmbfKCUwjZY+UsAACwPR2b5xSfH+R9kXmnnAV0SarMOYULTr5368rRDfeQswAAwPZa42v2dM+Mm0ueI+RzsfmxnAV0wZIlU3dxF/iWwgUnv4vN/8pZAACgjFbZpwrPEfK9TXzqWQ8kyqYlF5s8L4nN0+QsAABQJonsk+VzhPyvOTq5Us4CKnKL93HyQpP33XjwwmwPOQsAAJRZNTa1m3t2XFfyPCGfi+2EnAVU5C7s6YULTX4X2TPkHAAAMJ0kNv9TeJ6Q38Xm43IOUJFW9jeFC01el8TZ4+UcAAAwHff8eKR8npDfpcpcJOcAFaxQ6/bWfDB+aF2T/0KtnAUAAKazamRqZ/cMuaLkuUI+N2YWyVnAHKXx5CMKF5i8zv3t9X1yDgAAmIn8s53lc4X8jn8l7yJ3Qf9DXmDyuzQyj5FzAADATGh+3CS84ux1cg4wR4myXyhcYPK5m/hMTgDAXI2Pr9nVPUuuL3m+kL99U84B5khH5q8lF5h8LTKfkzMAAMBsuOfJJwvPF/K5v8kZwBwsXbRxn5KLSx6XKvMCOQcAAMxGGtlny+cL+V1z8dp95RxgllqRfbC8sOR1W3nhAACqajYmFrhnyuaS5wz5WiN7qJwDzBLfWBlc58oZAABgLtIo+07Jc4Y8LYnMOjkDmKU0NqfKC0seF2evlDMAAMBcJMqawnOGvI2PIu4C/rYaWJE5RM4AAABz0WxMHFh4zpC3ucX7HDkDmCV3Ia+WF5a87S/y/AEAqMI9W/5U8rwhD0tVdoM78nlyBjBDK9S6SF5U8roPyRkAAKAKHZnTSp435GujG/aXM4AZ4qviwyqJzfPkDAAAUEUS2WfJ5w15XCN7rJwBzFAaZ+sLF5S8LV2c7SdnAACAKvjX88CK7EY5A5ghHWXvL1xQ8rU/yPMHAKAb3DPmwpLnDvnZh+X5Y4bcxTu35IKSh/ERQACAXtGRead87pCfJcpcIM8fMzMv/+1UeUHJ1+wz5AAAANANSWSfXHzukKfdumpkamc5A+gg/3nfkotJfrZ1aWwbcgYAAOiG9tfHbyl5/pCH5Z/fLmcAHbRi83B5IcnbLpTnDwBAN7lnzc9Knj/kYYmaeJQ8f3SQRNkL5YUkT4vMafL8AQDoJve8eU/h+UN+FtuXyPNHB+7CnVC4kORnsXmxPH8AALopVeYFhecP+Vlk3iTPHx24i/bRwoUkL2s2zDJ5/gAAdFNTTT5APn/I17JPyfNHB5qPEgylvzebx+4izx8AgO6amu+eOdeXPIfIu+z58vTRgbtwlxcvJHlXbL4rzx4AgF5IlPlW4TlEPnaNPHtM4+CF2R7uom0tuZDkWWls3iLPHwCAXtD8/lgwrRzdcA95/tiBJM4OkheQ/Myd9dHy/AEA6IU0tkfJ5xD5Gb8/NgtpZB4jLyD5Wf5FSfL8AQDohfzL2uRziPwsUeZJ8vyxA6kya+QFJC+7Sp49AAC9pPkdsiBKlDXy7LED+c/9ygtIXvZtefYAAPSSe/Z8s+R5RL4VmXfKs8cOuAv26cIFJP/iRQEA6DOt7MmF5xH52Jfl2WMH3MX6SckFJM9KYvNSefYAAPSSVtkx8nlEXnahPHvsgLtY15RcQPKvI+TZAwDQS7oxeWjJ84j860Z59iixamxqt5KLRx7WbEwskOcPAEAvtcbX7Kn5rpAgyr8XRp4/hGZjclReOPKyy+TZAwDQD6kyF5U8l8iz+MjiGUhjm8gLR172DXn2AAD0g1u8v1jyXCLPakV2uTx7CImaeJS8cORj9mR59gAA9EOqsjcWn0vkW0lsHyfPHkIa2WfLC0f+lX9Jkjx7AAD6we0aL5LPJfIvt2u8QJ49hPybhuSFI/9KI/MYefYAAPRDGk8+Qj6XyL/SOPt3efYQ3IU6UV448rDRySXy7AEA6Ic0Wn+fwnOJPIwfa+0oic3/FC8cedZWPuIHADAo4+NrdnXPoi0lzyfyqcieIc8egrtQXy5cOPKty+W5AwDQT+5Z9OeS5xN5VKLMt+S5Q9Cx+bG8cORXqTLnyHMHAKCf3PPo2/L5RN71C3nuEDR/A/W/yHxUnjsAAP3Ej7YG0ZXy3CG4i3RLyYUjvzpBnjsAAP2UqOxVJc8n8qstq0amdpZnj7bm/pN7lVw08qz881Pl2QMA0E+pyp4vn0/kYWNmkTx7tDVjc0DhgpF3uZvdE+TZAwDQT/n3ScjnE/lX0rD3lWePtjSafKC8YORfScMcJs8eAIB+SpRN5fOJ/Ks1OtGUZ4+2fCGTF4z8q6XMveXZAwDQT8vHNozJ5xN52RHy7NHWis3DSy4YeVb+s/zy7AEA6KcVat3u8vlE/pUoc6Q8e7TlP/srLxh51y3y3AEAGAT3TLq+5DlFPhVlT5HnjjatsmcWLhj51sXy3AEAGIRUZb8teU6RT8X2ufLc0ZZE2QsLF4w8y/5InjsAAIOgVfa94nOKfCpVZo08d7S5v5VMyAtGvmW/JM8dAIBBSJT5TPE5RV4V2Y3y3NHmLtB/FC4Y+dbp8twBABgEHZnTSp5T5FFJZF8rzx1t7m+eb5AXjPwqjc2p8twBABiEVGVvl88p8q6T5LmjzV2cU0ouGHmU+5vnm+W5AwAwCFplr5fPKfKrRNn3ynNHG//kE0T/Kc8dAIBB0LF5RclzinwqsmfIc0dbqrJPFC4YeVWqjJXnDgDAICRRtlY+p8i7Pi/PHW35J16UXDDyqCQ2L5XnDgDAIGiVHSOfU+RXiTLfkueOtvziyAtGnhWb58hzBwBgEJLYPK3wnCLPsmfLc0ebu0BnFi8Y+VQamdXy3AEAGATdyB4rn1PkXd+W5442zeLtfYkyR8pzBwBgENIo+1f5nCK/cmf8HXnuaHMX6Cx5wcizIvsQee4AAAyCey4dUXhOkV/F5rvy3NGmWby9L393QZ47AACDoOOJB8nnFPlW9j157mjLfwC+eMHIp1i8AQB1kcbmcPmcIu/6vjx3tLF4+18rsg+W5w4AwCA0RydXyucUedcP5LmjzV2cb5dcMPIoFm8AQF0kDXOYfE6RX6XKnCPPHW2axdv7WLwBAHXB4h1E58pzR1v+kS8lF4w8KlHZKnnuAAAMgm5MHiqfU+RdP5TnjjYWb/9j8QYA1IXbO1bI5xR5F4v3jrB4+x+LNwCgLli8g+g8ee5oyz/kvOSCkUclo/Zh8twBABiE/PeO5HOKfMv+SJ472li8/S+Js8fLcwcAYBDSKHu0fE6RZ8Xmx/Lc0ab5VBPvS1X2dHnuAAAMQhqZ1fI5Rb7FN1fukLs4XyleMPKpVJkXyHMHAGAQ0sg+Wz6nyLNi83V57mhzi/enCheMPCt7mTx3AAAGwS3eLyo+p8irIvM5ee5ocxfo9MIFI7+K7EZ57gAADEIaZ+sLzynyq8ieIc8dbe4CvatwwcivIvMaee4AAAyCjs0rCs8p8qpUmffJc0ebju1b5QUjv0oi+2Z57gAADIKOs9fJ5xR51yny3NHmlrLXllww8qg0NqfKcwcAYBC0sifL5xT5VaqyN8pzR1sSmePlBSPPiu3H5LkDADAI7rn04cJzirwqVXZKnjvakihbKy8Y+VWizLfkuQMAMAg6Ml+Tzynyq1QZK88dbVplx8gLRt71C3nuAAAMglb2/JLnFHlUouxx8tzR5l4Az5AXjDwrMn+V5w4AwCC459KfC88p8qokNs+T5462VGVPkBeMvGvzyMjUfHn2AAD0m3sm3VLynCKPSuLsaHnuaGvF5uHygpF/NRsTC+TZAwDQT63xNXvK5xP5VxLbx8mzR1tzdHKlvGDkX8sX2/vJswcAoJ/06Ib95fOJPKyRPVSePdrSaPKBhQtG/hVPPEiePQAA/dRUWavwfCLvajbMMnn2aFsa24a8YORfSWSfLM8eAIB+0o3ssfL5RB7WWB/Ls0dbs3nsLu4ibS1cNPKq/PPa5dkDANBP+cfMyecT+deqsand5NljO+4iXS0vGvmWPVmeOwAA/aQj86bi84l8KlXZDfLcIbil7DfywpFnReZz8twBAOgn9zz6ZOH5RL71B3nuEHRsvlty4cijEmUukOcOAEA/aWV/JJ9P5F3nynOH4Jayz5RcOPKr6+S5AwDQT+5ZdGXJ84k8KlH2C/LcIbi/gb5bXjjyrzTaeE959gAA9MOSfabuKp9L5GFx9gF59hDS2PxX4cKRd7VGJ5ry7AEA6Ickzg6SzyXyshPl2UNwF+nlJReOvMs+UZ49AAD9kEbZo4vPJfKtVBkrzx5CqrKnywtHHhabTJ49AAD9oFX2ssJzibwric3z5NlDSEbtw+SFI/9KlH2vPHsAAPohjc2p8rlEHtbIHivPHoKOzCGFC0c+9gN59gAA9IN7Bn275LlEntWK7HJ59hBWqHWRvHDkZflHCs6T5w8AQK+5Z9BVJc8l8qxmbA6QZw+h2Tx2F3extsiLR/61dN+JxfL8AQDopebitfvK5xF52Zbx8TW7yvNHCXexLi65gORZiTJHyrMHAKCXdCN7qHwekY/ZS+XZYwc0P3sVRImyRp49AAC9pPnY4lD6vjx77ECqsg+WXEDyLb5RCgDQZ3xDdhilynxEnj12QEf21fICko/ZH8mzBwCgl3Rsvlt8HpF3xdnr5NljB3Rsn1u4gORjN60eWb2TPH8AAHpjar579lxb8jwi77LHytPHDrQi++DiBSQfa6rJB8jzBwCgF5YvtveTzyHyszSefIQ8f+yAHt2wv7yA5Gepyp4vzx8AgF5II/ts+RwiP2s2Jg6U548daH+W923yIpKP2XfL8wcAoBd0lL2j+BwiD9u6Qq3bXZ4/ppEqc1HJhSTPSpS5QJ49AAC94J4758nnEHnZZfLs0YG7aGeVXEjyr81L9pm6qzx/AAC6adXY1G7umXNryXOI/OsH8vzRQf4ZzyUXkvzsCHn+AAB0Uyuyy0ueP+RjkfmoPH90kKjsVYULSV6WKmPl+QMA0E1aZS+Tzx/ytez18vzRAb95HFSflOcPAEA3uWfNh0qeP+Rhbod8kTx/dJDG5nB5IcnbLpHnDwBAN7lnzR9Knj/kYfn3wcjzRwdptPGe8kKSv7WUubecAQAAuiFdnO0nnzvkb4cuzO4lZwAz4C7eZfJikp8lUfZCef4AAHRDEpvnyecOedsV8vwxQzo2Xy+5oORjsf2YPH8AALpB8/PdIXWmPH/MkLt4J5VcUPKz/G+o8+QMAABQlXvG/KXkuUM+FmXvkOePGdIqO6ZwQcnbWio7WM4AAABVNBsTB8rnDXlcbF8iZwAzlEbZisIFJW9LomytnAEAAKrIFzH5vCGv40v55qq5/+Re7gJuLbmo5GeflzMAAEAV7tnyyZLnDXlaszGxQM4AZsFdxIvlRSVvu27VyNTOcgYAAJijee7ZcmXJ84Z8LDJ/lQOAWXIX8suFC0v+1pg8VM4AAABzoeMJXXjOkM99U84AZsldxBNLLiz52wlyBgAAmAsd2VeXPGfI01KVvV3OAGZJx+Y58sKS1/1CzgAAAHOhVfbTkucMeVoa2RfJGcAs8c9E4dWMzQFyDgAAmI2l+04s1nxAQ1ClsTlczgFm6eCF2R7uYm6RF5f8LYnMOjkHAADMRhKbl8rnC/ndCrVubzkHmAN3MX8nLy55HV/3CgCoJFHmqyXPF/K1yPxRzgDmKFXmI4ULTD53G39rBQDMVWt8zZ7uWXJryfOFfC22H5NzgDnKv9GwcIHJ87JnyjkAAGAm0sisLj5XyOtiOyHnAHPEV8cHWGw+LucAAICZcM+R0wvPFfI6frGyi1aNTe2m+SejoEpVdkOzMfUvchYAAJhOe2e4Tj5XyOtuyz+MQ84CKnAX9bySC00el8TZ0XIOAACYTqLMk+TzhHwv+6mcA1TkLuwpxQtNXheb/5VzAADAdNwS9qnC84Q8z75bzgEqSiPzb8ULTZ53K59uAgCYqeb+k3u5Z8fNJc8T8rhUZc+Xs4CKkoa9r7zQ5H9JlL1QzgIAAGV0bJ8rnyPkf001+QA5C6hunru4V8uLTb5nz5aDAABAGR2ZrxWfI+Rz+YcxrB5ZvZOcBXQB30IVZFt0Y30sZwEAgO3pMbPIPTM2lzxHyO/OkrOALtGRfXXJBSfPS5WxchYAANieVtnL5POD/C9V2RvlLKBL0ih7tLzg5H+JMhfIWQAAYHuajx0OsiSyT5azgC5ZumjjPu4ib5UXnfwvUTaV8wAAQK7ZMMvkc4PCqNmYHJXzgC5yF/l38qKT/7nF+71yFgAAyCWx+W/53CD/S5W5SM4CuixfwOSFpyC6sTW+Zk85DwCAsC3ZZ+qumq+ID7PInCbnAV2WquzphQtPYRTbl8h5AACELVXmBYXnBQVREtlnyXlAl7U/Loif8w4wfskSACC558MP5fOCwqgVWSXnAT3gLvYv5MWnMHIvsuVyHgAAYUoa65fK5wSFkv2NnAf0SKLM24oHQEEUZe+X8wAACBO/VBlu+dnLeUCPpLE9Sh4ABdNNzf0n95IzAQAIy2EL7N00v1QZbGlkVsuZQI+sHN1wD83XwoZbbDI5EwCAsLjnwcsLzwcKpa35d7vImUAPab6hKuQuXjUytbOcCQBAGFaPrN5JR+aPJc8HCiA+bGEAtMpeLw+CQso+Q84EACAMOsqeUnwuUDDF9q1yJtBjrdg8vHAQFFD2fDkTAIAwpMqcU3wuUCglsX2cnAn0WLMx9S/u4t8qD4PCKY2yf5VzAQDwW3N0cqV8HlBQbeZDFgZEK3t2yYFQMNkvyZkAAPgtUeYzxecBBdS5cibQJ4nKXlVyIBROW/Xo5BI5FwAAPy1TG8bdvX9LyfOAgil7vZwL9EnSMIcVD4RCKlXmfXIuAAB+0lH2DvkcoOA6Qs4F+mTbxwkpc0XJoVA43dJsTI7K2QAA+GWFWhe5e/7NJc8BCqer8t1Pzgb6SEfmtJKDoYBKY3OqnAsAgF9Slb1d3v8puE6Xc4E+08o+seRgKKxuXbrvxGI5GwAAP/BuN20ryp4iZwN9dvDCbA/Ni5GUeZecDQCAH9w9/pSS+z6F1a2t8TV7ytnAAKTKfLHkgCisNi0f2zAmZwMAMNxakVXuHn9LyX2fQioyX5OzgQFJouyFhQOiEHuPnA0AwHDTvNtNrkTZ4+RsYECWxrah8890LjkoCqpN6eJsPzkfAIDhxLvd1G4rv8tVM+5QflhyUBRYfK43APhDR+ad8j5P4ZUoc4GcDQyYjs0r5EFRkN3WGp24v5wPAMBwSRr2vu6evqnkPk+hFZnXyPnAgLVUdnDhoCjIEmW/IOcDADBcUmU+K+/vFGbuuZ7K+UANuL8R/VEeFgVaZB8i5wMAMBx0PPGgwn2dAs1e6kZinpwR1IA7nJOLB0aB9pORkan5ckYAALU3T/N7W/SP7LvlgKAm8nc5iwdGwRbb58oZAQDUW6qypxfu5xRsaZQ9Ws4IaqLZPHYXd0hXykOjYLsk/2ZTOScAgHpaNTa1W6rMRSX3cwqzq5YsmbqLnBPUCB89RKL/kDMCAKinVNnJkvs4hdu75IygZpqjkytLDo4CLVXZDfkXLMk5AQDUS2u/4xe6+/a18j5O4ZaobJWcE9TPPD7dhO5UZM+QQwIAqBd3vz69cP+mkPszH5IwJNxhnVBygBRwyah9mJwTAEA9tCL7YHev3irv3RR0J8o5QU011eQDSg6Qwu7/xsfX7CpnBQAwWPkvzyWR+VXJfZsCrtkwy+SsoMbcof1MHiKFXaKyV8k5AQAMlo7sRnm/puC7UM4Jas4d2oaSg6Swu3mZ2jAuZwUAMBjLxzaMuXvzTSX3awo7PpFs2Czdd2Kx5ufFSJQo81U5KwCAwdCx+V95nyZqKXNvOSsYAmmUfUceJpG70T9VzgoAoL+SOHt84f5MpMwP5axgSLgF68UlB0rBZy9dodbtLecFANAfh4ytvbu7H19SvD9T6CWRWSfnBUOi2ZhY4A5xkzxUIteH5bwAAPpDx9kHSu7LRJv50rshlyj7hZKDJcq/1fIJcl4AAL2VRuYx8n5M1O6bcl4wZJI4O7rkYIn+n47MX/N/FZEzAwDojZWjG+6R/7hf4X5MtK3smXJmMGTyL01xh3ll8XCJXLH9mJwZAEBvaL4WnnbcVavGpnaTM4MhpCPzppIDJrq9KHuKnBkAQHfxKSY0bbF9q5wZDKk0Wn8fzWd60467srXf8Qvl3AAAuiONNt7T3WsvK7n/EuVtbY1O3F/ODYZYosy3Sg6aaFupMp+VMwMA6A53n/2kvO8S3VH+vStyZjDk8i9NkQdNtH1JbF4q5wYAUE0SZS+U91ui7Usi+yw5NxhyS5ZM3cUd7hXysIm262YdmUPk7AAA5iaJs4PcvfXvJfdbojvilyp9larsjSUHTrR9Fx68MNtDzg4AYHZWqHW7u3vqL0rus0Tbd5KcHXhimdowrvklS+pUnH1Azg4AYHa0su8u3F+JRPxSpefcIX9DHjpRMT7EHwDmKo3M6uJ9lejO8UuVAeBmQDPs+pYy95bzAwCY3vKxDWPuHnpNyX2V6E7xS5UBaDaP3SX/qnB5+ETF7Pn8wgcAzFz726J/WLyfEhW6Ov89ADlD8JA77BNKBoCoUKqyD8r5AQCU01H2fnkfJSrPniznB57Soxv2d4e+pTgERKW9XM4QAODOdGxfUnL/JCprS/6t4nKG4DF36J8uGQSism5LVLZKzhAA4HbN0cmV7l55a8n9k6hYZD4nZwieSxrmsMIgEO24K5qNyVE5RwAQuhVqXeTukZeV3DeJyosnHiTnCAFwh/+DwjAQ7ajY/JhfBAGAf2r/MiXPUppN58k5QiASZZ5UMhBE03W6nCMACJW7J76n5D5JtMOSODtazhECsXpk9U5uCH4nh4Jo2mKTyVkCgNAkUba2cH8kmqZUmYtWjUztLGcJAUmUPU4OBlGHtuRfxCRnCQBCkarsCfm9sOT+SLTD0jhbL2cJgTl4YbaHG4ar5HAQdehm3Zg8VM4TAPguUTZ198CbSu6LRNN1bWt8zZ5ynhCgNDb/VTIgRJ26cpnaMC7nCQB8lS7O9nP3vstL7odE0xeZN8l5QqD0mFnkhuKWwpAQdShV2W+bjYkFcqYAwDcrRzfcw933fi3vg0QzaJNurI/lTCFgqTLvKxkUopn0/VVjU7vJmQIAXyxZMnUXd687q+T+RzSTPixnCoHTo5NL3GBsLRkWoo6lKvvEyMjUfDlXAOCBee4+92F53yOaaa3RiaYcKmBEK/slOSxEMy2NzalypgBg2Okoe4e83xHNojPlTAHbJCpbVTIwRLPpRDlXADCs3D3thJL7HNHMi+xD5FwB/+CG5MzC0BDNpjh7pZwrABg27n62oXB/I5pd35ZzBdyJG5IjSgaHaFbl3+gmZwsAhoWOzYvlfY1otqVR9q9ytoCCRJlvyeEhmmVbU5U9X84WANRdEtlnab6VkqrHu92YmTQ2h5cMENFs26xj81Q5XwBQV2lsj3L3rttK7mdEs4p3uzErbmi+IYeIaA5t0so+Uc4XANRNe+m+teQ+RjTL7NlyvoBpJQ1zWHGQiObUbUmcHS1nDADqIonsk/W2NwoK9y+iWdeK7IPljAEd6ch8TQ4T0RzbnEbm3+SMAcCgpSp7uubHS6h7nSVnDJiRNMpWlAwU0VzbkirzAjlnADAoaWSfrfPfRyner4jmVP6dKHLOgBnTKvuKHCqiCm3VsX2JnDMA6Lf8jQDNp5dQdztLzhkwK63ILi8ZLKJKJZFZJ2cNAPolfwNA528ElNyfiOYa73ajK7SyX5LDRdSF/kPOGgD0mvuL//El9yOiqp0pZw2Yk0TZVPPOAPWgNDanrh5ZvZOcOQDovqn57r5zirwPEXWj5ujkSjlxwJylKvuEHDKibpQo85kVat3ucuYAoFvGx9fsynOMetin5cwBlTRjc4DmiwWoV8Xmu2753lvOHQBU1dx/ci93nzmrcN8h6k6bWsrcW84dUFkam7eUDBxRV0oi86tmY3JUzh0AzNXS2Dbc/eVn8n5D1K0SZd4m5w7oipWjG+7hhuwqOXREXeySJM4OkrMHALPVbEwcmCpzUcl9hqhbXePmbIGcPaBrkihbWzJ4RN3sukRNPErOHgDMVBpPPsLdS64tub8Qda1EWSNnD+iqJUum7pKq7Ldy+Ii63GYdm0zOHwB00n6DiG+jpN4WmT/mv7Qr5w/oukSZJxUGkKgXxdkHuLEBmIlm89hdEmXfW7iPEPWgJDZPkzMI9EwaZd+RQ0jUo77f2u/4hXIGAeAOSxdt3Ecre3bJ/YOoF53rxm6enEOgZ5oqa2m+VIf618VJY/1SOYcA0FSTD8j/2b/kvkHUk9LYHC7nEOi5VJmPyGEk6mE3JnF2tJxDAOFKIvtkd2+4vuR+QdST8i99k3MI9MXysQ1jbghvlkNJ1ONOyX/JV84jgHDkP8+tlT255P5A1Ms2pdH6+8h5BPrG/c3vDSWDSdTrzsv/4ifnEYD/8i/acveAc0vuC0S97iQ5j0BfHbbA3s0N4iUlw0nU665JYvs4OZMA/JUoc6R77f+t5H5A1OPspa3xNXvKmQT6TkfZU4oDStSX8l/wPXHVyNTOci4B+GP1yOqddJy9zr3et5TcB4h6Hh8fiFrRKvuKHFKi/pV9TzfWx3IuAQy/FWpd5F7nZxZf90R96xtyLoGBasbmAM0vWtJgu4Z3JAC/tP9F9eqS1ztRv7oladj7ytkEBs4N53+UDCxRf4vsGYeMrb27nE8Aw6O5/+Re7vX8ocLrm6jfReY1cj6BWsi/2lsr+5vC0BL1vz/rRvZQOaMA6i9R2Sr3Gv5TyeuaqN/9foVat7ucUaA28mWnZHCJBlH+i5cnrRqb2k3OKYD6yT+fX0fmTZpfoKSalH+KjpxToHb4RkuqWb9sjU405ZwCqI+ksX6pW3IuKHn9Eg2o7FNyToFa0mNmkRvaa4tDTDSwbsvfSWs2pv5FziuAwcn/GT9V2Ru3vUaLr1uigeRm8gY+KQtDJVH2ODnIRDXoD63YPFzOK4D+05F9iHtN/q7kdUo00NwOY+S8AjU3NV8r+yM5zEQ16fRmY2KBnFoAvbdCrdtbR+a0ktclUR36ebN57C5yboHaS2ObuAHeXDLURHXob0lknyXnFkDvJHF2tHvtXV7yeiSqQ1t0Y/JQObfA0Gj/7J4cbKL6FJuvNxsTB8rZBdA9+ReQuNfblwuvP6I6FZk3ydkFhsrtn+1tflkYbqJ6tSmNzVv44h2gu1rja/Zsf0TgppLXHVGdupCPn4UXdDyhNb+xTsPRFVplx+S/oyDnGMCszEti8zy3dP+15HVGVLc2tyK7XA4xMLTS2PxXyaAT1TR7vpvZw+UcA+gsjbIV7nV0XvF1RVTPEmXeIOcYGGrbvpFMmZ/JYSeqdZE9Y+m+E4vlPAMoajYmR1OVfVDf/q2xxdcTUQ1LIvOr/Mdi5TwDQ6/ZMMs0P+dHw9ctWtmTD12Y3UvONICRkaWLNu6jY/vW218rhdcPUZ3bnCibypkGvJEqO1Uy+ES1b9s3mUXmNc39J/eScw2EKP/FyfY9/Xr5eiEakk6Qcw14Jf9Q+vznZ0uGn2hYusotG5P5V13L+QZCsO3TqmI74V4LV5a8PoiGpV/wIyYIQktlB7uBv7XkRUA0RNlL08i+KP/9BTnjgI9u/12d7Bg3/xcXXw9EQ9Vt+Zf8yRkHvJXG2b+XvBCIhrFLkihb22xM/Yucc8AH+b/uuDl/uevPJfNPNHQlkX2tnHPAa6tGpnbWfNwU+dWVOs5euXJ0wz3kvAPDKP9CqfabJFeUzDvRUJYocwH/UokgLV9s7+deBDfKFwXRkJf/otmJeswskjMPDIP8E3zcDJ/gurZkvomGuZtaoxP3lzMPBGPbN5sVXxhEPnSLm+//bjYmDpRzD9RRPqtubk9x3Vwyz0QelH8zMRC4VJmPFF8cRP6UKPPVJLaP46voUUPz0sg8xi0kX5FzS+RVsf2YHH4gSLd/Fmz228KLhMi3IvNHHZtshVq3t3wdAP2U//x2Epl1bi5/V5hTIv/6A9/BAGwn/1gfzUcMUjj9XSv77jSafKB8LQC9lMTZQe4vgO/U/H4NhdOmVmSXy9cCELw0ztaXvGCIfO9cHduX8Gko6JX83W0dmxdvm7Xi/BF5Xf6FZ/I1AeB28xJlvyBfNESBdHP+M4iJmnjU6pHVO8kXBzA7U/NbsXm4juwZ22arOG9E/heZr/G7NcA0mo2JBf+/vbsPsrOqDzi+gUAgQDvQhE3uOc/uhq7j0NXA5j7nudkEZDWgf2idKsmIpQIqjQyahs2ec+4GW3MFrYZaixAdhhalzNiRVx3RWttqENsCBgwZifKmFkIIqA2CAfJKe84uVTn3EHc39+259/uZ+c7CjJLN7j2/c3bvvc/jFssTVYuHqLParqT+JFdEwVQtkmP9/uYgipvdUKcnzFNc1hWYhFTqYbdo9lctIqKOzN6bCnNpMTF/GK4VwFM9YycpYdf6x0r144eoIzuQJeW3hGsFwKtwi+ajkYVE1OHp+/3dMdOCfW24ZtBZ/G+2J+4qab9f/Tgh6uxSadaHawbAQfjXuLoN5TvhYiKiX/dAKvW6oiy/Plw/aE/jVyRJzF+O/wBW/XggIunfTGnuLhZXHhGuHwC/Q0lYOf4arcjCIqJX9Ljr71Nhly+dY48L1xLyyX8vlbTv9N/bl7/H4fediF7Z06qwJgnXEoBJyhJzultIeyOLi4ji7fPPFvnXhaeFNYPhmkJr889g+Mufue/jRsXsI5pKbvaZM8I1BWCK0sR8MLLAiGhS2SfHLymX2ItLUi8M1xeayx+0/ffm5cv+ba/+/hHRJFsdri8A06SE/nxkkRHR1HtGJearqbQmE3qI10I2znBXZab7umfuoD2aSfMV973YGfn+ENEUy6S+IVxvAA5Bf/+qWYq7rhHVo+ddd2SJ+bSS9lz/Bj635GaEaxBT57+W7kDwpxNf2/GXjnCbdqJal5j7huTI0eH6A3CIeLMlUcN63l8ZwB0ar3YfL/SvFR8YqBwZrklM8F+bTJRPUYl9byrNVe7r91+KQzZRI/p5sVDuCdckgBrhzZZETe1hdxD/uvt4pX9dctpjzxqSIyJcp+2q2HvJfCXssvHXZCf27/zXwv1w8kjk60RE9W9fSdg3husUQI2Nb3rVC5CImpf/7e5mJfWt/rfk7p/HMmHO83eh9Td6ycPTwMN9laP83UH95+w/94k7QZoNqTRf9k9lu39+LvL3JqImlQozEq5jAHWSSXNduAiJqKXzbyR8QPnXOifmJiXMF/zd5fxNgDJhL3Kd72/xPHHwLZ+yuG+sL+y0nrHjw1mwsFsfE/7vfP6No/6/pQr6TP/fToX+gP+z/J/p/2z3edziPxf371vcx19EPl8iat2+GM4CAHXk32zpX4MaWYxERETUvm3Ow7NoQNvxry11C3BHZFESERFR+/Uz/4xWeB4A0CBFqUtq4nJo4eIkIiKi9ukF/xKy8BwAoMEyqd/hFuT+yCIlIiKi/HcglebscP8H0CRuUa6OLFQiIiLKe4kdDfd9AE2m/LWFw8VKRERE+U3oz4b7PYCWUDls/Jq74aIlIiKiPHb7iq4Vh4e7PYAW4S8xxGUGiYiIcl5i7vPX6Q/3eQAtZkm3PtEt2h9XLWIiIiLKQ48Vey+ZH+7vAFpUWrCvVdyNjoiIKG/9Mk3068J9HUCLyxJzulvAL0YWNREREbVee5Wwy8L9HEBOuJ+az3EL+UBkcRMREVErlZgLwn0cQM74639WLW4iIiJqmTJpy+H+DSCnlLCXhYuciIiIml+WmI+H+zaAnFPcYIeIiKjV2hDu1wDaw4xMmusii56IiIgaXaKv93tzuFkDaBuVw1Rib6xa/ERERNTA9K3clRLoAMXiyiNSab9WPQSIiIio3qXSfHNgoHJkuD8DaFPDfZWj3ML/djgMiIiIqH5lQn+3WKjMDvdlAG1uYG7l2Eyau8OhQERERHUoMfcVTyr/frgfA+gQp/WMHZ9Ks6VqOBAREVHNSoX54eC8tXPDfRhAh1nSrU9U0j4UDgkiIiKqST8ZkiMi3H8BdChVWJNkUj8SGRZEREQ0/R4dnD/aG+67ADrcYGILbkA8GBkaRERENOXsQyVhZbjfAsC40oJLu92weKB6eBAREdFk86/pLvZeMj/cZwHgFfybP5TU94dDhIiIiCbVD/z7p8L9FQCihuTICW5wbIoMEyIiInr1NhcLo3PCfRUADspfa9QNkLsiQ4WIiIiq2+R/cRXupwAwKUvn2OPcILkzMlyIiIjoN93FzXEAHLKF3foYN1C+FRkyREREHZ+/Dbz/RVW4fwLAtAzJkaOV1P8SDhsiIqIOb6P/BVW4bwLAIenvXzXLDZjbI0OHiIioE/tGsVCZHe6XAFATw12VmSrR10eGDxERUceUSX2D3xPDfRIAas4dvv86HEJERESdUCrNercVzgj3RgCom1TaD7kBtD8cSERERG3aAdfqcD8EgIZIhV3uhtCLkeFERETUTu1RiXlXuA8CQEOpZPQNbiA9ExlSRERE7dCzmdBvCvc/AGiKNNGvc4PpiciwIiIiynM7lDCnhvseADSVKqxJ3IDaGhlaREREOcw+lPXqBeF+BwAtYUiOnODv4FU9vIiIiHLVPYPz1s4N9zkAaCn+LpepNF+ODDEiIqKWL5X2a9yNEkCOVA7LpL4iHGZEREQtnTCfWtG14vBwVwOAlpcJc54bZLurBhsREVFrtVsl5oJwHwOAXMmEHlL+XeHVQ46IiKj5CfNUWjBLw/0LAHKpJKxUibmvatgRERE1NX1/sVDuCfctAMi1YqEyWyX2xuqhR0RE1JRu402UANqaG3Qfcb0UGYBERESNSZjL3ZY0I9yjAKDtKGnf6QbfrqpBSEREVN9eSBPz7nBfAoC2lonyKW4APhYZikRERPVou0pGVbgfAUBHWNKtT3SD8M7IcCQiIqpdifmPwcQWwn0IADrKcFdlZirNesXrvomIqPb5veVv/F4T7j8A0LEyYd7mhuPOyNAkIiKaTs+kiX17uN8AAJzB+aO9blB+LzI8iYiIppC9N+vVC8J9BgDwW4rFlUek0lxVPUSJiIgmkTCf6+9fNSvcXwAAryIVdrkboM9WDVQiIqJImdS/4lKBADBNi+RYvxumm8PhSkREFPTA4l57criPAACmYLivcpSS9trIkCUiIvrfNDH/WCxUZof7BwBgmlJh36O42yUREf2mXZk0F4b7BQCgBkrSvMYN2bsjw5eIiDqre/yeEO4TAIAa8jdBcAP3I669kUFMRETt3b5M2go3xAGABlLJqHID+MHIUCYiovbs4aLUpXA/AAA0gH8zjRvEGxS3myciauuyxFyzsFsfE+4DAIAGy5LyW9xg3h4OaiIiynnCPJUJ87Zw7gMAmmhIjpyQSX1z1dAmIqJclknzlcF5a+eG8x4A0CKU1H/mBvYvwwFORES56TkuEwgAOVEslHvc4P5WZJgTEVFrt1H1jJ0UznUAQItLE/M+N8R3RgY7ERG1Vjszqd8fznEAQI6UFlzarYT5UmTIExFRK5SYm1SfmRfObwBATmVCv9UN+MeqBj4RETWrbWli3x7OawBAGxiYWznWDforXfsjGwARETWmA64Npf5VvxfOaQBAm0mlzVJptkQ2AyIiqm9bVaG8JJzLAIA2NtxVmamEXes2gRcjGwMREdW2PanU6wYGKkeG8xgA0CEWybH+VJpvRzYJIiKqQZnQ313ca08O5y8AoEMpac91G8S2cMMgIqJpt83P1nDeAgDQtbBbH6OEvUzx8hMiokNptxLmcj9TwzkLAMArLO4b68ukvjmymRAR0cG7LevVC8K5CgDAQaVSD3P1EyKiSbVVCbssnKMAAEzaiq4Vh2fCXuQ2lV9ENhoiok5vZybNKn+lqHB+AgAwLaf1jB2fSnOV22T2RTYeIqJOa3+WmGsysfYPwnkJAEBNlHpG/0gl5t8imxARUUc0fglWYU4N5yMAAHWR9tiz3Aa0KdyQiIjaN3uvn33hPAQAoCFSac5Ohflh9QZFRNQ2/SgTZkU4/wAAaDj/BkyVmAuUMD+NbFhERHnt8TQx7/MzLpx7AAA01cBA5Uj/7n63WT0d2cCIiPLSz1NhRvr7V80K5xwAAC3F360tS/SH3eb1TGRDIyJq1Z5LpV63dI49LpxrAAC0NH8JQreRfcL1fGSDIyJqlXZnifl0sTA6J5xjAADkiuoz8/ym5ja3XZENj4ioWe3ys2kwsYVwbgEAkGv+RhOZtBW32f1PZAMkImpUfgZ9lJvfAADa3sDcyrEqsaNK2icjGyIRUb3akUpreA03AKDj+CsGuMP3SrcZPhrZIImIapO/1GliL+YqJQCAjuevkZsm5t2pNFuqNkwioum3NRX2PcNdlZnh3AEAoOOpgv5jJfV/RjZQIqLJtimT+h1upMwIZwwAAAikBbNUJfZGt4HujWyqRERhe/3M8LMjnCcAAGAS/GW+lDCXu56KbLRERE+nwn6MSwICAFAj/nb0Stpz3SZ7T2TjJaLOa1MmzHm8YRIAgDpSyajKpL7Bbby7I5sxEbVvezJp/qkodSmcCwAAoI6WdOsTU2EudZvxE5ENmojapx2p1Ov8XXDDOQAAABrIXyosFXa5EuZf3QZ9ILJpE1H+OpBK88000ef4l5qF6x4AADSZKqxJskR/OJP6kchGTkSt36N+DZeEleH6BgAALarYUz4tlfYf3Eb+XGRzJ6IWyf2g/KtMmuuyxJw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")}));
+end HydrogenCHPPlant;
diff --git a/PowerPlants/PVPowerPlant.mo b/PowerPlants/PVPowerPlant.mo
new file mode 100644
index 0000000000000000000000000000000000000000..3ffa51b280a804f5a70ec09b3123772c58e07b57
--- /dev/null
+++ b/PowerPlants/PVPowerPlant.mo
@@ -0,0 +1,30 @@
+within PNRG.PowerPlants;
+
+model PVPowerPlant
+ Real powerPerArea(unit = "kW/m^2") "Power per Area of Sunlight";
+ Real currentPower(unit = "kW") "current power";
+ Real areaPV(unit = "m^2") = 1 "Area of Photovoltaic Panels" annotation(
+ Dialog(enable = true, group = "PV properties"));
+ Real efficiency_PV = 0.2 "Energy conversion efficiency of Photovoltaic Panels (must be smaller than 1)" annotation(
+ Dialog(enable = true, group = "PV properties"));
+ Interfaces.ElectricalOutput electricalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.FileInput fileInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticFlowPlace SolarPP(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {58, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {currentPower}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {2, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerPerArea = t1.power;
+ currentPower = powerPerArea*areaPV*efficiency_PV;
+ connect(SolarPP.outTransition[1], electricalOutput) annotation(
+ Line(points = {{68, 0}, {110, 0}}));
+ connect(t1.outPlaces[1], SolarPP.inTransition[1]) annotation(
+ Line(points = {{6, 0}, {48, 0}}, thickness = 0.5));
+ connect(fileInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-2, 0}}));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {7, 47}, extent = {{45, -45}, {-45, 45}}, imageSource = 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+ uses(PNlib(version = "2.2"), Modelica(version = "3.2.3")));
+end PVPowerPlant;
diff --git a/PowerPlants/STEPowerPlant.mo b/PowerPlants/STEPowerPlant.mo
new file mode 100644
index 0000000000000000000000000000000000000000..2222ffaad3e3a80b2ea146503155a9383660a320
--- /dev/null
+++ b/PowerPlants/STEPowerPlant.mo
@@ -0,0 +1,32 @@
+within PNRG.PowerPlants;
+
+model STEPowerPlant
+ Real powerPerArea(unit = "kW/m^2") "Power per Area of Sunlight";
+ Real currentPower(unit = "kW") "Power per Area of Sunlight";
+ Real area_PV(unit = "m^2") = 1 "Area of Solar Thermal Panels" annotation(
+ Dialog(enable = true, group = "PV properties"));
+ Real efficiency_PV = 0.2 "Energy conversion efficiency of Solar Thermal Panels (must be smaller than 1)" annotation(
+ Dialog(enable = true, group = "PV properties"));
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ Interfaces.FileInput fileInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.HeatOutput heatOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {currentPower}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-4, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace energeticFlowPlace(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {50, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ powerPerArea = t1.power;
+ currentPower = powerPerArea*area_PV*efficiency_PV;
+ connect(energeticFlowPlace.outTransition[1], heatOutput.heatOutput) annotation(
+ Line(points = {{60, 0}, {110, 0}}));
+ connect(heatOutput, energeticFlowPlace.inTransition[1]) annotation(
+ Line(points = {{110, 0}, {40, 0}}));
+ connect(fileInput.fileInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-8, 0}}));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {7, 47}, extent = {{45, -45}, {-45, 45}}, imageSource = 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")}),
+ uses(PNlib(version = "2.2"), Modelica(version = "3.2.3")));
+end STEPowerPlant;
diff --git a/PowerPlants/WindPowerPlant.mo b/PowerPlants/WindPowerPlant.mo
new file mode 100644
index 0000000000000000000000000000000000000000..af3a074d20c483b7a1b2f57b3b6112bddef25d76
--- /dev/null
+++ b/PowerPlants/WindPowerPlant.mo
@@ -0,0 +1,40 @@
+within PNRG.PowerPlants;
+
+model WindPowerPlant
+ Real windVelocity(unit = "m/s") "Velocity of wind";
+ Real windPower(unit = "kW") "Power of wind";
+ Real singlePower(unit = "kW") "Power of a single wind turbine";
+ Real currentPower(unit = "kW") "Power per Area of Sunlight";
+ Integer number = 1 "Number of Wind Turbines" annotation(
+ Dialog(enable = true, group = "Properties"));
+ Real efficiencyTurbine = 0.5 "Energy conversion efficiency of Wind turbine (must be smaller than 1)" annotation(
+ Dialog(enable = true, group = "Properties"));
+ Real rotorLength(unit = "m") = 3 "Length of rotors" annotation(
+ Dialog(enable = true, group = "Properties"));
+ Real rotorArea(unit = "m^2") "Area of rotors";
+ Real densityAir(unit = "kg/m^3") = 1.25 "Density of Air" annotation(
+ Dialog(enable = true, group = "General properties"));
+ Interfaces.ElectricalOutput electricalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.FileInput fileInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {52, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {currentPower}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ rotorArea = Modelica.Constants.pi*rotorLength^2;
+ windPower = 0.5*windVelocity^3*densityAir*rotorArea/1000;
+ windVelocity =t1.power;
+ singlePower = windPower*efficiencyTurbine;
+ currentPower = number*singlePower;
+ connect(p1.outTransition[1], electricalOutput) annotation(
+ Line(points = {{62, 0}, {110, 0}}));
+ connect(t1.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{4, 0}, {42, 0}}, thickness = 0.5));
+ connect(fileInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-4, 0}}));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {-1, -52}, rotation = 180, extent = {{48, 70}, {-48, -70}}, imageSource = 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PcGr+b/zrSx1d3F5Yx+cQAMDghUFnrWP46U1ZUb8+XvOkJXn1+PBr/Vf8a0s3VlLWf7d02jm3jc8nAIDBSsv6LfHQ06+qbfGaJ6W9w3F78ZH2/G9P1N/au2MvFWuPic8tAIBBSvLqF+OBp2ddGK95EsJQd79wYVR1/HrSgXZt+7G7mzatHxmfZwAAg9K+LGLPcDM+8PSmhTOb28TrPhRLef3k8HUvin8d6dCqtrWfdBWfbwAAg5IVdTM+6PSnpS3Vj8RrPhjr6+tH7LuhmZcRaVr9x2Kx9oj43AMAGIy+v+8gKZtnxWs+UO3fPqR5lcdfW5pCV2R59f/G5yAAwCBkRfXrHQNOfyrrV8drPhCLW1bvG77OJ8e+rjTFkrJ+g/chAACDkyzXD4sHm37VvCde8/5aXF778TSvvjz+NaWNqDl1IW9uEZ+XAAC9NRptPzbt85uSy/qf4jXvj6Ronh5+/uVjX0/awPa8p2fzzrvF5ycAQG+FIeb8eKjpUefH6/1O0rx5aeqNx+pNzeeW8h0PiM9TAIBeSor69PGBpjd9K17vjQl7eW3H15AOb3n15fYlfPH5CgDQO1nZvG5smOlRJxbn3S5ec2zvR5XWfxP/XKlHXZyU1U/G5y4AQK8kRXNcxyDTm07aUj04XvP1bVpZuWlS1O+Of57Uwy5Pl+ufi89hAIDeyIq1J3YMMb1pqVh7TLzmbxuNRkclZf138c+RetwVSVk9KT6XAQB6oe8fZ5rk1S/Ga261nyPfftRp/OOlAeQCAQDop8XlHffoGF56U1LWvxGvub0wyIrqXfGPlQbUFe3f2sXnNgDAYTUa7To6DCq7O4aXXpSVzR/Ea27vQBv/OGmAXX5jL5sDADgswpByUcfg0pOaV8brfddy8z3hv312/MdqP7swVKdlfdqeT3kq61d/u/YN6uPVz/mf/16dGL4nS+HnjcLXWEt7fe4MoosWN6/9YHyOAwAcNmHY+7eOoaUXtfcuiNfbOjnffvfw3z8Z/3j9d1eme4f3hTDU/95iXj92aevZ92k/3Sk+lofq5FNX77C0pfqRLK+eEX69V6R5fUo4p3Z1rEndXbC4ZfW+8XEFADgswvBYdQws/Siv3xiv99sW8uaO6d4BePznzV3tBV5zQvj35y6V1Q8tNM3N4uO10ZZOO+e27Ud3JkXzmrSotoW1XTy+bu3r/FPOOPfO8TEEANhwYTBZ7RhWelFWNm+L13t9C2c2twk/7mPxz5uDLkvzKs+K5neyor5ffFz6arFYe0S44NsULmQ+3rGnuS7Lq7OXVlZuHh8zAIANFQaTHfGg0pfa17jH640t5M0t9v3J9NjPn7G+Gjo+W157XPtG8vg4DE37Mqckr14U9vTh0DUd+53H3hcfJwCADZX2+eIgrxbj9XZph+WsqD8Y//wZ6Kr2jcNpvvrLs3BBcEOSbdtvn+T1C8N+P9FxDOarsn51fHwAADZM+3KGsQGlJ4WB/53xem9Ie8fkWbn/QVLU57Qf45ot77xTvM9ZlyzXj9p3g7v2TdVjx2YO2t2+uTs+LgAAGyIMI9s7BpR+lNd/G6/3xqyvrx8Rft7xY19nIIWLoX9I8urx8b7m0d43nDcvCxdK/x4fpznospO2VA+OjwkAwNSFQeSfO4aTvvTWeL37o/0I1I6v1deuC30gy5uHx/tg798I7bvfwnxdJOT1p9LNZ906Ph4AAFOV9vjjQLOieVO83v2VlvUfxl+vZ13dvqfipHL1gfHaGdfeoyEcs+emRfO5jmM5q30gPg4AAFMVhuj/7BhK+tJfxus9EElZPz/d+yfz8dc9zFXbFou1B8Xr5Ttr7+Gw5/va7/N2YmVl/ZL4GAAATE0YQC6PB5LeVDZ/HK/3QCVl86y0Lx+VmdefSorm5+M1cuD2fkJV9ap09t+4fGWyXD8s3j8AwMSdcsa5t+wYRnpTe5OveM0HI8mrXwxf74r4629gF7V/AtyHOxfPmqV8xwPC8f1QxzGfpT45Gm0/Nt47AMBEtTei6hhEelP7p/7xmg9WWjQ/Hb7mpfGvMe2yoh61n7wTr4fJSvPq2aEvx8d/VtqfGwICAByStFz94XgI6VOTfgnO4vLaj4dh/RvxrzOlLmoH1ngNTM+JxXm3C8d9IbS74/sx9HZn5dovxHsGAJiYpKye1DGE9Ke8eXS85kO1uHntB8PX/tLYrzXZPpSdXt0z/rXZGEt5/eTwPfhqx/dl6F2wdNo5t433CwAwEWH4fkHHANKbks1r94/XPAnt69Szov58/OtNoMvbOxu3N2OLf0021uLyjnuE78fHOr5Hgy6ct2m8VwCAiUjz6u3x8NGn2jdMx2uelIW8uVf4NT4d/5qH0Pnuatsv7b0RwsXa69JefpztQbd7sayfEO8VAOCQhUHjzI7hoy9dHK930hY377hLWjQf7/i1D6y8+sdk2/bbx1+ffki3ND87Y29W/uw0L5wBgDkVhoz/6hg8+tIn4/VOw743sW7v+PX3r7z+2/ZPqOOvS7/se5nReWPfv6GW12+M9wgAcNDaNzaODRz96kPxmqdl3/0eDvSz8q/Oivr/xF+L/lo4s7lN+L59uON7OcSuWsqb74v3CABwULKiemTHwNGbkrxajNc8Tccvn39MUtSnx+u4gb6ebal+Kv4a9F97Z+Xw/Xtfx/d0cIWL03+I9wcAcFCysnlePGz0qSyvXx6vedr2vIE1r94bryXqq0tl9UPxz2U42k+Tal+W0/G9HVyLRfW0eH8AAAes/ZP5eNDoU4dr6Nm0af3ItKzfEa9nX19KivoH4p/DMKVF8/vp4D/JqPlc+7de8d4AAA5IGCzOHx80+lN7s7J4zRspKes3RGu6wGu8Z09SVL8bn3uDK29eGu8LAGC/tXfvHRsw+tXuhby5RbzujRbW8Yp96/lCe+O0+L8zG9qXsHWcg0Pqa+2breN9AQDsl6yofr1jwOhNSVH/e7zmwyWs5TlZUd8v/v+ZLUnRvCY+D4dUe7O3eE8AAPslDBNJPFz0qTCMfzBeM0xbWlRvjs/FAfWtdPPOu8V7AgD4jto/me8YLnpTVlSvitcMGyFcmL4zPh8HU169Pd4PAMCNWsibe40NFf3rKfG6YSOMRqOj0gO/IV5funyprL473hMAwA1K8upFHUNFr0rKnfeO1w0bJdm2/fbhPPxMfF4Ooaxo3hTvBwDgBoUBYns8UPSsC+M1w0ZbKpuHhHPx0o7zs+9dtpA3d4z3AwAwZmnr2fcJw8PujoGiN3kzMn2RlvWvpD1/vHRX/Xm8FwCAMcP4PPfmZfG64XDJ8uovxs/R3veVpZWVm8d7AQD4X8LQsNYxSPSrfPUn4nXD4bJp0/qRWVGvjJ2n/e+58V4AAP5be4ffjgGib10xGu06Ol47HE77Xo73zY7ztc99It4HAMB/G8gdYD8Urxv6ICub53Wcr70uK+qfifcBAHCT45fPPyYMC1+Kh4e+1b4nIl479EVS1GV8zvY5b+4HADolRXNcPDj0sWS5fli8duiLdPPOu4Xz9Ovxedvjrl7cvOMu8T4AgDmXFPU5HYND3/rS+vr6EfHaoU+SsnlWx7nb2/xtHADwvyzm9WPjgaGfNe+J1w59NLBPL/p0vH4AYI6lZX1ax8DQu5Kifma8duij9uVv4Zy9Lj6H+9pSsfaYeA8AwBx613LzPekwhpgr081n3TpeP/RVVtRpx3ncz8r6HfH6AYA5lOb1KWODQj8r4rVDny1sa+6aDufeB1/dtLJy03gPAMAcSfPVh6bD+FuDNndzZXCyov7TjnO5lyVl9aR4/QDAHAkDwdZ4QOhp12TLO+8Urx/6bjTafmy4QPh8xzndu7Kiele8fgBgTmTLa4+Lh4P+Vm2L1w9DkeT1C8fP6V528ULT3CxePwAw49p7BYRBoO4YDnpZ+7nx8R5gKBby5hbhPP5KfF73sayofyZePwAw49qPBI2Hgh73zXa4ivcAQ5IV1as6zu3elZXN2+K1AwAzbOHM5jZp0XwxHgr6W7MU7wGG5uRTV+8QzufLxs/v3vWZeO0AwAxL8mqxYyDocc1Px3uAIUrz6u3j53f/OmlL9eB47QDADAoXBo8PT/6742Ggx326fX9EvA8YoqTcee9wTl/dcZ73rOZl8doBgBlzyhnn3jI88X9mfBDodS+O9wFDlhT16R3ned9yw0EAmHXtGw07hoA+960Ti/NuF+8DhmyxqJ7Wca73rW+6WzIAzLA0bx6dDudOyN8uifcBQzca7To6nNtf6zjfe9Xi8tqPx2sHAGbAKWece+dhfTrR3pLl+mHxXmAWhMfjCfH53sNeEa8bABi40Wh0VHiS/3DHE3/f+1C8F5gVWdn8WMc536/yejleNwAwcFlRv37sSX8ALZb1E+K9wCwJw/en4vO+Z301XjMAMGBJ3jw1HdbHlu4tr3fGe4FZM4Q7Jieb1+4frxsAGKClfMcDwpP7xfGT/RBKivqZ8X5g1ixtqX4kPvf7VlI2z4rXDQAMTLa8807hif3T8RP9IMrrT7Xvk4j3BLNm06b1I8M5/9Wxx0CPaj/+OF43ADAg6eazbh2e1Ov4SX4oZWX9a/GeYFaFc/598WOgZ30kXjMAMBDHL59/THgy/1DHE/xQ+kT7p6nxvmBWJUVzXMfjoE99LV4zADAA7VCdFfWo48l9OOWrvxzvC2bZwrbmrmnPPzSgXWO8bgCg59Kyfkf8pD6w6vX19SPifcGsS4vm4x2Ph97kY4UBYED2vqlxEHdbvdGSsvrJeG8wD9K8/tv48dCzXhyvGQDooU0rKzdN+/+Gxu9cXv99vDeYF+Ex8Nyxx0SPSorqxHjNAEDPjEbbjw1P3EX8RD7ArsyK+n7x/mBeZHnz8I7HRZ/aGq8ZAOiRhTOb2yRF9dGOJ/HhlddvjPcH82Tfp4xdNfbY6Et5/al4zQBAT6Sbd94tPFnvHHsCH2TNF9v7MsR7hHmTFPU544+P3nSFDwsAgB5aKtYeE56oL+x48h5ki0X1tHiPMI/Sss7ix0ev2rzzbvGaAYDDKC2a3w9P0lePPWkPtKyst8R7hHmVlc0fxI+RPrW0pfqReM0AwGGw543HeX1K/GQ98C5dyJt7xXuFeZWVa7/Q8TjpTe364jUDABus/RSfnr8W+aBK8upF8V5hniXL9cPix0mfCr8PPSdeMwCwQdo3/6Vl/XvhSfnS+El66IULnpX2xm3xnmGeZcs77xQ/VnpV2fxxvGYAYAMsblm9bztAjz05z0bfXNp69n3iPcO82/MHAkV9Rcdjpi+9NV4zADBFs/y3Bd8uK5vnxfsG9gqPkc/Ej5n+1CzF6wUApmSxWHvQDP9twd7yKo/3DfyP8Dj5yNjjpj99IF4vADBhp5xx7p3DRcE7wxPvNR1PxrPUF04+dfUO8f6B/xEeJ+/veOz0pa3xegGACWk/njRcFPxpeML9ZseT8Kx1TZo3j46PAfC/hd8T0o7HTy9Kiuqj8XoBgEM0Go2OSormuLSs/zN+8p3hXhEfB2Bc+H3hHR2Pn36U1zvj9QIAB+mUM8695b47oH527El3ttvqY0th/4TfI97W8RjqS5+M1wsAHKDFzTvuEp5U/zL09Y4n21nv/BOL824XHxOgW5rXb+x4HPWlf43XCwDsp7Rc/eEkrxbDE+qVHU+y89A3s+Ud3x8fF+CGZXn1Fx2PpX6U15+K1wsA3Ij25l5J2fxZ+yQ69sQ6X12XlfUvxccHuHFp0byy4/HUlz4drxcAiCTbtt8+Kevnt5/k0fFkOpe1n8IUHyfgO0vy6k/ix1N/av4tXi8AzL2FprlZtqX6qX1//b8jdN34k+j81r6UKj5mwP5Jivq18WOqN3lZEQCEi4G8ucW+9w+8KDxxl+FJ8tKxJ03tLa+XN62s3DQ+hsD+yYr6b8YeV33JR5kCMG9OzrffPSvXfiEtmz8OT9KjtGh2jT1B6oZaO2G061bxMQX2XxjAT+l4bPWlf47XOzEnbakevJjXj5UkaRolZfO8xaJ+cZLXL1/Mm79KiuY17Y3H9tx8rKifm5b1q8Pgf1L4982h1dB/dDwRav/7dPuRrfHzPXBg0r2/J8WPr750ZrzeicmK+oMdv6AkSRpczeey06t7xs/1wIFLy/qfxh9j/Sgr6y3xeifGxYEkSTPRf71rufme+HkeODhJUZ/T8TjrS++L1zsxLg4kSRp8X3GTM5istN8vcTw+Xu/EuDiQJGnQXejCACZrNNp1dHhsXdPxeOtFWVG9Kl7zxLg4kCRpsH3hpHL1gfFzO3Bo0q3193Y83npTUlS/G695YlwcSJI0xJrPLW5ZvW/8vA4cuqRofn78MdefwvqeHq95YlwcSJI0rMJz978sLu+4R/ycDkxGeJy9OH7c9an2Y6LjNU+MiwNJkgbVR04szrtd/HwOTE5aNCd0PPZ6U5jf7xeveWJcHEiSNJg+sLSycvP4uRyYrLSsz+h4/PWlaxea5mbxmifGxYEkSYPorZs2rR8ZP48Dk5f2+2NMvxCvd6JcHEiS1OuuCj03fv4GpmNhW3PXjsdhn/pYvOaJcnEgSVJPy6svLxVrj4mfu4HpyfL6V8ceiz0qKep3x2ueKBcHkiT1sLzemZQ77x0/bwPTFYbvvx57PPaopGz+LF7zRLk4kCSpdy144zEcHuHxt9rxmOxNi0X1tHjNE+XiQJKk3nRpmlfPjp+rgY2xkDe3CI/Dqzsem/1pa/298bonysWBJEm96LzFYu1B8fM0sHHam4t1PDb71JWbVlZuGq97olwcSJJ0WLsuLeu3HL98/jHxczSwsbKielXHY7RPnReveeJcHEiSdHgKz8Gfb/+kMn5uBg6P8Jhs4sdpnwoXL++K1zxxLg4kSTocNUsLZza3iZ+XgcNjaevZ9wmPzd3jj9UeVda/F6974lwcSJK0cSVF/e/hufdn4udj4PBK8+al8eO1byV5/aPxuifOxYEkSRvSNWlev3E02n5s/FwMHH7hMXpWx+O2T125Ie9NcnEgSdLU+0iarz40fg4G+mFhW3PXtP1wgPHHbn8q6ype91S4OJAkaTq1bzhOivqZ8XMv0C9JUf1u/PjtW+2dm+N1T4WLA0mSJt5lSdG8pr2hUvy8C/RP2v7t3vjjuFclefWL8bqnwsWBJEkT66o0r/92cfOOu8TPt0A/nbSlenDa908pKuprl04757bx2qfCxYEkSYfcdUlRvzs8p94vfp4F+i0pqhM7HtN9q47XPTUuDiRJOuiuTsv65KW8+b74+RXov/cs7/iu8Dj+Zsdju1dlRfOmeO1T4+JAkqQD7sr2TxvbmybFz6vAcCR5/cKOx3fv2tB7o7g4kCRpv/tS+0bjpbL67vj5FBiW9fX1I8Jj+pMdj/O+dclotOvoeP1T4+JAkqQbLzxXNklZ/8aGPkEDU5Xk1ePjx3pP+0C89qlycSBJUmcXh+fIdyZ5/aPxcycwfOEx/uGOx33vSormuHjtU+XiQJKk/679OMMPZ0X166PR9mPj50xgNrSv4e94/Pexa7PlnXeK1z9VLg4kSarPSvLqRSfn2+8eP08Csyc85nd0/D7Qv8r6n+K1T52LA0nSHHZ12v4NQVm/JDu9umf83AjMrvC4/6WO3xP6Wd68IF7/1Lk4kCTNSRekZZ0lRfP0dPNZt46fD4HZt2nT+pHh94LzOn5/6GNXL+TNHeM9TJ2LA0nSjHZB6P1JWT/fTcqAVlI2z+r4vaKvbY3XvyFcHEiSZqBLQx9Jivqvs7x6RrJ57f7x8x0w304549xbpkXzuY7fP/pZXv9mvIcN4eJAkjSgLguthd6Xls0fp/nqLy8Waw+Kn9sAYmlZv6Xj95S+dulhe/mjiwNJUg/6WvsneklRfTTN6+Xw70tJWb+h/QShJG+emuXNw08+dfUO8XMYwP5YLNYeEX6fubbj955eFn7vW4z3sGHaz05d2nr2fSRJmnRZ2fxYunXHo6/f0vKOH/cJQcBGGY1GR6V7/8ZxbAjva26+CAAAU5AWzcvi4bvXlfW58R4AAIBD1P4NZrr3vUrjQ3hPS/L6hfE+AACAQ7BpZeWmYdj+WDx897xL3rO847vivQAAAIeg/VCDjuG717UfxxzvAwAAOARZufYLYdjeHQ/fPe/a9mVQ8V4AAICDtJA39wqD9tc7hu++94F4LwAAwEFaaJqbhSF7R8fg3f/y1Z+I9wMAABykNK//dmzoHkYfivcCAAAcpLSs/7Bj6B5ESVn9ZLwfAADgIKR59ex0eG9A3lNW1CvxfgAAgIMQhuufCUP2VfHQPZQW8/qx8Z4AAIADlCzXDwsD9iXxwD2gPhzvCQAAOEDJ5rX7h+H6wo6Beyhdt1isPSLeFwAAcABO2lI9OAzXF3QM3IMpy6v3xvsCAAAOQPun7WG4/mo8bA+sK90NGQAADkGaN48Og/XFHcP2wKreHO8NAADYT4tl/YQwWF82PmgPrgvfs7zju+L9AQAA+2HffQyu7Bi0h1fYS7w/AADgOxiNRkdlRfOmsQF7oLnhGQAAHIRk2/bbh2H6H+IBe8Bd3X7KUrxPAADgRiyVzUPCMP2ZjgF7sGVl87p4nwAAwI3IyvrXwjB9aTxcD7tm1/HL5x8T7xUAAOjQvowoKeu/Gx+sB9+1SV7/aLxfAACgQ1au/UI68Dse31DhgucN8X4BAIBI+3n/aVln8UA9K2VF/S9LKys3j/cNAABcT1bWvxSG58/HA/UMdUX7xup43wAAwD5pvvrQMDh/uGOYnq3K+vfivQMAADdpP560+u4wNCeha8cG6dlrc7x/AACYe6PR9mOzov7TMDB/s2OInsW+sJA3d4yPAwAAzK12QE7L+tVhWP5qxwA9q13pY0sBAGCfpa1n3ycMyceHvtUxPM92efXb8fEAAIC50/6JeRiQ3xe6ZmxonoOSvFqMjwkAAMyN7PTqnmEwfkXoX+Nhec7acfzy+cfExwcAAGbaCaNdt0rL6rfSvR9HurtjUJ6zms+dcsa5d46PEwAAzKRsecf3Z2X9krSotqXz+F6CG+6i9tjExwsAAGZG+0lDSVE/My3rLAzAX+gYilXUV2dF/TPxsQMAgME6qVx9YLgI+JUw7P5lVtZb0qL5YscgrP/d7valVfGxBACA3lnIm3u1Hym652NF89WHZsXaE5OiOW7PfQfK+h2h08KAuyOdnxuTTba8eWl8zGdGVtQfHNuwJEmSpLGyvPqLeJ6eKS4OJEmSpP2pOSGepWeOiwNJkiTpxmtvcrZp0/qR8Sw9c1wcSJIkSTfawvr6+hHxHD2TXBxIkiRJ3SVFdeLcXBi0XBxIkiRJnR0fz84zz8WBJEmSFNe8Mp6b54KLA0mSJOm/uzYp6+fHM/PccHEgSZIk7enK9m7R8bw8V1wcSJIkae7Lqy8ny/Wj4ll57rg4kCRJ0pz3iaWtZ98nnpPnkosDSZIkzW15vZxuPuvW8Yw8t1wcSJIkaQ7bHebg149Go6Pi+XiuuTiQJEnSnHVxVta/FM/F3MTFgSRJkuap5uPvWm6+J56J2cfFgSRJkuaisn7HaLT92Hge5npcHEiSJGnG+0roKfEcTAcXB5IkSZrhti6V1XfHMzA3wMWBJEmSZrBLsqL5nXj25TtwcSBJkqRZKinqMju9umc897IfXBxIkiRpRvpamlfPjuddDoCLA0mSJA28a8NM+85k2/bbx7MuB8jFgSRJkgbc9ixvHh7PuBwkFweSJEkaYF9Iiua49fX1I+L5lkPg4kCSJElDKcyu38jy+uVLKys3j+daJsDFgSRJkgbQFUlZv+HE4rzbxfMsE+TiQJIkST3usrSo3ry4ecdd4jmWKXBxIEmSpB52SegvF/LmjvH8yhS5OJAkSVJfCrPp59OiednSaefcNp5b2QAuDiRJktSD/jnL618djUZHxfMqG8jFgSRJkg5TF6VFc0KyXD8snlE5TFwcSJIkaYP7SFLWvzEabT82nk05zFwcSJIkaQNaa+9PkJQ77x3Po/SIiwNJkiRNqTorqlct5TseEM+g9JSLA0mSJE2oy0Kbs7J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")}),
+ uses(PNlib(version = "2.2"), Modelica(version = "3.2.3")));
+end WindPowerPlant;
diff --git a/PowerPlants/package.mo b/PowerPlants/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..c5e6e308514fbb16bf22e579158074ae7668604c
--- /dev/null
+++ b/PowerPlants/package.mo
@@ -0,0 +1,13 @@
+within PNRG;
+
+package PowerPlants
+
+
+
+
+
+
+
+ annotation(
+ Icon(graphics = {Bitmap(origin = {-26, -40}, extent = {{-74, -46}, {74, 46}}, imageSource = 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")}));
+end PowerPlants;
diff --git a/PowerPlants/package.order b/PowerPlants/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..d85628c4015cf6b0cbef3c952a705ae1fec6d45e
--- /dev/null
+++ b/PowerPlants/package.order
@@ -0,0 +1,4 @@
+PVPowerPlant
+HydrogenCHPPlant
+STEPowerPlant
+WindPowerPlant
diff --git a/PowerToX/Electrolyser.mo b/PowerToX/Electrolyser.mo
new file mode 100644
index 0000000000000000000000000000000000000000..f64b09f1332d059790cff8d7b51d8e46277d2bb0
--- /dev/null
+++ b/PowerToX/Electrolyser.mo
@@ -0,0 +1,63 @@
+within PNRG.PowerToX;
+
+model Electrolyser
+ PNlib.Components.TC Electrolyser(arcWeightIn = {39.4, 9.1, 1}, arcWeightOut = {1.1, 8}, maximumSpeed = 1, nIn = 3, nOut = 2) annotation(
+ Placement(visible = true, transformation(origin = {0, -24}, extent = {{-10, -10}, {10, 10}}, rotation = 90)));
+ PNRG.Interfaces.HydrogenOutput H2Out annotation(
+ Placement(visible = true, transformation(origin = {110, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.OxygenOutput O2Out annotation(
+ Placement(visible = true, transformation(origin = {110, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, -40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.WaterInput WaterIn annotation(
+ Placement(visible = true, transformation(origin = {-110, -58}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, -58}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.ElectricalInput EnergyIn annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput activation annotation(
+ Placement(visible = true, transformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 58}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t1(arcWeightIn = {2}, nIn = 1) annotation(
+ Placement(visible = true, transformation(origin = {-52, 22}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-76, 42}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p12 annotation(
+ Placement(visible = true, transformation(origin = {-30, -80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticTransitionWithoutActivator energeticTransitionWithoutActivator1(arcWeightOut = {energeticTransitionWithoutActivator1.power}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-68, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p11 annotation(
+ Placement(visible = true, transformation(origin = {-30, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticTransitionWithoutActivator energeticTransitionWithoutActivator(arcWeightOut = {energeticTransitionWithoutActivator.power}, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-68, -80}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticFlowPlace p1 annotation(
+ Placement(visible = true, transformation(origin = {70, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace p13 annotation(
+ Placement(visible = true, transformation(origin = {70, -50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(splitLogicalInput.inhibitor_output, t1.inPlaces[1]) annotation(
+ Line(points = {{-66, 40}, {-64, 40}, {-64, 22}, {-57, 22}}));
+ connect(activation, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 60}, {-92, 60}, {-92, 42}, {-86, 42}}));
+ connect(splitLogicalInput.test_output, Electrolyser.inPlaces[3]) annotation(
+ Line(points = {{-66, 44}, {-36, 44}, {-36, -38}, {0, -38}, {0, -28}}));
+ connect(energeticTransitionWithoutActivator.outPlaces[1], p12.inTransition[1]) annotation(
+ Line(points = {{-64, -80}, {-40, -80}}, thickness = 0.5));
+ connect(WaterIn, energeticTransitionWithoutActivator.inPlaces[1]) annotation(
+ Line(points = {{-110, -58}, {-84, -58}, {-84, -80}, {-72, -80}}));
+ connect(EnergyIn, energeticTransitionWithoutActivator1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-86, 0}, {-86, -50}, {-72, -50}}));
+ connect(energeticTransitionWithoutActivator1.outPlaces[1], p11.inTransition[1]) annotation(
+ Line(points = {{-64, -50}, {-40, -50}}, thickness = 0.5));
+ connect(p11.outTransition[1], Electrolyser.inPlaces[1]) annotation(
+ Line(points = {{-20, -50}, {0, -50}, {0, -28}}, thickness = 0.5));
+ connect(p12.outTransition[1], Electrolyser.inPlaces[2]) annotation(
+ Line(points = {{-20, -80}, {0, -80}, {0, -28}}, thickness = 0.5));
+ connect(Electrolyser.outPlaces[2], p13.inTransition[1]) annotation(
+ Line(points = {{0, -20}, {0, 0}, {40, 0}, {40, -50}, {60, -50}}, thickness = 0.5));
+ connect(Electrolyser.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{0, -20}, {0, 50}, {60, 50}}, thickness = 0.5));
+ connect(p1.outTransition[1], H2Out) annotation(
+ Line(points = {{80, 50}, {110, 50}}));
+ connect(p13.outTransition[1], O2Out) annotation(
+ Line(points = {{80, -50}, {110, -50}}));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Diagram,
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Ellipse(origin = {-80, -60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, -60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, 0}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, 0}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, 40}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, 60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, -40}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, 40}, extent = {{-20, 20}, {20, -20}}), Bitmap(origin = {-28, 39}, extent = {{70, -61}, {-70, 61}}, imageSource = 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")}));
+end Electrolyser;
diff --git a/PowerToX/package.mo b/PowerToX/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..f313868c8a8a506ba2ae94ea5adb662f68577bb4
--- /dev/null
+++ b/PowerToX/package.mo
@@ -0,0 +1,7 @@
+within PNRG;
+
+package PowerToX
+
+ annotation(
+ Icon(graphics = {Text(origin = {66, 3}, extent = {{-86, 63}, {86, -63}}, textString = "X"), Bitmap(origin = {13, -2}, rotation = 90, extent = {{-33, -30}, {33, 30}}, imageSource = 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Bitmap(origin = {-49, -4}, extent = {{-63, -66}, {63, 66}}, imageSource = 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+end PowerToX;
diff --git a/PowerToX/package.order b/PowerToX/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..479a806c8a4d290665d43a01ee34c8176cc0ce82
--- /dev/null
+++ b/PowerToX/package.order
@@ -0,0 +1 @@
+Electrolyser
diff --git a/Sources/ConstantSource.mo b/Sources/ConstantSource.mo
new file mode 100644
index 0000000000000000000000000000000000000000..1ccfb7612e7e2c4c23b4d38219234c57382b064b
--- /dev/null
+++ b/Sources/ConstantSource.mo
@@ -0,0 +1,28 @@
+within PNRG.Sources;
+
+model ConstantSource
+ Real out "Output" annotation(
+ Dialog(enable = true, group = "Constant Output"));
+ Real cumulativeOutput "Cumulative output of File";
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ PNlib.Components.TC t12(arcWeightOut = {out}, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {14, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC p1(nIn = 1, nOut = NOut) annotation(
+ Placement(visible = true, transformation(origin = {70, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.FileOutput fileOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ cumulativeOutput = p1.t;
+ connect(t12.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{19, 0}, {60, 0}}, thickness = 0.5));
+ for i in 1:NOut loop
+ connect(p1.outTransition[i], fileOutput[i]) annotation(
+ Line(points = {{80, 0}, {110, 0}}));
+ end for;
+ annotation(
+ uses(PNlib(version = "2.2"), Modelica(version = "3.2.3")),
+ Diagram,
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Text(origin = {-1, 3}, extent = {{-91, 93}, {91, -93}}, textString = "File
+Input")}));
+end ConstantSource;
\ No newline at end of file
diff --git a/Sources/FileToTransitionOutput.mo b/Sources/FileToTransitionOutput.mo
new file mode 100644
index 0000000000000000000000000000000000000000..89d8824941168657fa6ae7d82bc8fe93dcce37c5
--- /dev/null
+++ b/Sources/FileToTransitionOutput.mo
@@ -0,0 +1,38 @@
+within PNRG.Sources;
+
+model FileToTransitionOutput
+ Real Output "Output of File";
+ Real cumulativeOutput "Cumulative output of File";
+ parameter String tableName "Name of table where data is stored" annotation(
+ Dialog(enable = true, group = "Data source"));
+ parameter String fileName "Name of file where data is stored" annotation(
+ Dialog(enable = true, group = "Data source", loadSelector(filter = "Text files (*.txt);;MATLAB MAT-files (*.mat)", caption = "Open file in which table is present")));
+ parameter Integer NOut "Number of Outputs" annotation(
+ Dialog(enable = true, group = "General properties"));
+ Modelica.Blocks.Tables.CombiTable1D combiTable1D(extrapolation = Modelica.Blocks.Types.Extrapolation.NoExtrapolation, fileName = fileName, smoothness = Modelica.Blocks.Types.Smoothness.LinearSegments, tableName = tableName, tableOnFile = true) annotation(
+ Placement(visible = true, transformation(origin = {0, 34}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Modelica.Blocks.Sources.RealExpression Idx(y = min(time - floor(time/combiTable1D.u_max)*combiTable1D.u_max, combiTable1D.u_max)) annotation(
+ Placement(visible = true, transformation(origin = {-78, 34}, extent = {{-12, -10}, {12, 10}}, rotation = 0)));
+ PNlib.Components.TC t12(arcWeightOut = {max(NOut*combiTable1D.y[1], 0)}, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {24, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.FileOutput fileOutput[NOut] annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Backend.EnergeticFlowPlace p1(nIn = 1, nOut = NOut) annotation(
+ Placement(visible = true, transformation(origin = {68, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ Output = combiTable1D.y[1];
+ cumulativeOutput = p1.t;
+ connect(Idx.y, combiTable1D.u[1]) annotation(
+ Line(points = {{-65, 34}, {-12, 34}}, color = {0, 0, 127}));
+ for i in 1:NOut loop
+ connect(p1.outTransition[i], fileOutput[i]) annotation(
+ Line(points = {{80, 0}, {110, 0}}));
+ end for;
+ connect(t12.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{28, 0}, {58, 0}}, thickness = 0.5));
+ annotation(
+ uses(PNlib(version = "2.2"), Modelica(version = "3.2.3")),
+ Diagram,
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Text(origin = {-1, 3}, extent = {{-91, 93}, {91, -93}}, textString = "File
+Input")}));
+end FileToTransitionOutput;
diff --git a/Sources/package.mo b/Sources/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..0d1d8af7fb77779ca84410d70ac521e6bf685486
--- /dev/null
+++ b/Sources/package.mo
@@ -0,0 +1,9 @@
+within PNRG;
+
+package Sources
+
+
+
+ annotation(
+ Icon(graphics = {Rectangle(origin = {-18, 0}, fillPattern = FillPattern.Solid, extent = {{-70, 20}, {70, -20}}), Polygon(origin = {62, 0}, fillPattern = FillPattern.Solid, points = {{-30, 60}, {-30, -60}, {30, 0}, {-30, 60}})}));
+end Sources;
diff --git a/Sources/package.order b/Sources/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..6183ce8516a6aae294f8d5925c627fd5da7c003f
--- /dev/null
+++ b/Sources/package.order
@@ -0,0 +1,2 @@
+FileToTransitionOutput
+ConstantSource
diff --git a/Storage/Battery.mo b/Storage/Battery.mo
new file mode 100644
index 0000000000000000000000000000000000000000..69220e4b4d6e606d04c4733516a54bf3a5ad6900
--- /dev/null
+++ b/Storage/Battery.mo
@@ -0,0 +1,97 @@
+within PNRG.Storage;
+
+model Battery
+ Boolean isEmpty;
+ Boolean isFullyCharged;
+ Boolean isCharging;
+ Boolean isDischarging;
+ Real currentInputPower;
+ Real currentOutputPower;
+ Real power "Charging and Discharging power" annotation(
+ Dialog(enable = true, group = "Properties"));
+
+ PNlib.Components.TC t1( arcWeightIn = {power*logicalInput.t*(1 - full.t), 1},arcWeightOut = {power}, firingCon = not isFullyCharged, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {78, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.ElectricalInput electricalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.ElectricalOutput electricalOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t11(arcWeightIn = {power, 1}, arcWeightOut = {power*logicalInput1.t*(1 - empty.t)}, firingCon = not isEmpty, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {36, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC storage(maxMarks = 100, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.LogicalInput logicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-72, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {72, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.TC dump(arcWeightIn = {2, 2}, nIn = 2) annotation(
+ Placement(visible = true, transformation(origin = {0, 36}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNlib.Components.PD empty(maxTokens = 1, nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-42, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD full(maxTokens = 1, minTokens = 0, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {56, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD partiallyCharged(maxTokens = 1, nIn = 2, nOut = 2) annotation(
+ Placement(visible = true, transformation(origin = {6, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t12(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t > 0.1, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-22, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t13(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t < storage.maxMarks, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {32, -34}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t14(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t == 0, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-22, -88}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t15(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t == storage.maxMarks, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {32, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ isEmpty = empty.t == 1;
+ isFullyCharged = full.t == 1;
+ isCharging = logicalInput.t == 1 and not isFullyCharged;
+ isDischarging = logicalInput1.t == 1 and not isEmpty;
+ currentInputPower = t1.arcWeightIn[1];
+ currentOutputPower = t11.arcWeightOut[1];
+ connect(electricalInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-41, 0}}));
+ connect(p1.outTransition[1], electricalOutput) annotation(
+ Line(points = {{88, 0}, {110, 0}}));
+ connect(t11.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{41, 0}, {68, 0}}, thickness = 0.5));
+ connect(t1.outPlaces[1], storage.inTransition[1]) annotation(
+ Line(points = {{-31, 0}, {-11, 0}}, thickness = 0.5));
+ connect(storage.outTransition[1], t11.inPlaces[1]) annotation(
+ Line(points = {{10, 0}, {32, 0}}, thickness = 0.5));
+ connect(logicalInput, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 60}, {-83, 60}}));
+ connect(logicalInput1, splitLogicalInput1.logicalInput) annotation(
+ Line(points = {{110, 60}, {83, 60}}));
+ connect(splitLogicalInput1.test_output, t11.inPlaces[2]) annotation(
+ Line(points = {{61, 62}, {24, 62}, {24, 0}, {32, 0}}));
+ connect(splitLogicalInput.test_output, t1.inPlaces[2]) annotation(
+ Line(points = {{-61, 62}, {-48, 62}, {-48, 0}, {-41, 0}}));
+ connect(splitLogicalInput.inhibitor_output, dump.inPlaces[1]) annotation(
+ Line(points = {{-61, 58}, {0, 58}, {0, 40}}));
+ connect(splitLogicalInput1.inhibitor_output, dump.inPlaces[2]) annotation(
+ Line(points = {{61, 58}, {0, 58}, {0, 40}}));
+ connect(empty.outTransition[1], t12.inPlaces[1]) annotation(
+ Line(points = {{-31, -60}, {-27.2, -60}}, thickness = 0.5));
+ connect(t12.outPlaces[1], partiallyCharged.inTransition[1]) annotation(
+ Line(points = {{-17.2, -60}, {-5.2, -60}}, thickness = 0.5));
+ connect(partiallyCharged.outTransition[1], t15.inPlaces[1]) annotation(
+ Line(points = {{16.8, -60}, {26.8, -60}}, thickness = 0.5));
+ connect(t15.outPlaces[1], full.inTransition[1]) annotation(
+ Line(points = {{36.8, -60}, {44.8, -60}}, thickness = 0.5));
+ connect(partiallyCharged.outTransition[2], t14.inPlaces[1]) annotation(
+ Line(points = {{16.8, -60}, {21.8, -60}, {21.8, -88}, {-17.2, -88}}, thickness = 0.5));
+ connect(t14.outPlaces[1], empty.inTransition[1]) annotation(
+ Line(points = {{-26.8, -88}, {-57.8, -88}, {-57.8, -60}, {-53, -60}}, thickness = 0.5));
+ connect(full.outTransition[1], t13.inPlaces[1]) annotation(
+ Line(points = {{66.8, -60}, {72.3, -60}, {72.3, -34}, {36.8, -34}}, thickness = 0.5));
+ connect(t13.outPlaces[1], partiallyCharged.inTransition[2]) annotation(
+ Line(points = {{27.2, -34}, {-9.3, -34}, {-9.3, -60}, {-4.8, -60}}, thickness = 0.5));
+ annotation(
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {80, -1}, rotation = 180, extent = {{17, 31}, {-17, -31}}, imageSource = "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")}),
+ Diagram(coordinateSystem(extent = {{-120, 80}, {120, -220}})));
+end Battery;
diff --git a/Storage/H2Tank.mo b/Storage/H2Tank.mo
new file mode 100644
index 0000000000000000000000000000000000000000..99dd3ae6f4b5ff27b6242f9f307fbf7bbb01ba68
--- /dev/null
+++ b/Storage/H2Tank.mo
@@ -0,0 +1,82 @@
+within PNRG.Storage;
+
+model H2Tank
+ Interfaces.HydrogenInput hydrogenInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.HydrogenOutput hydrogenOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC dump(arcWeightIn = {2, 2}, nIn = 2) annotation(
+ Placement(visible = true, transformation(origin = {-4, 36}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNlib.Components.PD partiallyFilled(maxTokens = 1, nIn = 2, nOut = 2) annotation(
+ Placement(visible = true, transformation(origin = {2, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD full(maxTokens = 1, minTokens = 0, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {52, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Logics.SplitLogicalInput splitLogicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {68, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t14(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t == 0, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-26, -88}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-76, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC storage(maxMarks = 100, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-4, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t1(arcWeightIn = {maxInputMassFlow*logicalInput.t*(1 - full.t), 1}, arcWeightOut = {maxInputMassFlow}, firingCon = not isFullyCharged, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-40, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t12(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t > 0.1, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-26, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD empty(maxTokens = 1, nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-46, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t15(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t == storage.maxMarks, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {28, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t13(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t < storage.maxMarks, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {28, -34}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {74, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t11(arcWeightIn = {maxOutputMassFlow, 1}, arcWeightOut = {maxOutputMassFlow*logicalInput1.t*(1 - empty.t)}, firingCon = not isEmpty, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {32, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.LogicalInput logicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(splitLogicalInput1.logicalInput, logicalInput1) annotation(
+ Line(points = {{80, 60}, {110, 60}}, color = {53, 28, 117}));
+ connect(t12.outPlaces[1], partiallyFilled.inTransition[1]) annotation(
+ Line(points = {{-21.2, -60}, {-9.2, -60}}, thickness = 0.5));
+ connect(partiallyFilled.outTransition[1], t15.inPlaces[1]) annotation(
+ Line(points = {{12.8, -60}, {22.8, -60}}, thickness = 0.5));
+ connect(full.outTransition[1], t13.inPlaces[1]) annotation(
+ Line(points = {{62.8, -60}, {68.3, -60}, {68.3, -34}, {32.8, -34}}, thickness = 0.5));
+ connect(splitLogicalInput.test_output, t1.inPlaces[2]) annotation(
+ Line(points = {{-65.2, 62}, {-52.2, 62}, {-52.2, 0}, {-45.2, 0}}));
+ connect(splitLogicalInput1.inhibitor_output, dump.inPlaces[2]) annotation(
+ Line(points = {{57.2, 58}, {-3.8, 58}, {-3.8, 40}}));
+ connect(partiallyFilled.outTransition[2], t14.inPlaces[1]) annotation(
+ Line(points = {{12.8, -60}, {17.8, -60}, {17.8, -88}, {-21.2, -88}}, thickness = 0.5));
+ connect(splitLogicalInput1.test_output, t11.inPlaces[2]) annotation(
+ Line(points = {{57.2, 62}, {20.2, 62}, {20.2, 0}, {28.2, 0}}));
+ connect(storage.outTransition[1], t11.inPlaces[1]) annotation(
+ Line(points = {{6.8, 0}, {28.8, 0}}, thickness = 0.5));
+ connect(t14.outPlaces[1], empty.inTransition[1]) annotation(
+ Line(points = {{-30.8, -88}, {-61.8, -88}, {-61.8, -60}, {-57, -60}}, thickness = 0.5));
+ connect(t15.outPlaces[1], full.inTransition[1]) annotation(
+ Line(points = {{32.8, -60}, {40.8, -60}}, thickness = 0.5));
+ connect(splitLogicalInput.inhibitor_output, dump.inPlaces[1]) annotation(
+ Line(points = {{-65.2, 58}, {-4.2, 58}, {-4.2, 40}}));
+ connect(empty.outTransition[1], t12.inPlaces[1]) annotation(
+ Line(points = {{-35.2, -60}, {-31.4, -60}}, thickness = 0.5));
+ connect(t11.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{36.8, 0}, {63.8, 0}}, thickness = 0.5));
+ connect(t1.outPlaces[1], storage.inTransition[1]) annotation(
+ Line(points = {{-35.2, 0}, {-15.2, 0}}, thickness = 0.5));
+ connect(t13.outPlaces[1], partiallyFilled.inTransition[2]) annotation(
+ Line(points = {{23.2, -34}, {-13.3, -34}, {-13.3, -60}, {-8.8, -60}}, thickness = 0.5));
+ connect(logicalInput, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 60}, {-86, 60}}));
+ connect(hydrogenInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-44, 0}}));
+ connect(p1.outTransition[1], hydrogenOutput) annotation(
+ Line(points = {{84, 0}, {110, 0}}));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {1, 0}, extent = {{101, -82}, {-101, 82}}, imageSource = 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")}));
+end H2Tank;
diff --git a/Storage/O2Tank.mo b/Storage/O2Tank.mo
new file mode 100644
index 0000000000000000000000000000000000000000..eb341f4a7c3270b71e0c9fa2ba0ea701bfb022dc
--- /dev/null
+++ b/Storage/O2Tank.mo
@@ -0,0 +1,82 @@
+within PNRG.Storage;
+
+model O2Tank
+ Interfaces.OxygenOutput oxygenOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.OxygenInput oxygenInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC dump(arcWeightIn = {2, 2}, nIn = 2) annotation(
+ Placement(visible = true, transformation(origin = {-4, 36}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ PNlib.Components.PD partiallyFilled(maxTokens = 1, nIn = 2, nOut = 2) annotation(
+ Placement(visible = true, transformation(origin = {2, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD full(maxTokens = 1, minTokens = 0, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {52, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Logics.SplitLogicalInput splitLogicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {68, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t14(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t == 0, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-26, -88}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-76, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC storage(maxMarks = 100, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-4, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t1(arcWeightIn = {maxInputMassFlow*logicalInput.t*(1 - full.t), 1}, arcWeightOut = {maxInputMassFlow}, firingCon = not isFullyCharged, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-40, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t12(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t > 0.1, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-26, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD empty(maxTokens = 1, nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-46, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t15(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t == storage.maxMarks, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {28, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t13(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t < storage.maxMarks, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {28, -34}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {74, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t11(arcWeightIn = {maxOutputMassFlow, 1}, arcWeightOut = {maxOutputMassFlow*logicalInput1.t*(1 - empty.t)}, firingCon = not isEmpty, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {32, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ Interfaces.LogicalInput logicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(splitLogicalInput1.logicalInput, logicalInput1) annotation(
+ Line(points = {{80, 60}, {110, 60}}, color = {53, 28, 117}));
+ connect(t12.outPlaces[1], partiallyFilled.inTransition[1]) annotation(
+ Line(points = {{-21.2, -60}, {-9.2, -60}}, thickness = 0.5));
+ connect(partiallyFilled.outTransition[1], t15.inPlaces[1]) annotation(
+ Line(points = {{12.8, -60}, {22.8, -60}}, thickness = 0.5));
+ connect(full.outTransition[1], t13.inPlaces[1]) annotation(
+ Line(points = {{62.8, -60}, {68.3, -60}, {68.3, -34}, {32.8, -34}}, thickness = 0.5));
+ connect(splitLogicalInput.test_output, t1.inPlaces[2]) annotation(
+ Line(points = {{-65.2, 62}, {-52.2, 62}, {-52.2, 0}, {-45.2, 0}}));
+ connect(splitLogicalInput1.inhibitor_output, dump.inPlaces[2]) annotation(
+ Line(points = {{57.2, 58}, {-3.8, 58}, {-3.8, 40}}));
+ connect(partiallyFilled.outTransition[2], t14.inPlaces[1]) annotation(
+ Line(points = {{12.8, -60}, {17.8, -60}, {17.8, -88}, {-21.2, -88}}, thickness = 0.5));
+ connect(splitLogicalInput1.test_output, t11.inPlaces[2]) annotation(
+ Line(points = {{57.2, 62}, {20.2, 62}, {20.2, 0}, {28.2, 0}}));
+ connect(storage.outTransition[1], t11.inPlaces[1]) annotation(
+ Line(points = {{6.8, 0}, {28.8, 0}}, thickness = 0.5));
+ connect(t14.outPlaces[1], empty.inTransition[1]) annotation(
+ Line(points = {{-30.8, -88}, {-61.8, -88}, {-61.8, -60}, {-57, -60}}, thickness = 0.5));
+ connect(t15.outPlaces[1], full.inTransition[1]) annotation(
+ Line(points = {{32.8, -60}, {40.8, -60}}, thickness = 0.5));
+ connect(splitLogicalInput.inhibitor_output, dump.inPlaces[1]) annotation(
+ Line(points = {{-65.2, 58}, {-4.2, 58}, {-4.2, 40}}));
+ connect(empty.outTransition[1], t12.inPlaces[1]) annotation(
+ Line(points = {{-35.2, -60}, {-31.4, -60}}, thickness = 0.5));
+ connect(t11.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{36.8, 0}, {63.8, 0}}, thickness = 0.5));
+ connect(t1.outPlaces[1], storage.inTransition[1]) annotation(
+ Line(points = {{-35.2, 0}, {-15.2, 0}}, thickness = 0.5));
+ connect(t13.outPlaces[1], partiallyFilled.inTransition[2]) annotation(
+ Line(points = {{23.2, -34}, {-13.3, -34}, {-13.3, -60}, {-8.8, -60}}, thickness = 0.5));
+ connect(logicalInput, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 60}, {-86, 60}}));
+ connect(oxygenInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-44, 0}}));
+ connect(p1.outTransition[1], oxygenOutput) annotation(
+ Line(points = {{84, 0}, {110, 0}}));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {1, 0}, extent = {{101, -82}, {-101, 82}}, imageSource = 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")}));
+end O2Tank;
diff --git a/Storage/WaterTank.mo b/Storage/WaterTank.mo
new file mode 100644
index 0000000000000000000000000000000000000000..5043ecb10a94e1fd94b7292d0f92175e361ed87a
--- /dev/null
+++ b/Storage/WaterTank.mo
@@ -0,0 +1,87 @@
+within PNRG.Storage;
+
+model WaterTank
+ Real maxInputMassFlow "maxInputFlow" annotation(
+ Dialog(enable = true, group = "Properties"));
+ Real maxOutputMassFlow "maxInputFlow" annotation(
+ Dialog(enable = true, group = "Properties"));
+
+ PNRG.Interfaces.WaterInput waterInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Interfaces.WaterOutput waterOutput annotation(
+ Placement(visible = true, transformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PC storage(maxMarks = 100, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-4, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD full(maxTokens = 1, minTokens = 0, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {52, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC t11(arcWeightIn = {maxOutputMassFlow, 1}, arcWeightOut = {maxOutputMassFlow*logicalInput1.t*(1 - empty.t)}, firingCon = not isEmpty, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {32, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput annotation(
+ Placement(visible = true, transformation(origin = {-76, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t15(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t == storage.maxMarks, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {28, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNRG.Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {74, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD partiallyFilled(maxTokens = 1, nIn = 2, nOut = 2) annotation(
+ Placement(visible = true, transformation(origin = {2, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.PD empty(maxTokens = 1, nIn = 1, nOut = 1, startTokens = 1) annotation(
+ Placement(visible = true, transformation(origin = {-46, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t14(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t == 0, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-26, -88}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.T t12(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t > 0.1, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-26, -60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.T t13(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t < storage.maxMarks, nIn = 1, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {28, -34}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNRG.Logics.SplitLogicalInput splitLogicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {68, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ PNlib.Components.TC t1(arcWeightIn = {maxInputMassFlow*logicalInput.t*(1 - full.t), 1}, arcWeightOut = {maxInputMassFlow}, firingCon = not isFullyCharged, nIn = 2, nOut = 1) annotation(
+ Placement(visible = true, transformation(origin = {-40, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+ PNlib.Components.TC dump(arcWeightIn = {2, 2}, nIn = 2) annotation(
+ Placement(visible = true, transformation(origin = {-4, 36}, extent = {{-10, -10}, {10, 10}}, rotation = -90)));
+ Interfaces.LogicalInput logicalInput1 annotation(
+ Placement(visible = true, transformation(origin = {110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, 60}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+ Interfaces.LogicalInput logicalInput annotation(
+ Placement(visible = true, transformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {-110, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+ connect(t13.outPlaces[1], partiallyFilled.inTransition[2]) annotation(
+ Line(points = {{23.2, -34}, {-13.3, -34}, {-13.3, -60}, {-8.8, -60}}, thickness = 0.5));
+ connect(t15.outPlaces[1], full.inTransition[1]) annotation(
+ Line(points = {{32.8, -60}, {40.8, -60}}, thickness = 0.5));
+ connect(t1.outPlaces[1], storage.inTransition[1]) annotation(
+ Line(points = {{-35.2, 0}, {-15.2, 0}}, thickness = 0.5));
+ connect(empty.outTransition[1], t12.inPlaces[1]) annotation(
+ Line(points = {{-35.2, -60}, {-31.4, -60}}, thickness = 0.5));
+ connect(t11.outPlaces[1], p1.inTransition[1]) annotation(
+ Line(points = {{36.8, 0}, {63.8, 0}}, thickness = 0.5));
+ connect(splitLogicalInput1.inhibitor_output, dump.inPlaces[2]) annotation(
+ Line(points = {{57.2, 58}, {-3.8, 58}, {-3.8, 40}}));
+ connect(splitLogicalInput.inhibitor_output, dump.inPlaces[1]) annotation(
+ Line(points = {{-65.2, 58}, {-4.2, 58}, {-4.2, 40}}));
+ connect(storage.outTransition[1], t11.inPlaces[1]) annotation(
+ Line(points = {{6.8, 0}, {28.8, 0}}, thickness = 0.5));
+ connect(full.outTransition[1], t13.inPlaces[1]) annotation(
+ Line(points = {{62.8, -60}, {68.3, -60}, {68.3, -34}, {32.8, -34}}, thickness = 0.5));
+ connect(t14.outPlaces[1], empty.inTransition[1]) annotation(
+ Line(points = {{-30.8, -88}, {-61.8, -88}, {-61.8, -60}, {-57, -60}}, thickness = 0.5));
+ connect(partiallyFilled.outTransition[2], t14.inPlaces[1]) annotation(
+ Line(points = {{12.8, -60}, {17.8, -60}, {17.8, -88}, {-21.2, -88}}, thickness = 0.5));
+ connect(t12.outPlaces[1], partiallyFilled.inTransition[1]) annotation(
+ Line(points = {{-21.2, -60}, {-9.2, -60}}, thickness = 0.5));
+ connect(splitLogicalInput1.test_output, t11.inPlaces[2]) annotation(
+ Line(points = {{57.2, 62}, {20.2, 62}, {20.2, 0}, {28.2, 0}}));
+ connect(splitLogicalInput.test_output, t1.inPlaces[2]) annotation(
+ Line(points = {{-65.2, 62}, {-52.2, 62}, {-52.2, 0}, {-45.2, 0}}));
+ connect(partiallyFilled.outTransition[1], t15.inPlaces[1]) annotation(
+ Line(points = {{12.8, -60}, {22.8, -60}}, thickness = 0.5));
+ connect(logicalInput, splitLogicalInput.logicalInput) annotation(
+ Line(points = {{-110, 60}, {-86, 60}}));
+ connect(splitLogicalInput1.logicalInput, logicalInput1) annotation(
+ Line(points = {{80, 60}, {110, 60}}, color = {53, 28, 117}));
+ connect(waterInput, t1.inPlaces[1]) annotation(
+ Line(points = {{-110, 0}, {-44, 0}}));
+ connect(p1.outTransition[1], waterOutput) annotation(
+ Line(points = {{84, 0}, {110, 0}}));
+ annotation(
+ uses(PNlib(version = "2.2")),
+ Icon(graphics = {Rectangle(fillColor = {255, 255, 255}, fillPattern = FillPattern.Solid, extent = {{-100, 100}, {100, -100}}), Bitmap(origin = {1, 0}, extent = {{101, -82}, {-101, 82}}, imageSource = 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+end WaterTank;
diff --git a/Storage/package.mo b/Storage/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..8e0b59d80e2dbcf8dd165ce4f95e20271f1af403
--- /dev/null
+++ b/Storage/package.mo
@@ -0,0 +1,13 @@
+within PNRG;
+
+package Storage
+
+
+
+
+
+
+
+ annotation(
+ Icon(graphics = {Bitmap(origin = {3, 50}, extent = {{-91, -84}, {91, 84}}, imageSource = 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3LjcJO1S+Kc7K3hB+vqgkSZKkdl2V9SsrK3cqNwj8P6urq2f0ff9r+Ub5ysAbSJIkSZIO1+Kf/L0vd8UTyr0Bh7T4O9pd1z0+3zz/Pvv2wBtLkiRJkm7f57MLs/uU+wKOyu7du++ag3R3vpnen+0feLNJkiRJ2pldk12SPbLcEdDEbDa7V77B/kXf92/MX/9i4E0oSZIkaXlbfKHpf81ekpvgoeVegBNuY2Pj3rf9KeniZ49+YeBNKkmSJGm63ZBj84P5mf+i7Mn5+f/UchPAttqzZ8895vP5w/KN+i/zDfvyfKO+I3+9Ivy7UkmSJGnMfS0/w/9Z9rv5+wvzc/zTFj/Osfy8D5Mym83+fr6Rz8w39SPz13MX/09K/v787AWL/3clf32JJEmSpHbl5+y3ZJfn738ve3Yc/Px9fv5vj158Js/fn+XHpwAAAAAAAAAAAAAAAAAAAAAAAEzc/wWtBA3wWxThzwAAAABJRU5ErkJggg==")}));
+end Storage;
diff --git a/Storage/package.order b/Storage/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..113be9070b38bc40c99f2af175d73f668641ae59
--- /dev/null
+++ b/Storage/package.order
@@ -0,0 +1,4 @@
+WaterTank
+H2Tank
+O2Tank
+Battery
diff --git a/data.txt b/data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0e614fa37bd584183cc44b4214522eb68339800e
--- /dev/null
+++ b/data.txt
@@ -0,0 +1,27 @@
+#1
+double tab1(24,2)
+ 0 0
+ 1 0.01
+ 2 0.04
+ 3 0.09
+ 4 0.16
+ 5 0.24
+ 6 0.27
+ 7 0.28
+ 8 0.27
+ 9 0.24
+ 10 0.16
+ 11 0.04
+ 12 0.01
+ 13 0
+ 14 0
+ 15 0
+ 16 0
+ 17 0
+ 18 0
+ 19 0
+ 20 0
+ 21 0
+ 22 0
+ 23 0
+
\ No newline at end of file
diff --git a/data2.txt b/data2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ec05d5a17bbea4dc36d7429ebf207903c97375c4
--- /dev/null
+++ b/data2.txt
@@ -0,0 +1,27 @@
+#1
+double tab1(24,2)
+ 0 0
+ 1 1
+ 2 2
+ 3 3
+ 4 4
+ 5 5
+ 6 6
+ 7 7
+ 8 8
+ 9 9
+ 10 10
+ 11 11
+ 12 12
+ 13 13
+ 14 14
+ 15 0
+ 16 0
+ 17 0
+ 18 0
+ 19 1
+ 20 3
+ 21 6
+ 22 0
+ 23 0
+
\ No newline at end of file
diff --git a/data3.txt b/data3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..63e1112a4f93bee4ad7d5a5d5d7c0e9e6cfd67c3
--- /dev/null
+++ b/data3.txt
@@ -0,0 +1,27 @@
+#1
+double tab1(24,2)
+ 0 2
+ 1 2
+ 2 2
+ 3 3
+ 4 4
+ 5 5
+ 6 6
+ 7 4
+ 8 8
+ 9 9
+ 10 18
+ 11 18
+ 12 18
+ 13 13
+ 14 14
+ 15 2
+ 16 2
+ 17 0
+ 18 0
+ 19 1
+ 20 3
+ 21 6
+ 22 0
+ 23 1
+
\ No newline at end of file
diff --git a/data4.txt b/data4.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ae085404420c79dcf265a9f88bec7e0243258226
--- /dev/null
+++ b/data4.txt
@@ -0,0 +1,27 @@
+#1
+double tab1(24,2)
+ 0 1
+ 1 2
+ 2 3
+ 3 4
+ 4 3
+ 5 2
+ 6 1
+ 7 1
+ 8 1
+ 9 2
+ 10 3
+ 11 4
+ 12 3
+ 13 2
+ 14 1
+ 15 1
+ 16 1
+ 17 2
+ 18 3
+ 19 4
+ 20 3
+ 21 2
+ 22 1
+ 23 1
+
\ No newline at end of file
diff --git a/package.mo b/package.mo
new file mode 100644
index 0000000000000000000000000000000000000000..81c4c1bb84a4fcbbd0c59ada9b8c409b51be2e24
--- /dev/null
+++ b/package.mo
@@ -0,0 +1,6 @@
+package PNRG
+ import Modelica.Units.SI;
+ annotation(
+ uses(PNlib(version = "2.2"), Modelica(version = "3.2.3")),
+ Icon(graphics = {Bitmap(origin = {0, -3}, extent = {{-118, -101}, {118, 101}}, imageSource = 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")}));
+end PNRG;
diff --git a/package.order b/package.order
new file mode 100644
index 0000000000000000000000000000000000000000..41294a94f32cc64f8f006e0dd70259ce52d563c4
--- /dev/null
+++ b/package.order
@@ -0,0 +1,10 @@
+Examples
+PowerPlants
+EnergyConsumer
+PowerToX
+Storage
+Distribution
+Logics
+Sources
+Interfaces
+Backend