diff --git a/Backend/EnergeticFlowPlace.mo b/Backend/EnergeticFlowPlace.mo
index fdc121986a5040d8892ea83b2ae0c6bf3714f3e0..a46697867a414fb44acf7235d329c4ca0390fd44 100644
--- a/Backend/EnergeticFlowPlace.mo
+++ b/Backend/EnergeticFlowPlace.mo
@@ -26,6 +26,8 @@ model EnergeticFlowPlace
   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)";
+  Real actualPower "Power (sum of arcWeightIn weighted with speed)";
+  Real actualPowers[nIn] "Power (sum of arcWeightIn weighted with speed)";
   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****//
@@ -44,7 +46,7 @@ model EnergeticFlowPlace
 protected
   outer PNlib.Components.Settings settings "global settings for animation and display";
   Real minMarks = 0 "minimum capacity";
-  Real maxMarks = 0 "maximum capacity";
+  Real maxMarks = 10e-15 "maximum capacity";
   Real reStartMarks = 0 "number of marks at restart";
   Real disMarkChange "discrete mark change";
   Real conMarkChange "continuous mark change";
@@ -84,7 +86,7 @@ protected
   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);
+  PNlib.Blocks.firingSumCon firingSumOut(fire = preFireOut, arcWeight = arcWeightOut, instSpeed = maxSpeedOut, disTransition = disTransitionOut);
   //****BLOCKS END****//
   Real decFactorIn[nIn] "decreasing factors for input transitions";
   Real decFactorOut[nOut] "decreasing factors for output transitions";
@@ -140,9 +142,13 @@ public
 
 equation
   for i in 1:nOut loop
-    arcWeightOut[i] = arcWeightOutSplit[i] * power;
+    arcWeightOut[i] = arcWeightOutSplit[i] * actualPower;
+  end for;
+  for i in 1:nIn loop
+    actualPowers[i] = if fireIn[i] then arcWeightIn[i] * instSpeedIn[i] / maxSpeedIn[i] else 0;
   end for;
   power = sum(arcWeightIn);
+  actualPower = sum(actualPowers);
 //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
diff --git a/Backend/EnergeticTransitionWithoutActivator.mo b/Backend/EnergeticTransitionWithoutActivator.mo
index c7654d529feac30b0f6d8799ab98f8b525a2b065..1fae1c73e35d04aec566151b7fdfbf644081d4d1 100644
--- a/Backend/EnergeticTransitionWithoutActivator.mo
+++ b/Backend/EnergeticTransitionWithoutActivator.mo
@@ -9,6 +9,7 @@ model EnergeticTransitionWithoutActivator
     Dialog(enable = true, group = "Connector sizing"));
   //****MODIFIABLE PARAMETERS AND VARIABLES BEGIN****//
   Real power;
+  Real powers[nIn];
   Real maximumSpeed = 1 "maximum speed" annotation(
     Dialog(enable = true, group = "Maximum Speed"));
   Real arcWeightOut[nOut] = fill(1, nOut) "arc weights of output places" annotation(
@@ -56,14 +57,14 @@ protected
   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);
+  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[1:nIn], 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);
+  prelimSpeed = PNlib.Functions.preliminarySpeed(nIn = nIn, nOut = nOut, arcWeightIn = arcWeightIn[1:nIn], 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
@@ -74,6 +75,7 @@ equation
 //****ANIMATION END****//
 //****ERROR MESSENGES BEGIN****//  hier noch Message gleiches Kantengewicht und auch Kante dis Place!!
   power = sum(arcWeightIn[1:nIn]);
+  powers = arcWeightIn[1:nIn];
   for i in 1:nIn loop
     if disPlaceIn[i] then
       arcWeightIntIn[i] = integer(arcWeightIn[i]);
diff --git a/Distribution/BaseTransmissionLine.mo b/Distribution/BaseTransmissionLine.mo
index a3906e7d7b75271b989473561fb19ef17af19e32..5f48cb7b0944adca156605c0e73ae95d200b1e75 100644
--- a/Distribution/BaseTransmissionLine.mo
+++ b/Distribution/BaseTransmissionLine.mo
@@ -16,12 +16,12 @@ model BaseTransmissionLine
     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(
+  PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, maximumSpeed = 1/3600, 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;
+  powerInput = t1.power*t1.actualSpeed./t1.maximumSpeed;
   totalLoad = sum(electricalOutput.arcWeight);
   powerDifference = powerInput - totalLoad;
   for i in 1:NIn loop
diff --git a/Distribution/CO2Pipe.mo b/Distribution/CO2Pipe.mo
new file mode 100644
index 0000000000000000000000000000000000000000..fbb6e37af6d98d062ee7fed73a13698733e1c1da
--- /dev/null
+++ b/Distribution/CO2Pipe.mo
@@ -0,0 +1,37 @@
+within PNRG.Distribution;
+
+model CO2Pipe
+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"));
+  Real massInput(unit = "kg");
+  Real totalLoad(unit = "kg");
+  PNRG.Interfaces.CO2Input co2Input[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.CO2Output co2Output[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 = {massInput}, maximumSpeed = 1/3600, 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
+  massInput = t1.power*t1.actualSpeed./t1.maximumSpeed;
+  totalLoad = sum(co2Output.arcWeight);
+  for i in 1:NIn loop
+    connect(co2Input[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(co2Output[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 = {54, 54, 54}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Rectangle(origin = {-2, 79}, fillColor = {54, 54, 54}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-80, 15}, {80, -15}}), Ellipse(origin = {78, 79}, fillColor = {54, 54, 54}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Ellipse(origin = {78, 79}, fillColor = {15, 15, 15}, 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 CO2Pipe;
diff --git a/Distribution/ConditionalConnection.mo b/Distribution/ConditionalConnection.mo
index 7c94a01d5b6667550e75df7a1da198e58ecf8ad8..c9a0828a616280544d0e8285e978ca7bde48c584 100644
--- a/Distribution/ConditionalConnection.mo
+++ b/Distribution/ConditionalConnection.mo
@@ -14,9 +14,9 @@ model ConditionalConnection
     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(
+  PNlib.Components.TC t1(arcWeightIn = {currentPower}, arcWeightOut = {currentPower}, firingCon = logicalInput.t == 1, maximumSpeed = 1/3600, 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(
+  PNlib.Components.TC t11(arcWeightIn = {2, 2}, firingCon = false, maximumSpeed = 1/3600, nIn = 2)  annotation(
     Placement(visible = true, transformation(origin = {26, -56}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
 equation
   currentPower = power*logicalInput.t;
diff --git a/Distribution/DistributionPowerGrid.mo b/Distribution/DistributionPowerGrid.mo
index c1dc50700fc8118402dd3094b44be0990cdc698d..c0b835ed1e3ea937146dbc7bc1f1a6f847e2ef7b 100644
--- a/Distribution/DistributionPowerGrid.mo
+++ b/Distribution/DistributionPowerGrid.mo
@@ -1,30 +1,38 @@
 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 Voltage(unit = "V") = 1 "Voltage of grid";
   Real powerDifference(unit = "kW") "Difference between power suply and consumption";
   Real powerInput(unit = "kW");
   Real totalLoad(unit = "kW");
+  
+  Real powerInputs[NIn];
+  Real powerInputsRaw[NIn];
+  Real powerRatio[NIn];
+  
   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(
+  PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, maximumSpeed = 1/3600, 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;
+  powerInput = sum(powerInputs);//*t1.power*t1.actualSpeed./t1.maximumSpeed;
   totalLoad = sum(electricalOutput.arcWeight);
   powerDifference = powerInput - totalLoad;
   for i in 1:NIn loop
+    powerInputs[i] = electricalInput[i].arcWeight*electricalInput[i].instSpeed/electricalInput[i].maxSpeed;
+    powerInputsRaw[i] = electricalInput[i].arcWeight;
+    powerRatio[i] = electricalInput[i].instSpeed/electricalInput[i].maxSpeed;
     connect(electricalInput[i], t1.inPlaces[i]) annotation(
       Line(points = {{-31.2, 0}, {34.8, 0}}, thickness = 0.5));
   end for;
diff --git a/Distribution/HeatPipe.mo b/Distribution/HeatPipe.mo
new file mode 100644
index 0000000000000000000000000000000000000000..7d925eb2937d58999bb33b1e67f2c91712760a1e
--- /dev/null
+++ b/Distribution/HeatPipe.mo
@@ -0,0 +1,41 @@
+within PNRG.Distribution;
+
+model HeatPipe
+
+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"));
+  Real powerDifference(unit = "kW") "Difference between power suply and consumption";
+  Real powerInput(unit = "kW");
+  Real totalLoad(unit = "kW");
+  PNRG.Interfaces.HeatInput heatInput[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.HeatOutput heatOutput[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}, maximumSpeed = 1/3600, 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*t1.actualSpeed./t1.maximumSpeed;
+  totalLoad = sum(heatOutput.arcWeight);
+  powerDifference = powerInput - totalLoad;
+  for i in 1:NIn loop
+    connect(heatInput[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(heatOutput[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 = {255, 80, 50}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Rectangle(origin = {-2, 79}, fillColor = {255, 80, 50}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-80, 15}, {80, -15}}), Ellipse(origin = {78, 79}, fillColor = {255, 80, 50}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Ellipse(origin = {78, 79}, fillColor = {195, 59, 38}, 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 HeatPipe;
diff --git a/Distribution/HydrogenPipe.mo b/Distribution/HydrogenPipe.mo
index 3f22655bbe16527a674037a7a0b1c8a79ff16e22..45e4f9e4fe44b2c6bbbc02c8662680a0ffab3d0e 100644
--- a/Distribution/HydrogenPipe.mo
+++ b/Distribution/HydrogenPipe.mo
@@ -8,8 +8,6 @@ parameter Integer NIn "Number of Inputs" 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");
@@ -17,12 +15,12 @@ parameter Integer NIn "Number of Inputs" 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(
+  PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, maximumSpeed = 1/3600, 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;
+  powerInput = t1.power*t1.actualSpeed./t1.maximumSpeed;
   totalLoad = sum(hydrogenOutput.arcWeight);
   powerDifference = powerInput - totalLoad;
   for i in 1:NIn loop
diff --git a/Distribution/NaturalGasPipe.mo b/Distribution/NaturalGasPipe.mo
new file mode 100644
index 0000000000000000000000000000000000000000..9f740c7a2590749003b37157511556d6229cbce6
--- /dev/null
+++ b/Distribution/NaturalGasPipe.mo
@@ -0,0 +1,37 @@
+within PNRG.Distribution;
+
+model NaturalGasPipe
+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"));
+  Real massInput(unit = "kg");
+  Real totalLoad(unit = "kg");
+  PNRG.Interfaces.NaturalGasInput naturalGasInput[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.NaturalGasOutput naturalGasOutput[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 = {massInput}, maximumSpeed = 1/3600, 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
+  massInput = t1.power*t1.actualSpeed./t1.maximumSpeed;
+  totalLoad = sum(naturalGasOutput.arcWeight);
+  for i in 1:NIn loop
+    connect(naturalGasInput[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(naturalGasOutput[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}, lineColor = {126, 58, 0}, fillColor = {126, 58, 0}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Rectangle(origin = {-2, 79}, lineColor = {126, 58, 0}, fillColor = {126, 58, 0}, pattern = LinePattern.None, fillPattern = FillPattern.Solid, extent = {{-80, 15}, {80, -15}}), Ellipse(origin = {78, 79}, fillColor = {126, 58, 0}, fillPattern = FillPattern.Solid, extent = {{-10, 15}, {10, -15}}), Ellipse(origin = {78, 79}, fillColor = {81, 36, 0}, 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 NaturalGasPipe;
diff --git a/Distribution/OxygenPipe.mo b/Distribution/OxygenPipe.mo
index 60579bc26d2c7cd3d35c941fcb9aa78ccfbe1038..f7721b52d132bc6a333c8df9c7997be2fa5a5042 100644
--- a/Distribution/OxygenPipe.mo
+++ b/Distribution/OxygenPipe.mo
@@ -7,23 +7,19 @@ parameter Integer NIn "Number of Inputs" 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");
+  Real massInput(unit = "kg");
+  Real totalLoad(unit = "kg");
   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(
+  PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {massInput}, maximumSpeed = 1/3600, 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;
+  massInput = t1.power*t1.actualSpeed./t1.maximumSpeed;
   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));
diff --git a/Distribution/Transformer.mo b/Distribution/Transformer.mo
index 0cbc5bbbd962e5d0b2f6442dca9d1c9d0ee7cce8..a1b14b955e547343cfc724a05df68ec02ab719b1 100644
--- a/Distribution/Transformer.mo
+++ b/Distribution/Transformer.mo
@@ -3,6 +3,7 @@ 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(
@@ -11,7 +12,7 @@ model Transformer
     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(
+  Backend.EnergeticTransitionWithoutActivator t11(arcWeightOut = {outputPower}, maximumSpeed = 1/3600, nIn = 1, nOut = 1) annotation(
     Placement(visible = true, transformation(origin = {2, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
 equation
   inputPower = t11.power;
@@ -23,5 +24,6 @@ equation
   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")}));
+    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")}),
+    Diagram(coordinateSystem(extent = {{-120, 100}, {120, -160}})));
 end Transformer;
diff --git a/Distribution/WaterPipe.mo b/Distribution/WaterPipe.mo
index 4b5fa8b89b920aa6fc079449c3db4e351e085da7..372f835931edb2fc08f20dc7064c99b2b6732cfe 100644
--- a/Distribution/WaterPipe.mo
+++ b/Distribution/WaterPipe.mo
@@ -8,21 +8,19 @@ parameter Integer NIn "Number of Inputs" 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");
+  Real powerDifference(unit = "kg") "Difference between power suply and consumption";
+  Real powerInput(unit = "kg");
+  Real totalLoad(unit = "kg");
   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(
+  PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {powerInput}, maximumSpeed = 1/3600, 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;
+  powerInput = t1.power*t1.actualSpeed./t1.maximumSpeed;
   totalLoad = sum(waterOutput.arcWeight);
   powerDifference = powerInput - totalLoad;
   for i in 1:NIn loop
@@ -38,5 +36,4 @@ equation
   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.order b/Distribution/package.order
index 86de37af6e73a9a66934a8dc6bdc46c55d2ade99..bd094afedc810e0e731a3c38e99b4c4aaebdec70 100644
--- a/Distribution/package.order
+++ b/Distribution/package.order
@@ -5,3 +5,6 @@ ConditionalConnection
 WaterPipe
 HydrogenPipe
 OxygenPipe
+HeatPipe
+CO2Pipe
+NaturalGasPipe
diff --git a/EnergyConsumer/ElectricityConsumer.mo b/EnergyConsumer/ElectricityConsumer.mo
index 7e5177ba82c631ba870558ef868dc4f6be69a6b4..a3cf501825610eee4df83f1b7f6d02a37b6f9da9 100644
--- a/EnergyConsumer/ElectricityConsumer.mo
+++ b/EnergyConsumer/ElectricityConsumer.mo
@@ -3,7 +3,7 @@ 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(
+  PNlib.Components.TC t1(arcWeightIn = {powerConsumption}, arcWeightOut = {powerConsumption}, maximumSpeed = 1/3600, 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)));
@@ -11,7 +11,7 @@ model ElectricityConsumer
     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(
+  Backend.EnergeticTransitionWithoutActivator t11(maximumSpeed = 1/3600, nIn = 1) annotation(
     Placement(visible = true, transformation(origin = {0, 60}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
 equation
   powerConsumption = t11.power;
diff --git a/EnergyConsumer/HeatConsumer.mo b/EnergyConsumer/HeatConsumer.mo
index f3c39c4a207f83857f9770f5ec60d4a6c4ac0e8f..42aae6db1c52eec36ce70cbae0c0db30f329c382 100644
--- a/EnergyConsumer/HeatConsumer.mo
+++ b/EnergyConsumer/HeatConsumer.mo
@@ -3,22 +3,22 @@ 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(
+  PNlib.Components.TC t1(arcWeightIn = {powerConsumption}, arcWeightOut = {powerConsumption}, maximumSpeed = 1/3600, nIn = 1, nOut = 1) annotation(
     Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
-  Interfaces.ElectricalInput electricalInput annotation(
+  Interfaces.HeatInput heatInput 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(
+  Backend.EnergeticTransitionWithoutActivator t11(maximumSpeed = 1/3600, 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(
+  connect(heatInput, t1.inPlaces[1]) annotation(
     Line(points = {{-110, 0}, {-4, 0}}));
   connect(fileInput, t11.inPlaces[1]) annotation(
     Line(points = {{-110, 60}, {-4, 60}}));
diff --git a/EnergyConsumer/HydrogenConsumer.mo b/EnergyConsumer/HydrogenConsumer.mo
new file mode 100644
index 0000000000000000000000000000000000000000..a32ad88b1d0edd592354978f523d38f499b2ddab
--- /dev/null
+++ b/EnergyConsumer/HydrogenConsumer.mo
@@ -0,0 +1,27 @@
+within PNRG.EnergyConsumer;
+
+model HydrogenConsumer
+  Real powerConsumption(unit = "kW") "Consumption of Hydrogen";
+  Real cumulativeEnergyConsumption(unit = "kWh") "Cumulative consumption of electrical energy";
+  PNlib.Components.TC t1(arcWeightIn = {powerConsumption}, arcWeightOut = {powerConsumption}, maximumSpeed = 1/3600, nIn = 1, nOut = 1) annotation(
+    Placement(visible = true, transformation(origin = {0, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.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)));
+  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(maximumSpeed = 1/3600, 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(hydrogenInput, 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 = {-81, -1}, extent = {{-17, -19}, {17, 19}}, imageSource = 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")}));
+end HydrogenConsumer;
diff --git a/EnergyConsumer/package.order b/EnergyConsumer/package.order
index 0367810ea56524c33a867053ff66d4ab2eef10b5..d2b31351a149e063d93a2aa2589c39dd4c9bc699 100644
--- a/EnergyConsumer/package.order
+++ b/EnergyConsumer/package.order
@@ -1,2 +1,3 @@
 ElectricityConsumer
 HeatConsumer
+HydrogenConsumer
diff --git a/Examples/EnergyPark.mo b/Examples/EnergyPark.mo
index 17b555239144ea91221705923953492a099ebd9b..8218630aac3c7d32e90cc2a07d14d21dde8d3ab3 100644
--- a/Examples/EnergyPark.mo
+++ b/Examples/EnergyPark.mo
@@ -2,71 +2,191 @@ 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)));
+    Placement(visible = true, transformation(origin = {-44, 74}, extent = {{-16, -16}, {16, 16}}, rotation = 0)));
+  PNRG.Storage.WaterTank waterTank(maxInputMassFlow = 9.1, maxOutputMassFlow = 9.1, startFilling = 100)  annotation(
+    Placement(visible = true, transformation(origin = {30, 44}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Storage.H2Tank h2Tank(maxInputMassFlow = 1.1, maxOutputMassFlow = 1.1)  annotation(
+    Placement(visible = true, transformation(origin = {30, 84}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Storage.O2Tank o2Tank(maxInputMassFlow = 8, maxOutputMassFlow = 8)  annotation(
+    Placement(visible = true, transformation(origin = {30, 64}, 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)));
+    Placement(visible = true, transformation(origin = {-160, 74}, 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 = {-160, 104}, 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)));
+    Placement(visible = true, transformation(origin = {-120, 104}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.PowerPlants.PVPowerPlant pVPowerPlant(areaPV = 1000) annotation(
+    Placement(visible = true, transformation(origin = {-120, 74}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.EnergyConsumer.ElectricityConsumer electricityConsumer annotation(
+    Placement(visible = true, transformation(origin = {182, 114}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Distribution.OxygenPipe oxygenPipe(NIn = 1, NOut = 1, prioOut = {1})  annotation(
+    Placement(visible = true, transformation(origin = {-4, 56}, extent = {{-12, 4}, {12, 12}}, rotation = 0)));
+  PNRG.Distribution.HydrogenPipe hydrogenPipe(NIn = 1, NOut = 1, prioOut = {1})  annotation(
+    Placement(visible = true, transformation(origin = {-4, 76}, extent = {{-12, 4}, {12, 12}}, rotation = 0)));
+  PNRG.Distribution.WaterPipe waterPipe(NIn = 1, NOut = 1, prioOut = {1})  annotation(
+    Placement(visible = true, transformation(origin = {-4, 36}, extent = {{12, 4}, {-12, 12}}, rotation = 0)));
+  PNRG.Distribution.DistributionPowerGrid distributionPowerGrid(NIn = 5, NOut = 3, prioOut = {1, 2, 3})  annotation(
+    Placement(visible = true, transformation(origin = {24, 104}, extent = {{-18, 6}, {18, 18}}, rotation = 0)));
+  PNRG.Distribution.OxygenPipe oxygenPipe1(NIn = 1, NOut = 1, prioOut = {1})  annotation(
+    Placement(visible = true, transformation(origin = {62, 56}, extent = {{-12, 4}, {12, 12}}, rotation = 0)));
+  PNRG.Distribution.HydrogenPipe hydrogenPipe1(NIn = 1, NOut = 2, prioOut = {1, 2})  annotation(
+    Placement(visible = true, transformation(origin = {62, 76}, extent = {{-12, 4}, {12, 12}}, rotation = 0)));
+  PNRG.Distribution.WaterPipe waterPipe1(NIn = 1, NOut = 1, prioOut = {1})  annotation(
+    Placement(visible = true, transformation(origin = {62, 36}, extent = {{12, 4}, {-12, 12}}, rotation = 0)));
+  PNRG.Sources.FileToTransitionOutput fileToTransitionOutput11(NOut = 1, fileName = "P:/Programs/PNRG/data4.txt", tableName = "tab1") annotation(
+    Placement(visible = true, transformation(origin = {182, 134}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression(NOut = 1, expression = time > 70000)  annotation(
+    Placement(visible = true, transformation(origin = {96, 188}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression1(NOut = 1, expression = time > 70000)  annotation(
+    Placement(visible = true, transformation(origin = {72, 148}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression11(NOut = 1, expression = time > 70000)  annotation(
+    Placement(visible = true, transformation(origin = {72, 174}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression111(NOut = 1, expression = time > 70000)  annotation(
+    Placement(visible = true, transformation(origin = {72, 200}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression1111(NOut = 1, expression = 39.4 + electricityConsumer.powerConsumption < distributionPowerGrid.powerInput)  annotation(
+    Placement(visible = true, transformation(origin = {-28, 202}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression11111(NOut = 1, expression = 39.4 + electricityConsumer.powerConsumption < distributionPowerGrid.powerInput)  annotation(
+    Placement(visible = true, transformation(origin = {-28, 176}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression111111(NOut = 1, expression = 39.4 + electricityConsumer.powerConsumption < distributionPowerGrid.powerInput)  annotation(
+    Placement(visible = true, transformation(origin = {-28, 150}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression1111111(NOut = 1, expression = 39.4 + electricityConsumer.powerConsumption < distributionPowerGrid.powerInput)  annotation(
+    Placement(visible = true, transformation(origin = {-52, 164}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Sources.StochasticSource stochasticSource(NOut = 1) annotation(
+    Placement(visible = true, transformation(origin = {182, 38}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.EnergyConsumer.HydrogenConsumer hydrogenConsumer annotation(
+    Placement(visible = true, transformation(origin = {182, 18}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Storage.Battery battery(power = 2)  annotation(
+    Placement(visible = true, transformation(origin = {20, 138}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression2(NOut = 1, expression = 39.4 + electricityConsumer.powerConsumption+ 10 < distributionPowerGrid.powerInput) annotation(
+    Placement(visible = true, transformation(origin = {96, 162}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression21(NOut = 1, expression = false) annotation(
+    Placement(visible = true, transformation(origin = {-52, 190}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.PowerPlants.NaturalGasPowerPlant naturalGasPowerPlant2 annotation(
+    Placement(visible = true, transformation(origin = {-98, -42}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression3(NOut = 1, expression = false) annotation(
+    Placement(visible = true, transformation(origin = {-32, -24}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression5(NOut = 1, expression = true) annotation(
+    Placement(visible = true, transformation(origin = {-32, 0}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Storage.CO2Storage cO2Storage2(maxInputMassFlow = 220, maxOutputMassFlow = 0) annotation(
+    Placement(visible = true, transformation(origin = {-32, -46}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Sources.ConstantSource constantSource2(NOut = 1, out = 1) annotation(
+    Placement(visible = true, transformation(origin = {-150, -46}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Distribution.CO2Pipe cO2Pipe2(NIn = 2, NOut = 1, prioOut = {1}) annotation(
+    Placement(visible = true, transformation(origin = {-64, -54}, extent = {{-12, 4}, {12, 12}}, rotation = 0)));
+  PNRG.Logics.LogicalExpression logicalExpression4(NOut = 1, expression = true) annotation(
+    Placement(visible = true, transformation(origin = {-98, -20}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.Distribution.NaturalGasPipe naturalGasPipe12(NIn = 1, NOut = 1, prioOut = {1}) annotation(
+    Placement(visible = true, transformation(origin = {-124, -54}, extent = {{-12, 4}, {12, 12}}, rotation = 0)));
+  PNRG.PowerPlants.HydrogenCHPPlant hydrogenCHPPlant annotation(
+    Placement(visible = true, transformation(origin = {98, 78}, extent = {{-16, -16}, {16, 16}}, rotation = 0)));
+  PNRG.Distribution.HeatPipe heatPipe(NIn = 2, NOut = 1, prioOut = {1}) annotation(
+    Placement(visible = true, transformation(origin = {148, 58}, extent = {{-12, 4}, {12, 12}}, rotation = 0)));
+  PNRG.Sources.FileToTransitionOutput fileToTransitionOutput111(NOut = 1, fileName = "P:/Programs/PNRG/data3.txt", tableName = "tab1") annotation(
+    Placement(visible = true, transformation(origin = {182, 86}, extent = {{10, -10}, {-10, 10}}, rotation = 0)));
+  PNRG.EnergyConsumer.HeatConsumer heatConsumer annotation(
+    Placement(visible = true, transformation(origin = {182, 66}, 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)));
+    Placement(visible = true, transformation(origin = {-120, 24}, 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}));
+    Line(points = {{-149, 74}, {-131, 74}}));
+  connect(distributionPowerGrid.electricalOutput[2], electrolyser.EnergyIn) annotation(
+    Line(points = {{40.5, 116}, {40, 116}, {40, 104}, {-70, 104}, {-70, 74}, {-62, 74}}, color = {255, 200, 0}));
+  connect(electrolyser.H2Out, hydrogenPipe.hydrogenInput[1]) annotation(
+    Line(points = {{-26, 80}, {-20.9, 80}, {-20.9, 84.4}, {-15.4, 84.4}}, color = {106, 168, 79}));
+  connect(hydrogenPipe.hydrogenOutput[1], h2Tank.hydrogenInput) annotation(
+    Line(points = {{7, 84}, {19, 84}}, color = {106, 168, 79}));
+  connect(oxygenPipe.oxygenOutput[1], o2Tank.oxygenInput) annotation(
+    Line(points = {{7, 64}, {19, 64}}, color = {11, 83, 148}));
+  connect(o2Tank.oxygenOutput, oxygenPipe1.oxygenInput[1]) annotation(
+    Line(points = {{41, 64}, {51, 64}}, color = {11, 83, 148}));
+  connect(electrolyser.O2Out, oxygenPipe.oxygenInput[1]) annotation(
+    Line(points = {{-26, 68}, {-20.4, 68}, {-20.4, 63.6}, {-15.4, 63.6}}, color = {11, 83, 148}));
+  connect(waterPipe1.waterOutput[1], waterTank.waterInput) annotation(
+    Line(points = {{51, 44}, {41, 44}}, color = {61, 133, 198}));
+  connect(waterTank.waterOutput, waterPipe.waterInput[1]) annotation(
+    Line(points = {{19, 44}, {7, 44}}, color = {61, 133, 198}));
+  connect(waterPipe.waterOutput[1], electrolyser.WaterIn) annotation(
+    Line(points = {{-15, 44}, {-68, 44}, {-68, 65}, {-62, 65}}, color = {61, 133, 198}));
+  connect(h2Tank.hydrogenOutput, hydrogenPipe1.hydrogenInput[1]) annotation(
+    Line(points = {{41, 84}, {51, 84}}, color = {106, 168, 79}));
+  connect(fileToTransitionOutput11.fileOutput[1], electricityConsumer.fileInput) annotation(
+    Line(points = {{171, 134}, {166, 134}, {166, 120}, {171, 120}}, color = {150, 150, 150}));
+  connect(logicalExpression111111.logicalOutput[1], h2Tank.logicalInput) annotation(
+    Line(points = {{-17, 150}, {2, 150}, {2, 90}, {19, 90}}, color = {53, 28, 117}));
+  connect(logicalExpression11111.logicalOutput[1], o2Tank.logicalInput) annotation(
+    Line(points = {{-17, 176}, {2, 176}, {2, 70}, {19, 70}}, color = {53, 28, 117}));
+  connect(logicalExpression1111.logicalOutput[1], waterTank.logicalInput1) annotation(
+    Line(points = {{-17, 202}, {2, 202}, {2, 50}, {19, 50}}, color = {53, 28, 117}));
+  connect(logicalExpression111.logicalOutput[1], h2Tank.logicalInput1) annotation(
+    Line(points = {{61, 200}, {46, 200}, {46, 90}, {41, 90}}, color = {53, 28, 117}));
+  connect(logicalExpression11.logicalOutput[1], o2Tank.logicalInput1) annotation(
+    Line(points = {{61, 174}, {46, 174}, {46, 70}, {41, 70}}, color = {53, 28, 117}));
+  connect(logicalExpression1.logicalOutput[1], waterTank.logicalInput) annotation(
+    Line(points = {{61, 148}, {46, 148}, {46, 50}, {41, 50}}, color = {53, 28, 117}));
+  connect(logicalExpression1111111.logicalOutput[1], electrolyser.activation) annotation(
+    Line(points = {{-63, 164}, {-66, 164}, {-66, 86.5}, {-62, 86.5}, {-62, 83}}, color = {53, 28, 117}));
   connect(fileToTransitionOutput1.fileOutput[1], windPowerPlant.fileInput) annotation(
-    Line(points = {{-85, 20}, {-71, 20}}, color = {150, 150, 150}));
+    Line(points = {{-149, 104}, {-131, 104}}, color = {150, 150, 150}));
+  connect(windPowerPlant.electricalOutput, distributionPowerGrid.electricalInput[1]) annotation(
+    Line(points = {{-109, 104}, {-81, 104}, {-81, 116}, {7.5, 116}}, color = {255, 200, 0}));
+  connect(pVPowerPlant.electricalOutput, distributionPowerGrid.electricalInput[2]) annotation(
+    Line(points = {{-109, 74}, {-81, 74}, {-81, 116}, {7.5, 116}}, color = {255, 200, 0}));
+  connect(hydrogenPipe1.hydrogenOutput[2], hydrogenConsumer.hydrogenInput) annotation(
+    Line(points = {{73, 84}, {75, 84}, {75, 18}, {170, 18}}, color = {106, 168, 79}));
+  connect(stochasticSource.fileOutput[1], hydrogenConsumer.fileInput) annotation(
+    Line(points = {{171, 38}, {165, 38}, {165, 24}, {171, 24}}, color = {150, 150, 150}));
+  connect(constantSource2.fileOutput[1], naturalGasPipe12.naturalGasInput[1]) annotation(
+    Line(points = {{-139, -46}, {-135, -46}}, color = {150, 150, 150}, thickness = 0.5));
+  connect(naturalGasPipe12.naturalGasOutput[1], naturalGasPowerPlant2.naturalGasInput) annotation(
+    Line(points = {{-113, -46}, {-111, -46}, {-111, -47}, {-109, -47}}, color = {126, 58, 0}));
+  connect(naturalGasPowerPlant2.cO2Output, cO2Pipe2.co2Input[1]) annotation(
+    Line(points = {{-87, -45.2}, {-81, -45.2}, {-81, -46.2}, {-75, -46.2}}));
+  connect(cO2Pipe2.co2Output[1], cO2Storage2.co2Input) annotation(
+    Line(points = {{-53, -46}, {-43, -46}}, color = {54, 54, 54}));
+  connect(logicalExpression4.logicalOutput[1], naturalGasPowerPlant2.activation) annotation(
+    Line(points = {{-109, -20}, {-113, -20}, {-113, -36}, {-109, -36}}, color = {53, 28, 117}));
+  connect(logicalExpression3.logicalOutput[1], cO2Storage2.logicalInput1) annotation(
+    Line(points = {{-21, -24}, {-11, -24}, {-11, -40}, {-21, -40}}, color = {53, 28, 117}));
+  connect(logicalExpression5.logicalOutput[1], cO2Storage2.logicalInput) annotation(
+    Line(points = {{-43, 0}, {-51, 0}, {-51, -40}, {-43, -40}}, color = {53, 28, 117}));
+  connect(cO2Storage2.co2Output, cO2Pipe2.co2Input[2]) annotation(
+    Line(points = {{-21, -46}, {-11, -46}, {-11, -58}, {-79, -58}, {-79, -46}, {-75, -46}}, color = {54, 54, 54}));
+  connect(distributionPowerGrid.electricalOutput[3], battery.electricalInput) annotation(
+    Line(points = {{40.5, 116}, {34, 116}, {34, 138}, {31.5, 138}}, color = {255, 200, 0}));
+  connect(battery.electricalOutput, distributionPowerGrid.electricalInput[5]) annotation(
+    Line(points = {{9, 138}, {6.25, 138}, {6.25, 116}, {7.5, 116}}, color = {255, 200, 0}));
+  connect(logicalExpression21.logicalOutput[1], battery.logicalInput1) annotation(
+    Line(points = {{-41, 190}, {2, 190}, {2, 144}, {9, 144}}, color = {53, 28, 117}));
+  connect(logicalExpression2.logicalOutput[1], battery.logicalInput) annotation(
+    Line(points = {{85, 162}, {46, 162}, {46, 144}, {31, 144}}, color = {53, 28, 117}));
+  connect(hydrogenCHPPlant.electricalOutput, distributionPowerGrid.electricalInput[3]) annotation(
+    Line(points = {{115.6, 87.6}, {131.6, 87.6}, {131.6, 125.6}, {-0.4, 125.6}, {-0.4, 116}, {7.5, 116}}, color = {255, 200, 0}));
+  connect(logicalExpression.logicalOutput[1], hydrogenCHPPlant.activation) annotation(
+    Line(points = {{85, 188}, {46, 188}, {46, 88}, {80, 88}}, color = {53, 28, 117}));
+  connect(oxygenPipe1.oxygenOutput[1], hydrogenCHPPlant.O2In) annotation(
+    Line(points = {{73, 64}, {76.5, 64}, {76.5, 68}, {80, 68}}, color = {11, 83, 148}));
+  connect(hydrogenPipe1.hydrogenOutput[1], hydrogenCHPPlant.H2In) annotation(
+    Line(points = {{73, 84}, {75.5, 84}, {75.5, 78}, {80, 78}}, color = {106, 168, 79}));
+  connect(hydrogenCHPPlant.WaterOut, waterPipe1.waterInput[1]) annotation(
+    Line(points = {{115.6, 68.4}, {119.6, 68.4}, {119.6, 44.4}, {72.6, 44.4}}, color = {61, 133, 198}));
+  connect(hydrogenCHPPlant.heatOutput, heatPipe.heatInput[2]) annotation(
+    Line(points = {{115.6, 78}, {131.6, 78}, {131.6, 66}, {136.6, 66}}, color = {255, 80, 50}));
+  connect(fileToTransitionOutput111.fileOutput[1], heatConsumer.fileInput) annotation(
+    Line(points = {{171, 86}, {166, 86}, {166, 72}, {172, 72}}, color = {150, 150, 150}));
+  connect(heatPipe.heatOutput[1], heatConsumer.heatInput) annotation(
+    Line(points = {{159, 66}, {171, 66}}, color = {255, 80, 50}));
+  connect(sTEPowerPlant.heatOutput, heatPipe.heatInput[1]) annotation(
+    Line(points = {{-109, 24}, {132, 24}, {132, 66}, {137, 66}}, color = {255, 80, 50}));
   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}));
+    Line(points = {{-149, 74}, {-141, 74}, {-141, 24}, {-131, 24}}, color = {150, 150, 150}));
+  connect(distributionPowerGrid.electricalOutput[1], electricityConsumer.electricalInput) annotation(
+    Line(points = {{40.5, 116}, {104.75, 116}, {104.75, 114}, {171, 114}}, color = {255, 200, 0}));
+  connect(naturalGasPowerPlant2.electricalOutput, distributionPowerGrid.electricalInput[4]) annotation(
+    Line(points = {{-87, -39}, {-87, -38.5}, {-81, -38.5}, {-81, 116}, {7.5, 116}}, color = {255, 200, 0}));
 protected
   annotation(
     uses(PNlib(version = "2.2")),
-    Diagram(coordinateSystem(extent = {{-120, 80}, {140, -80}})),
+    Diagram(coordinateSystem(extent = {{-180, 220}, {200, -120}})),
     version = "");
 end EnergyPark;
diff --git a/Examples/package.order b/Examples/package.order
index 97a8bd8a7462072b5fa0b24634100c50c1bdb80e..3fcef5ecd5cfac0b7b7b7ba771adb98e322a705f 100644
--- a/Examples/package.order
+++ b/Examples/package.order
@@ -1,4 +1,3 @@
 EnergyPark
 simpleExample
 Microgrids
-test
diff --git a/Examples/simpleExample.mo b/Examples/simpleExample.mo
index 9858708430f2f853caa764e2afaaa0f2ab94e7e0..b0c1822bc77b2d91ab6a2d071e52eeea8ebc868f 100644
--- a/Examples/simpleExample.mo
+++ b/Examples/simpleExample.mo
@@ -11,7 +11,7 @@ model simpleExample
     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(
+  PNRG.PowerPlants.PVPowerPlant pVPowerPlant(areaPV = 100, efficiency_PV = 0.2) 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)));
@@ -23,7 +23,7 @@ model simpleExample
     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(
+  PNRG.Logics.IntegerController testController(NChange = 1, NMax = 10, NStart = 10, delay = 300) 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)));
diff --git a/Examples/test.bak-mo b/Examples/test.bak-mo
new file mode 100644
index 0000000000000000000000000000000000000000..217b6cea5d31cb1fcef367b94ee7c42934ca6ec9
--- /dev/null
+++ b/Examples/test.bak-mo
@@ -0,0 +1,45 @@
+within PNRG.Examples;
+
+model test
+  Real inputPower;
+  Real p1;
+  Real p2;
+  Modelica.Electrical.Analog.Basic.VariableResistor resistor2 annotation(
+    Placement(visible = true, transformation(origin = {-274, 40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  Modelica.Electrical.Analog.Basic.Ground ground1 annotation(
+    Placement(visible = true, transformation(origin = {-232, -2}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  Modelica.Electrical.Analog.Basic.Transformer transformer1(L1 = 1, L2 = 2, M = 0) annotation(
+    Placement(visible = true, transformation(origin = {-242, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  Modelica.Electrical.Analog.Basic.Resistor resistor1(R = 1) annotation(
+    Placement(visible = true, transformation(origin = {-196, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 90)));
+  Modelica.Electrical.Analog.Basic.Ground ground annotation(
+    Placement(visible = true, transformation(origin = {-252, -2}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  Modelica.Blocks.Sources.RealExpression realExpression(y = if inputPower > 0 then 10^2/inputPower else 10^6) annotation(
+    Placement(visible = true, transformation(origin = {-302, 62}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  Modelica.Electrical.Analog.Sources.CosineVoltage cosineVoltage(V = 10*sqrt(2), freqHz = 1) annotation(
+    Placement(visible = true, transformation(origin = {-304, 40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  Storage.Battery battery annotation(
+    Placement(visible = true, transformation(origin = {-394, 66}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+  inputPower = sqrt(time);
+  p1 = resistor2.i*resistor2.v;
+  p2 = resistor1.i*resistor1.v;
+  connect(transformer1.n1, cosineVoltage.p) annotation(
+    Line(points = {{-252, 20}, {-314, 20}, {-314, 40}}, color = {0, 0, 255}));
+  connect(transformer1.p2, resistor1.n) annotation(
+    Line(points = {{-232, 40}, {-196, 40}}, color = {0, 0, 255}));
+  connect(cosineVoltage.n, resistor2.p) annotation(
+    Line(points = {{-294, 40}, {-284, 40}}, color = {0, 0, 255}));
+  connect(transformer1.n2, ground1.p) annotation(
+    Line(points = {{-232, 20}, {-232, 8}}, color = {0, 0, 255}));
+  connect(realExpression.y, resistor2.R) annotation(
+    Line(points = {{-291, 62}, {-275, 62}, {-275, 52}}, color = {0, 0, 127}));
+  connect(transformer1.n1, ground.p) annotation(
+    Line(points = {{-252, 20}, {-252, 8}}, color = {0, 0, 255}));
+  connect(resistor2.n, transformer1.p1) annotation(
+    Line(points = {{-264, 40}, {-252, 40}}, color = {0, 0, 255}));
+  connect(transformer1.n2, resistor1.p) annotation(
+    Line(points = {{-232, 20}, {-196, 20}}, color = {0, 0, 255}));
+  annotation(
+    Diagram(coordinateSystem(extent = {{-700, 380}, {180, -220}})));
+end test;
diff --git a/Examples/test.mo b/Examples/test.mo
deleted file mode 100644
index 6c058a9715be27bcc8baa061306bdae3e540170c..0000000000000000000000000000000000000000
--- a/Examples/test.mo
+++ /dev/null
@@ -1,201 +0,0 @@
-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/CO2Input.mo b/Interfaces/CO2Input.mo
new file mode 100644
index 0000000000000000000000000000000000000000..615d9685a91248245c723d81a94cb7163870f896
--- /dev/null
+++ b/Interfaces/CO2Input.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector CO2Input
+  extends PNlib.Interfaces.TransitionIn;
+  annotation(
+    Icon(graphics = {Polygon(lineColor = {54, 54, 54}, fillColor = {54, 54, 54}, 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 CO2Input;
diff --git a/Interfaces/CO2Output.mo b/Interfaces/CO2Output.mo
new file mode 100644
index 0000000000000000000000000000000000000000..7c7e9194313231bc02c4c5fca8c4ccde0ab7a754
--- /dev/null
+++ b/Interfaces/CO2Output.mo
@@ -0,0 +1,7 @@
+within PNRG.Interfaces;
+
+connector CO2Output
+  extends PNlib.Interfaces.PlaceOut;
+  annotation(
+    Icon(graphics = {Polygon(origin = {14, -26}, lineColor = {54, 54, 54}, fillColor = {54, 54, 54}, fillPattern = FillPattern.Solid, points = {{-114, 126}, {86, 26}, {-114, -74}, {-114, 126}})}));
+end CO2Output;
diff --git a/Interfaces/package.order b/Interfaces/package.order
index 1105ae5642d740b3c3419f5e90bc27448fb7249a..73ecb8858e847899eb118e5087bfa4dd2baff8b0 100644
--- a/Interfaces/package.order
+++ b/Interfaces/package.order
@@ -14,3 +14,5 @@ NaturalGasInput
 NaturalGasOutput
 FileInput
 FileOutput
+CO2Output
+CO2Input
diff --git a/PowerPlants/FuelCell.mo b/PowerPlants/FuelCell.mo
new file mode 100644
index 0000000000000000000000000000000000000000..787905e9a5508b349a931269c86e7cee73ebe97c
--- /dev/null
+++ b/PowerPlants/FuelCell.mo
@@ -0,0 +1,62 @@
+within PNRG.PowerPlants;
+
+model FuelCell
+  PNlib.Components.TC CHP(arcWeightIn = {1.1, 8, 1}, arcWeightOut = {0.6*39.4*0.7, 9.1}, maximumSpeed = 1/3600, nIn = 3, nOut = 2) 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)));
+  PNRG.Interfaces.WaterOutput WaterOut 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.ElectricalOutput electricalOutput 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)));
+  PNlib.Components.TC t1(arcWeightIn = {2}, maximumSpeed = 1/3600, 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 = {82, -30}, 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.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, 30}, 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 = {{91, 30}, {110, 30}}));
+  connect(p1.outTransition[1], WaterOut) annotation(
+    Line(points = {{93, -30}, {110, -30}}));
+  connect(CHP.outPlaces[1], energeticFlowPlace1.inTransition[1]) annotation(
+    Line(points = {{0, -12}, {0, 30}, {69, 30}}, 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));
+  connect(CHP.outPlaces[2], p1.inTransition[1]) annotation(
+    Line(points = {{0, -12}, {0, 0}, {60, 0}, {60, -30}, {72, -30}}, 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, 60}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, 30}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, -30}, extent = {{-20, 20}, {20, -20}}), Bitmap(origin = {0, 60}, extent = {{-40, -38}, {40, 38}}, imageSource = "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")}));
+end FuelCell;
diff --git a/PowerPlants/HydrogenCHPPlant.mo b/PowerPlants/HydrogenCHPPlant.mo
index 1428c2086d936d3a6c4a2a18a3273a468c0522fd..2366329ee8a0d9320d5e2c7a4646acd19e53473c 100644
--- a/PowerPlants/HydrogenCHPPlant.mo
+++ b/PowerPlants/HydrogenCHPPlant.mo
@@ -1,7 +1,7 @@
 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(
+  PNlib.Components.TC CHP(arcWeightIn = {1.1*activation.t, 8*activation.t, 1}, arcWeightOut = {0.5*39.4*activation.t, 0.45*39.4*activation.t, 9.1}, maximumSpeed = 1/3600, 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)));
@@ -17,22 +17,14 @@ model HydrogenCHPPlant
     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(
+  PNlib.Components.TC t1(arcWeightIn = {2}, maximumSpeed = 1/3600, 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}}));
@@ -40,10 +32,6 @@ equation
     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(
@@ -56,14 +44,10 @@ equation
     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));
+  connect(H2In, CHP.inPlaces[1]) annotation(
+    Line(points = {{-110, 0}, {-60, 0}, {-60, -40}, {0, -40}, {0, -20}}));
+  connect(O2In, CHP.inPlaces[2]) annotation(
+    Line(points = {{-110, -60}, {0, -60}, {0, -20}}));
   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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")}));
diff --git a/PowerPlants/NaturalGasPowerPlant.mo b/PowerPlants/NaturalGasPowerPlant.mo
new file mode 100644
index 0000000000000000000000000000000000000000..0de7b50a25e977917bd9ab0b9d8d8dca62c4a0a3
--- /dev/null
+++ b/PowerPlants/NaturalGasPowerPlant.mo
@@ -0,0 +1,42 @@
+within PNRG.PowerPlants;
+
+model NaturalGasPowerPlant
+  PNlib.Components.TC CHP(arcWeightIn = {100*activation.t, 1}, arcWeightOut = {1200*activation.t, 220*activation.t}, maximumSpeed = 1/3600, nIn = 2, nOut = 2) annotation(
+    Placement(visible = true, transformation(origin = {-14, 2}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Interfaces.LogicalInput activation annotation(
+    Placement(visible = true, transformation(origin = {-110, 50}, 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 = {-76, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNlib.Components.TC t1(arcWeightIn = {2}, maximumSpeed = 1/3600, nIn = 1) annotation(
+    Placement(visible = true, transformation(origin = {-50, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1) annotation(
+    Placement(visible = true, transformation(origin = {82, -30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Backend.EnergeticFlowPlace energeticFlowPlace1(nIn = 1, nOut = 1) annotation(
+    Placement(visible = true, transformation(origin = {80, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  Interfaces.ElectricalOutput electricalOutput annotation(
+    Placement(visible = true, transformation(origin = {110, 30}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNRG.Interfaces.CO2Output cO2Output annotation(
+    Placement(visible = true, transformation(origin = {110, -30}, extent = {{-10, -10}, {10, 10}}, rotation = 0), iconTransformation(origin = {110, -32}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  Interfaces.NaturalGasInput naturalGasInput 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)));
+equation
+  connect(activation, splitLogicalInput.logicalInput) annotation(
+    Line(points = {{-110, 50}, {-87, 50}}));
+  connect(splitLogicalInput.test_output, CHP.inPlaces[2]) annotation(
+    Line(points = {{-65, 52}, {-28, 52}, {-28, 2}, {-19, 2}, {-19, 2}}));
+  connect(splitLogicalInput.inhibitor_output, t1.inPlaces[1]) annotation(
+    Line(points = {{-65, 48}, {-62, 48}, {-62, 30}, {-55, 30}}));
+  connect(energeticFlowPlace1.outTransition[1], electricalOutput) annotation(
+    Line(points = {{91, 30}, {110, 30}}));
+  connect(p1.outTransition[1], cO2Output) annotation(
+    Line(points = {{92, -30}, {110, -30}}));
+  connect(naturalGasInput, CHP.inPlaces[1]) annotation(
+    Line(points = {{-110, -50}, {-29, -50}, {-29, 2}, {-19, 2}}));
+  connect(CHP.outPlaces[1], energeticFlowPlace1.inTransition[1]) annotation(
+    Line(points = {{-10, 2}, {40, 2}, {40, 30}, {70, 30}}, thickness = 0.5));
+  connect(CHP.outPlaces[2], p1.inTransition[1]) annotation(
+    Line(points = {{-10, 2}, {40, 2}, {40, -30}, {72, -30}}, 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, -50}, 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, 30}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {80, -30}, 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 NaturalGasPowerPlant;
diff --git a/PowerPlants/PVPowerPlant.mo b/PowerPlants/PVPowerPlant.mo
index 3ffa51b280a804f5a70ec09b3123772c58e07b57..fa1233fc44445a4f48f896649dbd1ba11639507e 100644
--- a/PowerPlants/PVPowerPlant.mo
+++ b/PowerPlants/PVPowerPlant.mo
@@ -13,7 +13,7 @@ model PVPowerPlant
     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(
+  PNRG.Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {currentPower}, maximumSpeed = 1/3600, nIn = 1, nOut = 1)  annotation(
     Placement(visible = true, transformation(origin = {2, 0}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
 equation
   powerPerArea = t1.power;
diff --git a/PowerPlants/STEPowerPlant.mo b/PowerPlants/STEPowerPlant.mo
index 2222ffaad3e3a80b2ea146503155a9383660a320..b20394d5169255c0b80035dadb2519f4a6cacfca 100644
--- a/PowerPlants/STEPowerPlant.mo
+++ b/PowerPlants/STEPowerPlant.mo
@@ -3,29 +3,27 @@ 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(
+  Real area(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(
+  Real efficiency = 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.EnergeticTransitionWithoutActivator t1(arcWeightOut = {currentPower}, maximumSpeed = 1/3600, nIn = 1, nOut = 1) annotation(
+    Placement(visible = true, transformation(origin = {0, 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(
+  currentPower = powerPerArea*area*efficiency;
+  connect(fileInput, t1.inPlaces[1]) annotation(
+    Line(points = {{-110, 0}, {-5, 0}}));
+  connect(t1.outPlaces[1], energeticFlowPlace.inTransition[1]) annotation(
+    Line(points = {{5, 0}, {40, 0}}, thickness = 0.5));
+  connect(energeticFlowPlace.outTransition[1], 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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Ellipse(origin = {80, 0}, extent = {{-20, 20}, {20, -20}}), Ellipse(origin = {-80, 0}, extent = {{-20, 20}, {20, -20}}), Bitmap(origin = {-79, -1}, extent = {{21, -19}, {-21, 19}}, imageSource = 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9MHj0g7zT1+Z7MjklbvCazdFyzLIEACVGw+IhUtwyxzeqn5LRoUPt80A5mB46cWvktauA6SUykf613adukQsW3U26ubrVbEft8ewlfrlsbZSWR7ajzN7mZ7qw+wKDQTPkMXLnVSG79cdMKAAAs8JP8RUJkW68RbLkKzK26GH2uWCw+LvoI69XlfQLNH1Q8uG97L51SmT1VZy929SH2GPaC6aH/VwR2YbyO94asvsD/UVz4yn6HjoneG26NXMvsc8FABBFQyiRqcIZhkl3anOU/oBiUx70NXlV5HWqom0ZH3q43b9O6EkRbpK5e7c9pkUyNaNS8v3geatkH+5DgJDJqSL9lLY+L+xrUowXM2sXAMwJKfYrttAsPUWy5LH2eaG/FDRmvyz62QReZfdxLshYen8p75zkVfGn9rgWhUwkf6ePf1nkOatlnn7I7hv0jslVIdvJP+sHnU3Ba1GkefJq+9wAADtlelxbGCjFu0Ymhp5mnx/6gx7/schrUnVP6vSG3emFLezj4VzN00fYY9sN/gZZfdyvBc9TWZMVdh+heCZXhczS105PXRt5HQr1MqbGBYCO6PNX6xOTYzK52aRv+CWmZerGVvta1MHLOvkgp+/BCyKPhXP3E/bYdsrUWFv3m8hzVNlf2f2E4phcFbKdvl3msipk16Zvt9sBADArpgtdJFh66k8lT48u8iY4iCPZUCty/OvkNsnSz8z2qlMDjkc/vbTblUFlU+sAyZIvSG/uJxm0W/3+2X2G7phcFTJz75f+TxF5tW/y7fYAAMwa6d3NJbswvUR/vn62zRLMnckxjcFxr6U/lokktftvmZztJPxb7NQs/Vt7jGeLn+5R/A1o9jFr5dCj7H5DZ3S9KmS3apNvtwkAYE5Iljw3CJd+mrk/Su6OZfXK4pGpAhUe83rq59V+80xDliZnL+jPOM8m+VV7nHeFjC7ZT6belw24cTV5ud1/mBsyccQCPW+/LIOdN/8Gf2XdbhsAwJyYbETa7qJIyPTba7RA/ecgl46uG3pMRyLHue6e5a+Ghcei9bjI72J3XjuXb6gkTx+tf/OzyOPU1U/aYwCzw39jNT01ZAGrQnbtp+z2AQB0hLTTl0ZCZlDeKHnyaT99m91OmD3TH6SuiRzfJnil/+bmTsdjaqyw/T3s2vT5Ox7nGLKptbdk7sNS3Aq3VXGVPRawc2TcLfEzvESO5aC8hVoEAIUxNYfp5JhqGzaD9Bb1q0UtdtI09Ng9JHJMm+ZXJ2/My4f30iL+p8i/Y7fmbrl97+2ItIcW6+9dGPxdM7zUHg+II3nyVCnjN3JZ8hW7rQAAXaHh8rogbMqh/0pxmWStRXabYWaml8W2x7KJbtZi/tbI/8di3OJXhgzef1MLkRyn/35r5G+a42jrHvbYwBQ7rAqZB8etHN4uY4seZrcbAKArpm9mujwSOmXyNMmHHm+3HUKYkeNO1nHaufKYp6+403tvavrFsjZR/TVzf7PjsYG/fKP60p6vCtm16Q/stgMAFMLkbCFB6JTQzJ2rP59htx/uQI/P6cFxQ+yNI5PvuamFlt4hg51Rolzm6WvsudlU/A224r9JrcZsQdv4VhUAeoafmk+qdaPdBn/TVrcLcNQR6esKbNhwt/orufpzNPJvzTZP/tuem01jalXIyQ9iZf8GdUdPs/sBAFAoFR2K8DMt+MeweuUUki+8X+QYIfbSZo/Vntm19vxsCprH99Jc/oD0f1XI7mXoIgD0GtmYHix+aj4bQNXwN5Inb/Dj0e1+NQk9Ds+MHBtE7L9X2fOz7sjo0KG635+SqtaRzJ1r9wkAoCdo6Hw2CKEq6VevzJJ3ST58kN23JiC5e3dwTBBxMEYWYqojk6tCZslXZGpK1/A4VEfuDwKA/iB564FSj6+Ir5U8/ZC/am/3sc5IOz0hciwQcSAmT7HnaJ2YWhUy/YGUY1XILk0vsPsHANBTJHdfD8Oost4kWfoZ/0HC7mcd0f39VeQYIOJgfLM9R+uAZMljNVdPiexvdc2SF9j9BADoKfpJ/zDxsw/YQKq2/qr91/y+2f2tC34REmHe6fI4ro6p5+3gueqI8Rx1dUT//+3v+r/f/lijburxMxc+N5bFr9rztMpMrwq5JrKf1TZPfsGMVwAwEDSEfhiEUj30HyR+6JedtvtcdSRvPTGyv1ikvsE9X12jnq2epZ6hnq6eqp6knqge32dPcFPPe7Kb2g6/TSvdVOO+vVFf72jO+++YPU+rxuSqkFnyXKn1gkbJv9j9BgDoCzLhhsNQqp2ny0T613bfqwrLmBegb0h9Q+0bVN9Q+6b1NDfVyC53YaNbRX1jfoqbasr9BwZ/Jd3vr/8wYY8Hduv1vmG152oVmFwVMk+P1n24KLJfdfJ3sqm1t91/AIC+IVlyZiScamhynv58pt3/qiG5+064bzijfkjGWjd1FdhfofZNtW1Om6b/UOGbcf9BwzfifuiKHxpjjx3O3vH0wfZcLTN/WRWynV4S7EstTd9kjwEAQF+RzD0hDKc6m17gb5yp6lg+bbg3hfuEk1et17mpBtI3kr6hrMvV6n7pr4r7K/2r3NTVcJrw2ZulR9pztYxMrjacu3dKtVaF7NLkT7IxvYs9FgAAfUdD6fwwpGrvzzWIX16l1St1W/eXWkzNVYC+GfRNoW8O/XhmP77ZNpDYvX68uh+W4j/I+GE4jA+PmyXvsudrmaj0qpDdmrv/sMcDAGAgyETyd0FINcffSZa+sQqrV8pE+pjI9jdDfzOgv4nRX71e4cLGEPuj/9bAf8Dx48L9Bx4a8Ckz9117vpaByq8K2b3X+Zmd7HEBABgIk3eot93GSFg1ySuknf67XLDobvb4lAXJ09dEtruebr+C7RvsQcwIgrPTf7PgG3B/86kfL29fx6aYux/Z83WQyNiih2me/a9Uf1XILk0+Zo8NAMBA0XB+URhWjfRaydyHy7h65fSyynZ766Nv2HzjxhCR6uo/HJ3ppm5UbdaMKFv8jB/2nO03Mt4aqs+qkF17s+QL72ePEQDAQJFlR+2hAbU5ElpN9Wb1c7Jh4YPssRoUUrc5cv1wBN+Y+QbNjxW2zRtWW/+hyd+86meIacLV74lkoT1n+4W0W4/TbTg12KZGm37RHicAgFKgDdArw9BqvLfqcfmG5O5we7z6ib+5U/xVtHD7qqVvsv1QEX8jHrOINEs/JaMf++3H49v3RS1Mn2/P214jE0NPkzquCtm9t6kPsccLAKAUyOYF+2hj+ftIeOHk6pXpCZKnj7DHrR9MfVUcbFM13N5k+yvZNNno9Te+1u7Kd/pee972gql7btLn6XNOhNuAk+buO/a4AQCUCg2qtwThhXfWLxaUuSfYY9dL9PleFmxH2fULqtBk4670V7598135K9/J8fa8LZLJb7mmVoX8afjcuIPb9ANJYo8fAECpkE2tAzSwroyEGIae76dUtMewF+hzfTby/OXTN01+2AAzi+Bc9WO+/cI7flx/Nacb/Jk9b4tgelXI1zdnVciuPdkeQwCAUiLt5LhIiOFM5u5HkiUv9Dee2mNZFNNL04fPXQa33/zoZxexTRRiJ/pvRfyiRn6xHft+K6+3++bYnrudMr0q5LHSqFUhC3AifYw9lgAApURGW/fQ4Lo+CDLcuXnyC8nTV8im1t72mHaDX4Ze/AIO9vkGrR9/65sirmZjL/Xjvf1Kl1VYaj5rLbLn71yRDYvvrR+w/1Mf75rg8XEXpufY4wkAUGokSz8ehhnOyskbT9M3+aXY7XHtBBkfenjwHIPSX832Kz5yNRv7rR9y4u8JKPONllnyT/b8nS0ylt5fP7R/Wpq7KmQBJk+xxxUAoNTIhsWHSB2moRuoyZ+0+X53t0sLTw5XCR67z/qri35RGq5mYxn0Y739Tbn2fTp4P2LP310x9YGaVSELsG2PLQBAJdAA+1Ik1HDu/ln9qIwl97HHeDbo334k8pj90a8W6IeNMNMIllE/3GTElekmy1Pt+TsTk1N95u7/hFUhizFLnmuPMQBAJZA8eahMLSAQhht2ol+98vOyftF8e6x3hv7Nqshj9VZ/s5pfnIYl1rEK+g+EfmrBQS8nn7tf2/PXwqqQPfEiPz+5PdYAAJVBg+x7kXDD7rxV2sk3Z7sU9OTQlPAxeqNfoIbx2VhV/QfElW6Qjfc2WXfYgfYc9kiWPl3/fW3kb7Bb8/Roe7wBACqFNoWpTC4kEAk57NZtkrvlMuGG7XHfjmTJAyJ/V7x+POwpLmxgEKvoIBvvHaalm5phaHJVyA3B72FBppf4RYF2zE0AgEoi7WRFGHJYsCv1OC8Nj33695HfLc51jkYb66tvvP09CP1svP3UoH5VyKnVYVkVsve+zuYmAEAlkSx5bCTksDeOSpYe+Zdj36tFiHyjzdARbIrbr3j3ZS7vZER//ib8/9gDL5fRJfvtWK8AACqNBttIJOywd26UdvqPhX+74G+G9FOq2YYEsQkO4oo39s7cHWtrFQBApZE8eWoQdtgPi5klZr2bmnXENiCITdTPauLnlS/PdII4d6+VfPggW6sAACqPBlweCT0ss/4r9LMc0/shxjxJXevC8wYrYPJBW6MAAGrB9N32keDDUuoXBGFlSMRd6+9n8MOt7DmEZfVG2ZgebGsUAEAtmJriirvuSy8zjyDOXf8t0JmO8d3V8LO2PgEA1Irpqa5s+GEZ9I0C47QRu9OP7/bDsBjfXVZvlbz1QFubAABqxeT8skx5VT79DWC+UbDNAyJ25sluakEoe67hYM3d121dAgCoJZIn/y8IQRyMftzpChc2C4hYjH6YSV/m78ZZuFUb7sNtTQIAqCV+oQHJ3B8jYYj90n/d7ecTts0BIhavv/mY2UxKYHK8rUcAALVGsuRdYRhiX/Rfc/uvu21TgIi91S8a5ee0t+ck9sfx5JG2FgEA1Bq/4ID4hQdsIGLv9F9r++WpbROAiP3T3ytxjgvPT+y1K20dAgBoBJKnH4qEIvZC/3U2c2ojlkc/9SZXu/tostTWIACARuAXHtAgvCkMRixMf1Xb37Rliz0iDl4/d/caF563WLSjtv4AADQKDcLPRcIRi9AvYOOXnrZFHhHL5emOmUx6aZYeaWsPAECjkA0LHyR+IQIbkNi5fgaS1W7q6pkt7IhYTv2Qr3NdeD5jt14oMm83W3sAABqHNojfiIQkdqJfLfJUFxZzRKyG/sZmVqkszty92NYcAIBGIhPJQvELEtigxLnJjZGI9dBP2+kXpbLnOM7VX8qyo/awNQcAoLFIOz0hEpY4G/3VMG6MRKyXfkjYiAvPd5yL/2prDQBAo/ELEkTCEnelH0LC0uyI9ZUhJp16qWxesI+tNQAAjUcDclUkNHEm/YqRDCFBrL9+zm7/4dpmAM5slrzN1hgAAJjnG+5kaRCaGNfPQmKLMiLWV//h2n/ItlmAMa+STa0DbI0BAIBpJHPrI+GJ2/VfLfs5e20xRsRmeLYLcwHvbO7eZ2sLAADsgF+gIAhPnHLUTc1eYAswIjZLFsrZmTfI+OH3tLUFAAB2wC9QIH6hgjBEm62f8m+5CwsvIjZT/+F7vQuzAj9p6woAAETwCxVEQrS5nuPCYouI6D+Er3NhZjTXLTI6dKitKQAAEMEvVCB+wYIwTJulH6+9yoVFFhFxu36+bv8NmM2PJpolX7H1BAAAdoKG578GYdokuTkSEWerb7q5mfJ2GVv0MFtLAABgJ/gFC8QvXBCGav318+36eXdtUUVE3Jl+kRybJ40x/YGtIwAAMAv8wgVhqNZcPxPJSS4spIiIs/E018SVKbfJeGvI1hAAAJgFfuEC8QsYhOFaT/3NT8xEgojd6mcwadbKlKfZ+gEAAHPAL2AQCdf6ea6bGodpCyciYif6b8qa0nTnQ4+3tQMAAOaAX8BA/EIGNmDrpJ9hgGYbEYvWLwdf97m6M3eurRsAANABGqqfDEK2Lq5xYZFERCxKP0ztfBdmT318hq0ZAADQAX4hA/ELGoRBW239gjZc2UbEXlvfpnuDrRcAANAFkrsvR8K2uvo5c21RRETslb7pPs+FWVRls+QFtlYAAEAXyMQRC8QvbGADt4qudmExRETstf4bNX+Dts2kavpzkXm721oBAABdIu3k+5HQrZYs1Y6Ig7Q2TXfyclsjAACgAPzCBuIXOAiCtyJyZRsRy2D1m+7fyabW3rZGAABAQWjQnhoJ3/LLmG1ELJO+6a7qmO4sfaOtDQAAUCDSbj0uCN+y62cjscUOEXHQVnL2kuRPkg/vb2sDAAAUjIbu2jCES+qIY+o/RCyvVWu6c/duWxMAAKAHSJY+PQjhMsqiNohYBauzIuV1cqG7u60JAADQIzR4JyJhXB79DUlc2UbEqniSq0LT/VFbCwAAoIdI7o6KhHE59Dci0WwjYtU8WR1zYaaVw5sla93X1gIAAOghfsEDDeCfRUJ5sI66qTGRtpAhIlbBFeq4C7Nt0GbJF2wdAACAPiCZOyYI5UHqrwz5r2VtAUNErJKnuTDfButtMp4+2NYAAADoA5IP76VB/NtIOPdff0XoFBcWLkTEKrrShTk3OL9t8x8AAPqI5MkbIuHcf/0VIVuwEBGrrF+wy2Zd/90m7TSx2Q8AAH3EL4CggXxFJKT7p78SZAsVImLV9Td/r3Vh5vXXk2zuAwDAAJB2+u+RkO6PrCKJiHXWN92DXBgnTx9tMx8AAAaAXLDobhrMfw6Cutf6Kz9M/4eIdXdQC+Nk7myb9wAAMEA0nD8ShHUv9dP/0WwjYlP0c3T3f7rAJ9usBwCAASJjyX00nG+KBHbxMv0fIjbR012Yh70zszkPAAAlQPLkvyOhXayZY0YSRGyu/Zq5ZDx5js14AAAoAbJ+0XwN6luD4C7Ss1xYgBARm+R5LszGIs3dJpF5u9mMBwCAkiDt5JtBeBfluY5x24iIy93U0DqbkYWZvtRmOwAAlAgZax2hgb01DPAu9Xfo+zv1beFBRGyifmVdP8TOZmW35u7XMrJ0T5vtAABQMjSwlwch3o2+qKxwYcFBRGyyvVn+/XU20wEAoIRI1vqrSIh37hkuLDSIiCiyxoWZ2bmXy+iS/WymAwBASdHgPisS5nOXlSQREWfWj+f26xLY7OzE3B1rsxwAAEqMTKRPCsJ8rvpx276Y2AKDiIh36BfF6X489zUytuCuNssBAKCEiMzbXbKhlrTT50nufh8J9dnJuG1ExNm7yoU5OieTD9o8BwCAEqAh/RDJ0iP15zs0rL+vDfaP9L9vCYO8A5lvGxFx9vopUzufn/tG2ZgebDMeAAD6iA9imRh6mrTTN0mWfEXDOVOvj4R2Ma5zYTFBRMSd66dOHXdhpu7az9rcBwCAHiEXLLqbtFuPkyx9rQbw59Q16lWRcO6dvlic5MJCgoiIu9bP6mRzdWfmft2E1qskTx5qawIAAHSB5MP7y4Qblsy9TAP3E+pp6mVBEA9CpgBEROzOzqcKvEba6Tn685NaH16i/52wxDsAwC7wq4VJ7pxkyQskTz+kIXqS5MkvIiFbDn2RsIUDERHnpp/dyc/yZDO2M2+UqWGEn1dfpbXkEbJ5wT623gAANAIZH3q4NtfPlnb6bxqKy9QL1dsi4VlOxxxTACIiFuWproipAmdSa0t6gf78muTJGyaHIm5qHWDrEgBAZZENiw/RkHumNtdvUb+u/53L1BUIG4jV8jQXFgxEROxcv3CYzdreepHWpf+TLHmbNuJPldHWPWwNAwAoFTJ++D2lnSzVAHu9Btd/68/zZXJ8XRBw1XetCwsFIiJ2p//W0H97aDO3v/5GPVEy9x5pp38vo0OH2noHANBzZN1hB0rW+isNo1dqKH1KXan//cdIaNVTPyuJn8rKFgpEROxe/+2hzd2Bm/xJf56ufnRykbTxoYfb2ggA0BH+JhMZbw1puLxoKmSSFfrzV2EQNcwzXVggEBGxOP23iDZ7y+ef1bUyeeEpfalMJKksO2oPW0sBACbxUyhpUCyULHmuNtXHqcdrgPxE3RoJmGbrV0WzhQEREYu18wVxBu3Nalvr6RckT18zOZ3tyPx9bd0FgAYwNc46fbvk7jsaDBumA8KGBlr93fMscIOI2B9XujCHq+nt6katvd+ULH2j1pK/kbEFd7W1GQBqhp70L48EAu7KVS4sCIiI2BtPUNe5MIvr48/VH6rvUJ/hJx6w9RoAKo5MrdRoT36cyVE3Ff62ICAiYu9c4Xo5N3f5zN3v9efJ+vN9+vMfJG890NZvAKgQ03NjXx2c7BjXL8hgCwEiIvbes12Yyc3yKvGzgeXpf02uwjyRLLQ1HQBKjJ64/xQ5sdF6rgsLACIi9sdyzM1dNq+TdrpOsvQzkrljJmcUG1m6p63zAFASJHfLIycybpcbJRERB299bqDspVvUCfVL6uukPfQoyYf3t3UfAAaAbEwP1hPzisiJi17/VaYNfkRE7L/nuzCjcefm7i227gPAgNAT8qjgJMWprzD9V5k29BERsf+WcgXKUrvar7Nhaz4ADBBppz+InKzNlhUlERHLpb+nxmY1xrxKRocOtbUeAAaMn/9TT9A/RE7aZuq/urRBj4iIg/VkF+Y1Rkyfb+s8AJQEPUH/PjxpGyrTACIiltNzXJjZeIe5+7qt7wBQMiaXnrUnb9NkGkBExPJ6ojruwuxG78Wy7rADbW0HgJIhFyy62/RKV/Ykbo6nuDDgERGxPJ7lwuzG2yVLHmvrOgCUFMnSp+uJuy1yMtdfrm4jIpZfFsMJzd37bD0HgJKjJ+6Xg5O5Ca5wYbAjImL55Cr3jo6x0iRABfFjwKSdXhI5qevrWhcGOiIillOucm/3epk4YoGt4wBQEWQifZI0ZWiJX8LdTzdlAx0REcvrKhfmedPM01fY+g0AFUNP5s8HJ3cdXePCIEdExHLLVe4Tbd0GgAoiG9O76Am9OXKS10uubiMiVtPmXuW+TPLhe9m6DQAVRfKhx+uJvTVystfDERcGOCIiVsNmXuXeJhNDT7P1GgAqjp7cn4ic8PXwJBcGOCIiVsemXeXO0s/YOg0ANUBG5u+rJ/lPg5O+6jIzCSJi9fVXuZuz+uRPfE22dRoAaoLk6aPFr2QVnvzV9VQXBjciIlbP1S7M+Pq5RcZbQ7Y+A0DN0Kb7Q5EAqKbrXBjYiIhYTU90U1O82qyvk3nyVluXAaCGyKbW3nrSXxiEQBU93YWBjYiI1dVP8Wqzvj6uFpm3m63LAFBTJE8foSf+rZEwqI7rXRjUiIhYbVe4MO/r4VUyOnSorccAUHOknb43EgjV0d/RboMaERGr73kuzPyqm7ujbB0GgAYg+fBeGgITQShUQT9fq7+j3YY0IiJW39NcmPtVNnPfsDUYABqEfuJ24u+YtuFQdv2d7DagERGxPvphgzb7q+mvZN1hB9r6CwANQ7LkXZGAKK/+DnYWukFErLf1WAjndq2xj7V1FwAaiCw7ag8NhbFIUJRTP7bPBjMiItbLE1z1F8LJ3PttzQWABiO5O1zD4aYgLMooUwEiIjbDERfWgMqYjMvI0j1tvQWAhqNN91vCwCiZ/mqHv+phQxkREevnKS6sA9Xwehlb9DBbZwEA5onM210/kZ8XCY7yyM2SiIjNctSFtaDs5ukrbI0FAPgLMnHEAg2LG4LwKIvcLImI2Cyrd/Pkiba2AgAEaFi8PhIgg5ebJRERm+eJLqwH5fUyyYfvZesqAECAyLzdJHNnR4JksJ7hwiBGRMT6u9aFNaF8bpMsfbqtqQAAMyIbFj5Iw+PPkUAZjNwsiYjYXKux8uRnbS0FANglkrlXRgJlMJ7jwgBGRMTmOObC2lAefyIj8/e1dRQAYFZoiJwRCZb+66eGsuGLiIjN8WwX1oZyuEXGW0O2fgIAzBoZHTpUw+SaSMD0T39Vg+EkiIjNdoUL60MZzJK32doJADBnpJ2+NAiYfspwEkRE9K53YY0YpH6CAZm3u62bAAAdocFychA0/ZLhJIiI6PWLn9kaMTivlrH0/rZeAgB0jGSt+2q4XBkJnN7qh5PYwEVExGZapmElWfICWysBALpG2uk/BoHTa/1NMjZwERGxuZZiWEnyTVsjAQAKQ4NmWRg8PZThJIiIuKODn63kVzK24K62PgIAFIZfslbD5vJIABUvw0kQEdHqL8TYetE/b5d263G2NgIAFI6MJ8+JhFDxMpwEERFjDmpYSeY+YGsiAEDPkNx9Jwiioj3VhSGLiIg4kGElybiMLN3T1kMAgJ4hF7q7awBdGgZSQY67MGARERG9/oKMrRu99QYZW/QwWwsBAHqOBtAzI6FUjGtdGLCIiIhev/qwvzBja0evzNwrbQ0EAOgbGkRfDYKpCM90YcAiIiJu11+YsbWjN55kax8AQF/xUyNpGP0mElDdeaILwxUREXG7K11YO4r3Mtmw+N629gEA9B0NpCer2yJB1ZnnuzBYERERd/QkF9aPYt0mWfp0W/MAAAaGZMkXImHVmatdGKyIiIjW3k4P+Flb6wAABopsah2g4XRxJLDmLtMBIiLibDzHhTWkCHO3SUbm72trHQDAwJHMPUGDamsQXHMxc1N3n9tQRUREtJ7uwjrSvVskay2yNQ4AoDRInnw6El6zl+kAERFxti53UxdqbC3pxix5m61tAAClQkaX7KeB9fMgwGbrKhcGKiIi4kye58Ja0rHpOSLzdre1DQCgdMi4W6LBdXsYZLPwFBeGKSIi4kz6G+1tLenMq2Usvb+taQAApUWD66ORMNu5jN9GRMS5epoL60knZskLbS0DACg1snnBPhpgPwkCbWf6rwVtkCIiIu7MQsZxp9+ydQwAoBLIePJIDbLbwmCbQebfRkTEThx1YU2Zvb/yqybbGgYAUBkkcx+OhFtcP72TDVFERMRdOeLCmjI7b5d86PG2dgEAlB4ZWbqnhtgz1G+rN0YCLq7/WtCGKCIi4q48w4U1ZVYm/2lrGABAqZmeneRzkrk/hqG2C/3XgTZAERERZ+NJLqwrs/Onkrt3y/pF821NAwAoDTI+9HANq/dJt8u6++V5bYAiIiLO1jEX1pY5ma6TLH2t5MP3srUOAKDvSNa6rwbTmzSg2mFgdeiZLgxPRETE2epXKra1pTNvVU/VOvciv5ibrYEAAD1DNrUOkDw9WkNolXS6qM3OZMEbRETsxrNcWFu69wZpJ9+UiaGnybKj9rC1EQCgayQf3kuy9Ej9lP8DDZ2bIkFUnCx4g4iI3VjUAjgze7nWxM9Ie+hRtl4CAMwZmUj/WpvsL2q4XBkJnOLlhklEROzWzm+c7MTN0k6Ok4kjFtgaCgAwIzLWOkLD44PaaF8SCZbe6sfd2eBEREScq+MurDG9d0zy5A0yltzH1lYAgHkyOnSoZMnbtMm+IBIg/dOPu7OhiYiIOFfXubDG9M/btKaeKe3kn2VjehdbcwGgQUg+fJCGwcu1yT5Hw2FrJDD6rx93Z0MTERFxrvopZm2NGYw3Sua+K3nyLH8/lK3FAFBDZPOCfWQ8eY422sdrCNwcCYbB6sfd2dBEREScqytdWGMG7xXaeP+3ZMljbX0GgIojMm83bbCXSjv9Xz3Zr4kEQDn04+1sYCIiInbiqS6sM+XyV5K590vuDrd1GwAqhIy3hvSE/qj6u8iJXj79eDsbmIiIiJ243IV1przm2ni/RTYsPsTWcgAoIZK3HihZ8i49eX8cOaHLLUu6IyJikXa9xHvf9fdTrZbMvUzWHXagrfEAMEBktHUPPUFfpa6NnLzVcZULwxIREbFTz3NhramO/j6rZeo/yKbW3rb2A0AfkJH5+0qWvEBPxJPUWyInavU8w4VhiYiI2KkjLqw11fQq9UuSub+x/QAAFIzIvN0lS/9Wcvd1PfGui5yQ1XaFC8MSERGxU/03p7bWVN/faOP9YWmnie0TAKALZMIN6wn2CfXSyIlXH090YVgiIiJ2qv/m1NaaerlRfYeMpfe3vQMAzAIZTx8sufsPPZF+GjnB6idTAiIiYtGe4sJ6U0+3qSOSp6+QCxbdzfYUALADsmHxvSVLX6snzfmRk6nenu/CoEREROxG/82prTf1d4vkbrm00+f5xe5srwHQSCQf3l9PjBfrCXKqemvkxGmGa10YlIiIiN14gpq5sOY0x2vVr0reeqJfBM/2IAC1RkaW7ilZ+nT99PktPRFuiJwgzZM5uBERsReud2HNaaK5+73k6X9J1lpk+xKAWqFv9Edro/0ZfeNfHpwITZc5uBERsRdWey7u3pi7TdJO/002LHyQ7VUAKomMLXqYvqnfq2/wXwZveLzD01wYkoiIiN3qv0G1NQd3MDlP8uTVMn74PW0PA1BqZCy5j2TpG/VNPB6+sTGqv5PchiQiImK3nuXCmoMxb9G+ZYW003+U0SX72d4GoBTIxvQu+iZ9qb5hV6q3R97IuDNPcmFIIiIidutKF9Yc3JXXS+a+oQ34U2TZUXvYngegr0g+vJdMJH+nb8zvqTdG3rA4W5e7MCQRERG79XQX1hyci39QPyVZ669sHwTQU6TdepxkyRf0k9+fIm9M7EQ/dZMNSURExG491YU1Bzv155K59+jPh9jeCKAQZCJZqG+yD0jufh15A2I3jrkwIBEREYtwhQvrDnZv5tbrz9fLxvRg2zMBzAnZsPgQyZO36htqInijYXGOujAgERERi7CZq03209vU0yVL/skv5md7KYAoMrbgrvqp7Rj1bH0DbY28sbBo17kwIBEREYvQD1m0dQd75Y2Su+/oz2f4Rf5sjwUNRza19tY3yLP1DfJD9ebIGwh7Kcu6IyJiLx13Ye3B3pq5P+rPz+mxX2L7LmgY+mZ4gjbaX9Y3xNXBGwX754gLwxEREbEoWd590F6s/db7pJ0eZnsxqCn6gjt94T+i/jbyhsBBeLYLwxEREbEoz3dh7cFB2dbG+02SL7yf7dGg4kiWPEAb7Xfqi3xh5IXHQbvKheGIiIhYlOe5sPbgoPWLBK6SPD1aNrUOsL0bVAS50N1dMvdKfTHXqNsiLzSWRRpuRETspf5eIVt7sEzeJO30B+rf+/vqbE8HJUNG5u+rL9bz9YU7Ud0SeUGxjPpld204IiIiFiUNd5W8Uv0fmUj/2vZ5MEBE5u2uL8qT9MX5mvrnyAuHZfdMF4YjIiJiUa5xYe3BCpheIu3kg5INtWz/B31C2kOLJUs/Lrn7ffgCYaWk4UZExF7qZ8OytQcrZnqB+nYZHTrU9oRQMLJ+0Xw92P+uB/6i8IXAynqGC8MRERGxKGm46+Q27QXPkXbycsmHD7K9InSIHsx7SZ6+Rg/seZGDjnXwdBeGIyIiYlGe48Lag3XwZu0Pj5csea5sXrCP7SFhF8jokv3008s/Spaeogfz1sgBxjpJw42IiL3Ur/dgaw/WzWu0d/xfbcCXiszbzfaWMI0sO2oPyZOn6oH6ph60GyIHEuvqqS4MR0RExKJc7cLag3X2d9pPfkzGW0O232ws0h56lDban9aD84fIAcMmSMONiIi9lIa7yf5YsuRdkrceaHvQ2uOX89RPHsdpo/2LyIHBpnmKC8MRERGxKM9yYe3BJrpW/Ve/bovtTWuJmTv72sgBwSZJw42IiL2Uhhu3z+k91jrC9qWN4C+rQ+ZuubA6ZDNlSAkiIvZSGu6meqX2mF9k1UqDXOjuLnn6Cj1AIzI5z2Jw4LCOnubCcERERCxKxnA3yZu0yf6BZOmRkg/vZXtNMEiWPEBy9049cBsjBxPrJA03IiL2Uubhrru3q6skT4+WTa0DbE8Js0QbbyeZ+7AezN9EDjJWXebhRkTEXkrDXVfb0k7fJFnrvrZ3hC7RxvsJ2oB/WQ/y1ZEDj1WUpd0REbGXsrR7nbxY+8D3yfjQw22PCD1ANrX21gP+bD3wy2Ryac/gBcGqSMONiIi9dI0Law9Wx8z9UX9+TsbdEtsPQh+RsQV31RfjGPVsfUG2Bi8UltuVLgxHRETEolzrwtqDZfdG9dvqM2Rk6Z6294MBIxsWHyJ58lZ9gSYiLx6WURpuRETspTTcVfE29XTJ3EskH97f9nhQUmQiWagv2gckd7+OvKhYFle5MBwRERGL8lwX1h4sk6Pq62VjerDt5aBiSLv1OMmSL0g7+VPkhcZB6hcksOGIiIhYlOe5sPbgoP25ZO49+vMhtmeDGuAnQZc8eZa+wN+TqfFB9g2A/fZsF4YjIiJiUY66sPbgILxM/ZSMJ4+0/RnUGNmY3kXa6UslS86UqXFD9o2B/dBP12TDERERsSjHXFh7sF9eL5n7hrSTp8iyo/awvRg0DBlL7iN58gZ9Q4xH3izYS/3YOhuOiIiIRZm5sPZgL71F+6kVkiUvlNEl+9meC2ASmThigb5RjtM3zObImwiLdp0LwxEREbEIl7uw7mBvzNy5kievlvHD72l7K4CdInn6aMnSz+gb6fLgjYXFuN6FAYmIiFiEJ7mw7mBx5m6TtNN/kw0LH2R7KIA548cdycTQ0/RN9S19g90QvOGwc8ddGJCIiIhFuMKFdQe7M3e/lzz9L8lai2y/BFAYfjJ2fbO9WN90p6q3Bm9EnLs2IBEREYvwVBfWHOzEa9WvSt56osi83WxvBNBTtPm+l2Tpa/VNeH7kzYmz1Y+xsyGJiIjYrWe4sObgbN0iuVsu7fR5snnBPrYHAhgIMp4+WN+Y79Y36E8jb1rcmSe7MCQRERG7daULaw7uzG3qiOTpK+SCRXezvQ5AqZAJN6xv2E+ol0bezGg9xYUhiYiI2K1+NWNbczA0dz/Sn++QsfT+tqcBKD0i83aXLP1bfSN/Xd/Ifw7e4Dil/8rPhiQiImK3nuPCmoPb/Y3k6YeknSa2fwGoLDIyf19tvI/SN/hJMjkpfPDGb66rXBiSiIiI3XqeC2tOs71K/ZJk7m9snwJQO2S0dQ99w79KXRM5GZony7sjImIvHHVhzWmeN0nu/k9//oNsau1texKARiB564F6IhyrJ8KPIydJM/RXIGxIIiIidmtzl3Xfqq7W/X+ZrDvsQNt7ADQamUhSPUE+qv42cvLUV1abRETEoj3RhfWm/uaSu7dIvvB+tscAAIOfVF7ayVLJkq/oyXNN5ISql/4KxAkuDEtERMRObc6iN7/SOvp+bbQPt/0EAMwSP9m8jCfP0Qb8eD2pbo6caPXQX4mwYYmIiNipZ7qw1tTHK9TPS5Y81vYNANAlkg8fpI33y6WdniNT47PsCVhd/ZUIG5aIiIidWr85uG+UzH1X8uRZ2g/sZXsEAOgBMjp0qH6yfZs23xdETsrqyVzciIhYpGtcWGuq523qGdJO/lk2pnexvQAA9BEZax2hJ+MHtfm+JHKyVsPVLgxLRETETq32HNxjkif/T8aS+9iaDwAlQPKhx2vj/UU9Wa+MnMDl1V+JsGGJiIjYqWMurDVlNk9+Ie3kOJk4YoGt7QBQUvz4LsnSI7X5/oH4Se/tiV02z3dhWCIiInbichfWmXL6B220Py3toUfZOg4AFUM2tQ6QPD1aT+xV6u2RE37wMjUgIiIWZbmnBLxB2sk3tdF+qiw7ag9bswGgBkjWuq+00zfpCZ9FQmCwnuzC0ERERJyrK11YYwbrrZKlp2j9fZGMLtnP1mYAqDEyPvRwPfnfq0FwcSQc+i8zlSAiYhGe48IaMxDTdZKnr5F8+F62BgNAA5GJ9DEaDp+TzP0xDIw+ebYLQxMREXGurnNhjemfF0nu3i3rF823tRYAYBIZWbqnhsUz1G/L5DizIEh657kuDE1ERMS56O8HGndhjemtl0qWflzy9BG2rgIA7BTJh/eXzL1Eg+R0mZp83wZMsa53YXAiIiLORX8/kK0vvfHP6tdkIn2SyLzdbQ0FAJgzsmHxvTVYXq+ORkKnOP1UTjY8ERERZ+vpLqwtxblFPVHa6fNlZP6+tlYCABSGhs1DJHPv0Z8/i4RRd/qpnGx4IiIiztazXFhbunObukbr3ivlQnd3WxMBAHqOjCeP1CD6pHpZJKTm7ioXhiciIuJsXevC2tKZF0ru3ilZ8gBb+wAABoKfvF/ayVMkc9/QkLouElyz0welDU9ERMTZ2t2S7r9VP6KNtrN1DgCgVPhJ/SVLXqjN9/mRMNu5PihteCIiIs7Gk1xYV3bt1dpgf1lr1hNsPQMAKDUy2rqHBtjvI8G2a31g2hBFRETclWe6sKbEzJOt+vOHWqeeLZtae9saBgBQCSaDzAbcbGXFSURE7MQRF9aUmczS19raBQBQGSRzLwuCbS76JXltiCIiIu7KURfWlJm9XvLkobaGAQCUHvFTBnZz06T3fBeGKCIi4s706zjYerIrM3cui9YAQKWYmqmkgIVxMscCOIiIODdPc2E9mZ1vtvUMAKC0TC+GY4OsM31w2jBFREScydUurCWz8yZpp4fZmgYAUDokTx+toXVbJMg60wenDVNERMSZPM+FtWS2Zm69/5bW1jYAgNIgm1oHaGBtDgKsG31w2jBFRESMeYKbGo5oa8lczN2xtr4BAJQGaaf/GwRXtzKOGxERZ2vn47d3dIvWs8TWOACAgSPjyXMioVWMjONGRMTZ6KeTtTWkM3MZWbqnrXUAAANDNiw+RMPpykhgFaNfwMCGKiIionVu82/v3My9x9Y7AICBIDJvNw2mlUFQFemYC0MVERFxR09yYf3ozlulPbTY1j0AgL4j7fRNkZAq3pNdGK6IiIjbXenC2tG9G2VTa29b+wAA+oa/qUTD6OZIQBXvKheGKyIi4nbPdWHtKMTkg7b+AQD0Bdm8YB/xn/yDYOqRTA+IiIgzWcR0gDN7m7SHHmXrIABAz9EA+mQklHon0wMiIuJMFjMd4M68SEbm72trIQBAz9DgebK6NRJIvZXpARERMWZx0wHObJZ+3NZDAICeIKOte0jufh8EUT9kekBERIy53oU1o3i3Srv1OFsXAQAKR9rJ8ZEQ6o9+ekA/Ts8GLSIiNtdTXFgveudmyYf3t7URAKAwJHPHRMKnvzKsBBERd/RsF9aK3vpZWx8BAApB8uShGjLXRYKnvzKsBBERd7Q/w0l2dKvkrSfaOgkA0BWy7Kg9NGBGI6HTf8cdw0oQEXHK/g4nucPc/VrWHXagrZcAAB0j7eS4IGwGKcNKEBHR24/ZSWb2S7ZeAgB0hEykj5HJSf+DoBmca1wYuoiI2Cz9t53+ZnpbI/rnNpkYepqtmwAAc8J/XaaB8stIyAxWvwgOw0oQEZvtqS6sD/33d5IPH2TrJwDArNEg+VokXMrh6S4MX0REbI6DHU5yh5n7hq2fAACzQtrp84JQKZNrXRi+iIjYHAc7nOTOZumRto4CAOwU2bD4EA2QK4NAKZN+WMmJLgxgRESsv/5bTlsXButlfiVmW08BAKKIzNtNg2NVJEzK5yoXhjAiItZf/y2nrQmD93u2pgIARNHAeHMkRMrpqAtDGBER663/dtPWg9KYPt/WVQCAOyG5cxoYN4cBUmL9Xeo2jBERsb6udmEtKI9XyMb0YFtfAQAmkc0L9tGguDASHuWWpd4REZujnxK2/0u5z9H0BFtjAQAm0ZD4VBgaFdDfPLnchaGMiIj10680bOtAGc3di22dBYCGI+3kKeJXzLKBURVXujCUERGxfpbzZsmYV0m+8H623gJAQ5Hxw++pwXBpJCyq4/kuDGVERKyX/mZJ/62mrQHl9VRbcwGgofixZpGQqJ4rXBjOiIhYH/1UsDb7y27mjrF1FwAahrSTlwfhUFXXuDCcERGxPpb+Zsmo10qWPMDWXwBoCDJxxAINgusj4VBNWXkSEbG+nuHC3K+Oq/yicrYOA0DNkZGle2oAjEVCodqe7cKQRkTE6uvv1bGZXyXz5NW2FgNAzZHcvS8Igzo47qbmaLVBjYiI1dUvcGbzvnpeL+Ppg209BoCaok3pEj3xb4uEQT1kikBExHp5rguzvpqOMLQEoAHIusMO1BP+4kgI1Ed/U40Na0RErKYnuzDnq2yevMHWZgCoGZK7rwcnfx31N9fY0EZExOo54sKMr7Y3ytiih9n6DAA1Qdrp8yMnfj1lIRxExOpbvYVuZuv5IvN2t3UaACqOjA4dKn6Z2fCkr6/+Jhsb3oiIWB1XuzDba2P6dlurAaDC+Bs09OReHZ7sNfc8F4Y3IiJWw+XqmAuzvT7eLGOtI2zNBoCKIrl7S+REb4Zc5UZErKa1vrr9FzO/Loat2wBQMWQiSfWE3hI5yZvhOheGOCIillt/dduvq2AzvY7m7t22dgNAhZCR+fvqyfzj4ORumlzlRkSsln7VYJvl9fUWGW8N2RoOABVB8uTTkRO7eXKVGxGxOvqZSZpydfsvphdIPryXreMAUHJkYuhpehJvC0/qhnqaC0MdERHLZ7Oubt9h5t5vazkAlBj9lHwvPXkvC07mJstVbkTE8lvfebdn420ynjzS1nQAKCl60p4YOZHxdBeGOyIilsdzXJjdzfInsnnBPrauA0DJkDx9ReQERq9fffIEFwY8IiIO3mZf3d7Rj9raDgAlQiaOWKAn6vWRkxe3e6YLQx4REQfvGhdmdjO9XcbdElvjAaAE+Inz9SQdi5y4uKN+1TKuciMilstTXJjXzfbnMrpkP1vrAWDA+LubIycsxvSrl9mwR0TEwelvbLdZ3XTz5NO21gPAAJEseaz4r6DsyYpx/RjBk1wY+IiI2H/PcGFOo3er1qsn2JoPAANAZN5uelL+UJq8fHsnrnVh6CMiYn/1S7j7oX42o3G7F8um1gG29gPAgJgcwz3eGtJPw8dIln5G2uk6PVGvi5y8uF2WfEdEHKxnuTCb0Zh+0dZ8ACgZMpEslCx5gTbhH9cTd5V6VXgyN1Q/TaANf0RE7I9+aB/TAM7GbdJOnmLrOwCUHMlbD5TcPXv6RsuTpcmrUq50YRFARMTe64f22UzGmfyNjC24q63nAFAxppeDf6Y24u/UT9LH639vjpzw9dOPHfRjCG0hQETE3umH9Nk8xl35VVu7AaAGaBN+kDbfS6WdvklP9G+rF4q/azoMgWrrF1uwxQAREXujXwthvQuzGHdtnjzL1moAqCHahO8vWeuv9MR/nfoldULqMEMKN1AiIvZHvxaCzWCM+XP1RGknH5TcHaU6P0GCrcsA0BDCGVLc+VK1Jeb91RZWoERE7K0nO26UDL1UPU39hB6bl8mEG/YXt2ytBQCIMj1DygsrM0PK2S4sDoiIWJx+diibvc3xSnWN+jmti6+VdutxcsGiu9naCQDQNbJh4YPumCElWSFlmyHlFBcWCERE7N5VLszceurXwMi01n158h6oPHmqbEwPtvUQAKCvTM6QkifP0nA6dnqGlF9GAqw/Mjc3ImLx+jm3x12YudV2i9atH+nP76nvkCw9UsbTB9saBwBQWu6YIcW9WQPtOzI1Q8q2SOAVr1/5zBYLRETs3HNdmLXV0deei6SdnqD16H3683mSDbVE5u1uaxcAQOWZnCGlPfQo6fUMKf6GHn81xhYMREScu2e6MGfL628kS0+RdvIxrQUv0ZqzWEbm72vrEQBAo5icISVrLSp8hpR1jllLEBG7tbxDSa5QV0uefFp//uvkxRxWcgQAmBsy1jpihxlSzlKvjgTuzvVzxdrigYiIs/c8F2Zrf71WMrde68EXtLl+g0ykT/L3DdmaAQAABSHt1kcjYbxzWRAHEbEz/f0wNlN7500yOcww/ZY212+bvBE/Sx5g6wAAAPQQGV2yn4bx5ZGQ3rlj6nIXFhJERJxZP8Vqbxa4uV39sfpDyd1/yHjyHP15uM18AAAYABrOr48E9+xc48JigoiIcf1FCr96r83SuXuxerL6kckhghNJKptae9t8BwCAEiD58F7STi+JhPnsPcOFRQUREUNHXJihc/M0bawPsFkOAAAlRvL06Eigz01/l/2JLiwsiIh4h6e7MD/n7q2yftF8m+UAAFBS/MIEMrlgQRDoc9ffbW+LCyIiTukvShQ1BWCe/LfNcwAAKCmSJc8Ngrwbz3ZhkUFEbLp+3QK/foHNzM69SbLWfW2mAwBACdHQbkeCvDv9V6a22CAiNtlzXJiV3fsRm+kAAFAypJ08JRLg3eunujrZhQUHEbGJ9m7p9j/LBYvuZrMdAABKhIb1SCTAi3HUsfQ7IuIK16v5tqdN/91mOwAAlAQZd0vC4C7YtS4sPoiITdHfJFnMfNs78wrJh/e3GQ8AACVA2smKSHAX7yoXFiFExLrrv+E714WZ2Avz5A024wEAYMBI7pyG9LYgtHvlqS4sRoiIdXa1C7Owd/7WL2Bmsx4AAAaIZO67kcDunWOORXEQsTme5sIc7LWZO8ZmPQAADAjJk4dqON8WhHWv9TdRLndhYUJErJP+JsmiFreZmz/zC5nZzAcAgAGgofw/kaDuj34lSmYuQcS6epKb+kbPZl+/zN1RNvMBAKDPyIbFh2gobwlCup+OuLBIISJWXf8Nnv8mz2Zef52wuQ8AAH1GsvTjkYDuv2e5sFghIlZV/82d/wbPZt0gzNKn2+wHAIA+IaOte2gYXx+E86A8w4VFCxGxivpv7mzGDc61Nv8BAKBPSDs5LhLMg9OvvMZ0gYhYdf03djbfBm7rcbYGAABAj5GN6V00hK8MQ3nA+puLTnZhAUNErIL+mzqba+XwVFsHAACgx0ju3hIJ5HLolz32d/bbQoaIWGb9N3T+mzqbaeVwm4y3hmwtAACAHiGbF+yjDffvI4FcHn3TzcI4iFgVy91sT5t839YDAADoEVoUXhkGcQllYRxErIKnuAo025PeLhNHLLA1AQAACkaWHbWHhu7mSBCX0/MdTTciltfBrSLZmbn7sq0LAABQMNJOXxQEcNn1c9nSdCNi2fQ3eA9yFcnO3CKjQ4fa2gAAAAUhMm83DduNkQAuvywBj4hlctBLtnfnJ2x9AACAgpCJ5O8iwVsdz3U03Yg4eP0N3f7GbptR1fF6GT/8nrZGAABAAWjInh8J3mq51tF0I+LgrH6zPW36XlsjAACgSyRzTwgDt6JypRsRB2G1h5FYr5JNrQNsrQAAgC6QLDkzErjVlRspEbGfVvMGyZ2bJ2+1tQIAADpEJtxwELR1cJ1jcRxE7L11bLanvNQvhGZrBgAAdICG6g8jQVsP/TzdNN2I2Cv9ojb1bLa3+ypbMwAAYI5IOz1MA3VrJGTro1+RkqYbEYvWL9depUVtOvOXfkE0WzsAAGAOSO6+HgnY+ulnDaDpRsSi9M12NZZr797cvdjWDgAAmCWStx6oYXprEK511X/t65dZtoUTEXEunuma02xPeaFfGM3WEAAAmAUaop+NBGu99V//nubCAoqIOBtXuzBXmmCWHmlrCAAA7ALZmB6sIXpjEKpNcaULCyki4kz6uf3XuDBLmuOorSMAALALpJ18MBKozfJsxwI5iLhr/f0ffm5/myGNM1lqawkAAMyA5MMHaXheE4ZpA2UpeETcmb7Z9jMd2exopittPQEAgBmQ3B0bCdLmygI5iBjTz7Fd/2n/5uaEG7Y1BQAADDK6ZD8NzcuDEG26ftpAv1qcLbiI2ExPd02biWSWJsfbugIAAAYNzNeFAYqT+itZZ7iw8CJis/T3d9h8wO1uldwdbmsLAABMI/nwXtJOL4kEKO6oL7a2ACNi/eXmyNmZu6/b+gIAANNInh4dBCfG9UV3uQsLMiLWUz9e2y+OZbMAY97qF06zNQYAoPH4VcI0JC+KBCfOpC++vgjbwoyI9bJ5K0cW4WdtnQEAaDySJc+NBCbuSl+EWSQHsZ76KUFHXHje42y80S+gZmsNAECj0XBsRwITZ6svygwxQayPJ7mpKUHtuY5zMPmgrTUAAI1FQ/EpYVDinPWLX6xwYeFGxGrJEJKivNYvpGZrDgBAI5F2ek4kKLFTV7mwgCNi+fXfUvnVZe05jZ2bu2NtzQEAaBwy7pYEAYnd67+K9l9J24KOiOX0VMcsJL3xcr+gmq09AACNQtrJikhAYhH6hXL8anS2sCNiefQ3Rvq59RlC0ktfZ2sPAEBjkNw5DcJtkXDEIuWGSsRy6r+FOt+F5ywWbHqJX1jN1iAAgEYgmftuGIzYE9e7qa+sbcFHxP7rr2r7ey38t1D2XMXemKdH2xoEAFB7JE8eqiF4WxCK2FvXOK52Iw7Skx3T/Q3Gi/wCa7YWAQDUGg2//4kEIvZDxnYj9l9/VXu1Y6z2IM2S59paBABQW2TD4kM0/Lb8//buBEqSos7j+HAKIgiICsIi6OgwVGb2YIvCooAKiPeBiLq6wAPvhRVF0UXBAxWVVbxFWWXFlRVUUBQQlBEZmu6sbMYBhoXlUMDlPuRmhpn5b/yzemDmH9HTXdV15PH9vPd78KarMyOiuqL+lZUZ6U2GpL/5k8uZsV8YEEK6m7Pj1jr59jVI+p2mfT8CgMqSLPlKYCIkg4ge7dZbw+vRN1skEEJmFn1dXRhzVLtQifa270kAUDky0tjcTXoP+JMgGWj0nFLuUklI96KnbbGudgGTXGjflwCgcqQZHetPgKQw0aNxnGZCSOfRiyIvjv3XFilOxpNd7HsTAFSGLEo2cpPdXd7kR4oVTjMhpP3o6j/6gdW+nkgR8yv7/gQAlSFZ/OHAxEeKGr3Ii7W7CVlz9IOpfkDl9JEyZYU0k8i+RwFA6cnixvqu4P5bYOIjRY+uZqJfk9tCg5C6Rz+QsvpIOZPFP7HvUwBQepLG7/YmPFKe6CoLnN9NSCt6gbF+ELWvk06TcjBiANEbrz3HvlcBQGnJ6fuv4ya2awMTHilb9PxuvXkHd6skdYx+03NR7L8uZpo0Pliajd3c///G+xnpYZLv2vcrACgtN6m9zZ/oSA9yn8uXXLLAz7obCm9Sp2ih/cfYfx10K2ONocfnS/f/ksU/c/++zHsc6XYekWzuVqu+XwFAKYnMWsu9efw5MNGRriW6043xJ+XyeLN8zJvJyf5jehS9UOyCmMKbVDNnucyP/b/77uZRyYbX8+bOsaHnT7yWlwR+h3Qt0Zft2ANA6UgWvcaf4EhXkl+EmnzIvVk/ebUx13+zj+11tPA+P2YpQVKN6LUK/btDZLbq69eS0WQbN4+e6B73UOB3ycxz/8qDFQBQWm4yuyQwwZGZJIv+V7LkUF35xY63co/Zy/udfkULbz3VhIsrSRmjp470r9BuJY1+YF/DIe6D9RbSjI5zv3Ovtw0ys6TxMXa8AaA03CS2hzexkc6jp+ak0QF6Eaod61VJ2tjS+91+Z+WqJiwnSMoQXd6vFxdDTidZ8n77Gl4TGZ29iZsLjnK/e5u3LdJhojv1xmx2rAGgFFxxeJ4/sZH2kyzQU3Ps+K5J/gbibWcA0cJbl087O/aLHEIGnXNcFsT+320/0+FtxmX+dhu43/+gmx/+6m2TdJIj7BgDQOFJlrxA8rt5eZMamX7OdQXr7nZsp8P97vzA9gYbLWy0wLFFDyH9zMo7Q14a+3+j/c8yew1Gu2T+nuu6+fZAt62rAtsn08/Nk52mBwCF5SavMwITGpk6y6UZ/VyaQzvZMW2H2863AtsuRrTQ0QssOc+b9DMrz8/WJS3t3+SgksWL7Wu3U/mKUGn0ZunHsqBVTZYcascVAApLmskcyQvHwIRGJstS9+b7I5cd7Hh2QrLofYF9FC963uxvY784IqQb0eUqz4sHf9rI5DnVvna7wb3+95EifstV9OgF6VNcIwMAheEmrh96ExmZLA+7fEOyxrZ2HGdCxpOXBvZV3LC6Celm9Nbrun52kY5mh5LFH7av3W5y/d9VmtGvvf2SyZNGB9hxBIDC0cJR9GitncSIzd8lS74gi5Jn2DHsBl1XNrDP4mflRZZ61Js1vUk70Q9rem72JbH/d1XUZI2X2dduL8h4lLjC+zTh7pXTSLLQjh8AFI7o0VpvAiOPJ41vlzT6hGTDT7Vj122tG+ME2lCW6NFJvZ02xTeZLCtPGdEPaf1cO7s7WSEL521qX7e9JOM7znbzwvdF727pt4c8kVfbsQOAwpDLdnq6cDe0yXKjZNHhMrLrhnbceqVSyzJq8a0XvOl6ybboIvWKFtnnxmUtslfN9fY12y9urn6W2/9XXR4MtIs0o4vtmAFAYbhJ6vP+xFX7XO2KgoMlG17PjlevSZqcEGhP+aPne68svjnyXY/o86xLSupFtuUusldJ9HP7mu03GdvhaW48P+vac4/fvppnPHmpHS8AGLj87mfccnjVXCbN5C0is9a2Y9Uv7o30oEC7qhUtvvXiOD3iqUc+baFGypuz4tY52eU/kj1JkqPta3ZQZMGcjV17PuradavfztrmHDtOADBwksUfD0xYdcxFkib72vEZBEkbOwfaV+3o8m8XxNzZsozRo9j6rYV+ezES+89t9VK484Tl2tlP0lvNu7bdEGhv3bJipvdDAICu0vOS3eR0W2DCqlN+I83GbnZsBkkWJRtJnddD16PfetGlHv1mucFiRm9GozdBquxR7DUkbWxpX7NFkd+9Mo3e6dp5pdfuOiWLf2bHBgAGxk1MH/QmqnpkmU7IMtYYsmNSFK6N1wXaXc/oHS61ANfTFHSdZlv8kd5Gj2DrNw9aYOu52EVfI7u3ucW+Vosov3tlFr/RtTcN9KEOWSZjQ8+34wIAfacXA0oz+Wtgoqpylrg+nyyj855nx6NoXFt/FWg/0WjBp0dW9RQULsDsfvScer3Q8Q9x61Sfuh3BXnN+a1+rRefavJd7Dv8Q6EvFk5xsxwIA+k6y5EB/gqpsHpIsOlFGk23sOBQVK8e0ES0I9aYpehRcj8Lq+t+cijK96AWOOl764UU/xNTjHOwZJDrOvlbLws35L3Z9OEvyc5xtvyqZJWWa8wFUUP51YzO+KjBBVS336BukZMNb2DEoOmkmbw/0h7QTPRJ+cdy6mE9vsqKnRdR1RRTtt34boB9IdHUY/YBS71NDOkyyn32tlo3rQ+Q+pP6X689jfv8ql6/Z/gNA30gavTkwMVUpt0kWH6VLHtq+l4VrfxzoF+lG9KJMPVVCz0f+fdw6N1yP8uqFgGU9PUULam2/ngqi/dHTQfSItRbWnBLSvYwl29vXalm5/jzH5XtS7btXPljGAy4AKsJNQs3AxFT+ZPFfRC8Enb/dBrbPZSO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")}),
     uses(PNlib(version = "2.2"), Modelica(version = "3.2.3")));
diff --git a/PowerPlants/WindPowerPlant.mo b/PowerPlants/WindPowerPlant.mo
index af3a074d20c483b7a1b2f57b3b6112bddef25d76..da768b8b76cde89b3e5f88edde4e378b1c1ed245 100644
--- a/PowerPlants/WindPowerPlant.mo
+++ b/PowerPlants/WindPowerPlant.mo
@@ -20,7 +20,7 @@ model WindPowerPlant
     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(
+  Backend.EnergeticTransitionWithoutActivator t1(arcWeightOut = {currentPower}, maximumSpeed = 1/3600, 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;
diff --git a/PowerPlants/package.order b/PowerPlants/package.order
index d85628c4015cf6b0cbef3c952a705ae1fec6d45e..c0e67a4a8c6948707233a5cd7f25dd1759513367 100644
--- a/PowerPlants/package.order
+++ b/PowerPlants/package.order
@@ -2,3 +2,5 @@ PVPowerPlant
 HydrogenCHPPlant
 STEPowerPlant
 WindPowerPlant
+FuelCell
+NaturalGasPowerPlant
diff --git a/PowerToX/Electrolyser.mo b/PowerToX/Electrolyser.mo
index f64b09f1332d059790cff8d7b51d8e46277d2bb0..ea39ec8b718a5614334c4dc2337db64fca2141e9 100644
--- a/PowerToX/Electrolyser.mo
+++ b/PowerToX/Electrolyser.mo
@@ -1,7 +1,7 @@
 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(
+  PNlib.Components.TC Electrolyser(arcWeightIn = {39.4*activation.t, 9.1*activation.t, 1}, arcWeightOut = {1.1*activation.t, 8*activation.t}, maximumSpeed = 1/3600, 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)));
@@ -13,41 +13,17 @@ model Electrolyser
     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(
+  PNlib.Components.TC t1(arcWeightIn = {2}, maximumSpeed = 1/3600, 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(
+  Backend.EnergeticFlowPlace p1(nIn = 1, nOut = 1)  annotation(
     Placement(visible = true, transformation(origin = {70, 50}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
-  PNRG.Backend.EnergeticFlowPlace p13 annotation(
+  PNRG.Backend.EnergeticFlowPlace p13(nIn = 1, nOut = 1)  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(
@@ -56,6 +32,14 @@ equation
     Line(points = {{80, 50}, {110, 50}}));
   connect(p13.outTransition[1], O2Out) annotation(
     Line(points = {{80, -50}, {110, -50}}));
+  connect(activation, splitLogicalInput.logicalInput) annotation(
+    Line(points = {{-110, 60}, {-90, 60}, {-90, 42}, {-86, 42}}));
+  connect(splitLogicalInput.test_output, Electrolyser.inPlaces[3]) annotation(
+    Line(points = {{-66, 44}, {-20, 44}, {-20, -38}, {0, -38}, {0, -28}}));
+  connect(EnergyIn, Electrolyser.inPlaces[1]) annotation(
+    Line(points = {{-110, 0}, {-40, 0}, {-40, -48}, {0, -48}, {0, -28}}));
+  connect(WaterIn, Electrolyser.inPlaces[2]) annotation(
+    Line(points = {{-110, -58}, {0, -58}, {0, -28}}));
   annotation(
     uses(PNlib(version = "2.2")),
     Diagram,
diff --git a/Sources/ConstantSource.mo b/Sources/ConstantSource.mo
index 1ccfb7612e7e2c4c23b4d38219234c57382b064b..fb74698e8609fccc29ebc73c04ee4e94053a179c 100644
--- a/Sources/ConstantSource.mo
+++ b/Sources/ConstantSource.mo
@@ -1,28 +1,29 @@
-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
+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}, maximumSpeed = 1/3600, nOut = 1) annotation(
+    Placement(visible = true, transformation(origin = {14, 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)));
+  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;
+  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 = {{18, 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 = "Const
+
+Input")}));
+end ConstantSource;
diff --git a/Sources/FileToTransitionOutput.mo b/Sources/FileToTransitionOutput.mo
index 89d8824941168657fa6ae7d82bc8fe93dcce37c5..077ffae28c028038260e0d93b4696b7d2676de36 100644
--- a/Sources/FileToTransitionOutput.mo
+++ b/Sources/FileToTransitionOutput.mo
@@ -13,7 +13,7 @@ model FileToTransitionOutput
     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(
+  PNlib.Components.TC t12(arcWeightOut = {max(NOut*combiTable1D.y[1], 0)}, maximumSpeed = 1/3600, 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)));
diff --git a/Sources/StochasticSource.mo b/Sources/StochasticSource.mo
new file mode 100644
index 0000000000000000000000000000000000000000..e1ab003a08fe5f402549df4f54c49e97064d5539
--- /dev/null
+++ b/Sources/StochasticSource.mo
@@ -0,0 +1,39 @@
+within PNRG.Sources;
+
+model StochasticSource
+  // 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 = {p11.t}, maximumSpeed = 1/3600, nOut = 1) annotation(
+    Placement(visible = true, transformation(origin = {0, 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)));
+  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)));
+  PNlib.Components.TDS t1(arcWeightOut = {1}, h = 0.01, nOut = 1)  annotation(
+    Placement(visible = true, transformation(origin = {0, 40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNlib.Components.PC p11(nIn = 1, nOut = 1) annotation(
+    Placement(visible = true, transformation(origin = {24, 40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+  PNlib.Components.TDS t11(arcWeightIn = {1}, h = 0.01, nIn = 1)  annotation(
+    Placement(visible = true, transformation(origin = {50, 40}, extent = {{-10, -10}, {10, 10}}, rotation = 0)));
+equation
+  cumulativeOutput = p1.t;
+  for i in 1:NOut loop
+    connect(p1.outTransition[i], fileOutput[i]) annotation(
+      Line(points = {{80, 0}, {110, 0}}));
+  end for;
+  connect(t1.outPlaces[1], p11.inTransition[1]) annotation(
+    Line(points = {{4, 40}, {14, 40}}, thickness = 0.5));
+  connect(p1.outTransition[1], fileOutput[1]) annotation(
+    Line(points = {{78, 0}, {110, 0}}, thickness = 0.5));
+  connect(t12.outPlaces[1], p1.inTransition[1]) annotation(
+    Line(points = {{4, 0}, {58, 0}}, thickness = 0.5));
+  connect(p11.outTransition[1], t11.inPlaces[1]) annotation(
+    Line(points = {{34, 40}, {46, 40}}, 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 = "Stochastic
+Input")}));
+end StochasticSource;
diff --git a/Sources/package.order b/Sources/package.order
index 6183ce8516a6aae294f8d5925c627fd5da7c003f..933e3e379569e97a2b30d80d12c7a0e6dba84d38 100644
--- a/Sources/package.order
+++ b/Sources/package.order
@@ -1,2 +1,3 @@
 FileToTransitionOutput
 ConstantSource
+StochasticSource
diff --git a/Storage/Battery.mo b/Storage/Battery.mo
index 69220e4b4d6e606d04c4733516a54bf3a5ad6900..ff16cc7768fd347d3b5ab56e45bf8b67a1917511 100644
--- a/Storage/Battery.mo
+++ b/Storage/Battery.mo
@@ -10,7 +10,7 @@ model Battery
   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(
+  PNlib.Components.TC t1( arcWeightIn = {power*logicalInput.t*(1 - full.t), 1},arcWeightOut = {power}, maximumSpeed = 1/3600, 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)));
@@ -18,7 +18,7 @@ model Battery
     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(
+  PNlib.Components.TC t11(arcWeightIn = {power, 1}, arcWeightOut = {power*logicalInput1.t*(1 - empty.t)}, maximumSpeed = 1/3600, 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)));
@@ -30,7 +30,7 @@ model Battery
     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(
+  PNlib.Components.TC dump(arcWeightIn = {2, 2}, maximumSpeed = 1/3600, 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)));
diff --git a/Storage/CO2Storage.mo b/Storage/CO2Storage.mo
new file mode 100644
index 0000000000000000000000000000000000000000..df6250bf2f32bba58a320dc7b6adb74f6d8ab2cc
--- /dev/null
+++ b/Storage/CO2Storage.mo
@@ -0,0 +1,96 @@
+within PNRG.Storage;
+
+model CO2Storage
+  Boolean isEmpty;
+  Boolean isFull;
+  Boolean isFilling;
+  Boolean isEmptying;
+  Real maxInputMassFlow "maxInputFlow" annotation(
+    Dialog(enable = true, group = "Properties"));
+  Real maxOutputMassFlow "maxInputFlow" annotation(
+    Dialog(enable = true, group = "Properties"));
+  
+  Interfaces.CO2Output co2Output 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.CO2Input co2Input 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}, maximumSpeed = 1/3600, 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( 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}, maximumSpeed = 1/3600, 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)}, maximumSpeed = 1/3600, 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
+  isEmpty = empty.t == 1;
+  isFull = full.t == 1;
+  isFilling = logicalInput.t == 1 and not isFull;
+  isEmptying = logicalInput1.t == 1 and not isEmpty;
+  
+  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(co2Input, t1.inPlaces[1]) annotation(
+    Line(points = {{-110, 0}, {-44, 0}}));
+  connect(p1.outTransition[1], co2Output) 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 CO2Storage;
diff --git a/Storage/H2Tank.mo b/Storage/H2Tank.mo
index 99dd3ae6f4b5ff27b6242f9f307fbf7bbb01ba68..5c788bee51f587582335e2c2ad74d54a1bec68ca 100644
--- a/Storage/H2Tank.mo
+++ b/Storage/H2Tank.mo
@@ -1,11 +1,20 @@
 within PNRG.Storage;
 
 model H2Tank
+  Boolean isEmpty;
+  Boolean isFull;
+  Boolean isFilling;
+  Boolean isEmptying;
+  Real maxInputMassFlow "maxInputFlow" annotation(
+    Dialog(enable = true, group = "Properties"));
+  Real maxOutputMassFlow "maxInputFlow" annotation(
+    Dialog(enable = true, group = "Properties"));
+  
   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(
+  PNlib.Components.TC dump(arcWeightIn = {2, 2}, maximumSpeed = 1/3600, 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)));
@@ -19,7 +28,7 @@ model H2Tank
     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(
+  PNlib.Components.TC t1(arcWeightIn = {maxInputMassFlow*logicalInput.t*(1 - full.t), 1}, arcWeightOut = {maxInputMassFlow}, maximumSpeed = 1/3600, 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)));
@@ -31,13 +40,18 @@ model H2Tank
     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(
+  PNlib.Components.TC t11(arcWeightIn = {maxOutputMassFlow, 1}, arcWeightOut = {maxOutputMassFlow*logicalInput1.t*(1 - empty.t)}, maximumSpeed = 1/3600, 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
+  isEmpty = empty.t == 1;
+  isFull = full.t == 1;
+  isFilling = logicalInput.t == 1 and not isFull;
+  isEmptying = logicalInput1.t == 1 and not isEmpty;
+
   connect(splitLogicalInput1.logicalInput, logicalInput1) annotation(
     Line(points = {{80, 60}, {110, 60}}, color = {53, 28, 117}));
   connect(t12.outPlaces[1], partiallyFilled.inTransition[1]) annotation(
diff --git a/Storage/O2Tank.mo b/Storage/O2Tank.mo
index eb341f4a7c3270b71e0c9fa2ba0ea701bfb022dc..d1d1b9320506e1f2b96488b02773b17e5f467939 100644
--- a/Storage/O2Tank.mo
+++ b/Storage/O2Tank.mo
@@ -1,11 +1,20 @@
 within PNRG.Storage;
 
 model O2Tank
+  Boolean isEmpty;
+  Boolean isFull;
+  Boolean isFilling;
+  Boolean isEmptying;
+  Real maxInputMassFlow "maxInputFlow" annotation(
+    Dialog(enable = true, group = "Properties"));
+  Real maxOutputMassFlow "maxInputFlow" annotation(
+    Dialog(enable = true, group = "Properties"));
+  
   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(
+  PNlib.Components.TC dump(arcWeightIn = {2, 2}, maximumSpeed = 1/3600, 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)));
@@ -19,7 +28,7 @@ model O2Tank
     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(
+  PNlib.Components.TC t1(arcWeightIn = {maxInputMassFlow*logicalInput.t*(1 - full.t), 1}, arcWeightOut = {maxInputMassFlow}, maximumSpeed = 1/3600, 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)));
@@ -31,13 +40,18 @@ model O2Tank
     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(
+  PNlib.Components.TC t11(arcWeightIn = {maxOutputMassFlow, 1}, arcWeightOut = {maxOutputMassFlow*logicalInput1.t*(1 - empty.t)}, maximumSpeed = 1/3600, 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
+  isEmpty = empty.t == 1;
+  isFull = full.t == 1;
+  isFilling = logicalInput.t == 1 and not isFull;
+  isEmptying = logicalInput1.t == 1 and not isEmpty;
+  
   connect(splitLogicalInput1.logicalInput, logicalInput1) annotation(
     Line(points = {{80, 60}, {110, 60}}, color = {53, 28, 117}));
   connect(t12.outPlaces[1], partiallyFilled.inTransition[1]) annotation(
diff --git a/Storage/WaterTank.mo b/Storage/WaterTank.mo
index 5043ecb10a94e1fd94b7292d0f92175e361ed87a..5b7a5cb2947949166a33fce5e613e944564ab2ad 100644
--- a/Storage/WaterTank.mo
+++ b/Storage/WaterTank.mo
@@ -1,20 +1,26 @@
 within PNRG.Storage;
 
 model WaterTank
+  Boolean isEmpty;
+  Boolean isFull;
+  Boolean isFilling;
+  Boolean isEmptying;
   Real maxInputMassFlow "maxInputFlow" annotation(
     Dialog(enable = true, group = "Properties"));
   Real maxOutputMassFlow "maxInputFlow" annotation(
     Dialog(enable = true, group = "Properties"));
+  parameter Real startFilling "startFilling" 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(
+  PNlib.Components.PC storage(maxMarks = 100, nIn = 1, nOut = 1, startMarks = startFilling) 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(
+  PNlib.Components.TC t11(arcWeightIn = {maxOutputMassFlow, 1}, arcWeightOut = {maxOutputMassFlow*logicalInput1.t*(1 - empty.t)}, maximumSpeed = 1/3600, 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)));
@@ -28,21 +34,26 @@ model WaterTank
     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(
+  PNlib.Components.T t12(arcWeightIn = {1}, arcWeightOut = {1}, firingCon = storage.t > 0.01, 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(
+  PNlib.Components.TC t1(arcWeightIn = {maxInputMassFlow*logicalInput.t*(1 - full.t), 1}, arcWeightOut = {maxInputMassFlow}, maximumSpeed = 1/3600, 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(
+  PNlib.Components.TC dump(arcWeightIn = {2, 2}, maximumSpeed = 1/3600, 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
+  isEmpty = empty.t == 1;
+  isFull = full.t == 1;
+  isFilling = logicalInput.t == 1 and not isFull;
+  isEmptying = logicalInput1.t == 1 and not isEmpty;
+
   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(
diff --git a/Storage/package.order b/Storage/package.order
index 113be9070b38bc40c99f2af175d73f668641ae59..889a34c8f514eabd4e974dcae18ee681c3300356 100644
--- a/Storage/package.order
+++ b/Storage/package.order
@@ -2,3 +2,4 @@ WaterTank
 H2Tank
 O2Tank
 Battery
+CO2Storage
diff --git a/data.txt b/data.txt
index 0e614fa37bd584183cc44b4214522eb68339800e..4e5992b20f373e9f6524743482c8d50b19f05e35 100644
--- a/data.txt
+++ b/data.txt
@@ -1,27 +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
+  3600   0.01
+  7200   0.04
+  10800  0.09
+  14400  0.16
+  18000  0.24
+  21600  0.27
+  25200  0.28
+  28800  0.27
+  32400  0.24
+  36000  0.16
+  39600  0.04
+  43200  0.01
+  46800  0
+  50400  0
+  54000  0
+  57600  0
+  61200  0
+  64800  0
+  68400  0
+  72000  0
+  75600  0
+  79200  0
+  82800  0
   
\ No newline at end of file
diff --git a/data2.txt b/data2.txt
index ec05d5a17bbea4dc36d7429ebf207903c97375c4..b602d16af59b944d44ade664fd24644b083149fd 100644
--- a/data2.txt
+++ b/data2.txt
@@ -1,27 +1,26 @@
 #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
+  0      0
+  3600   1
+  7200   2
+  10800  3
+  14400  4
+  18000  5
+  21600  6
+  25200  7
+  28800  8
+  32400  9
+  36000  10
+  39600  11
+  43200  12
+  46800  13
+  50400  14
+  54000  0
+  57600  0
+  61200  0
+  64800  0
+  68400  1
+  72000  3
+  75600  6
+  79200  0
+  82800  0
\ No newline at end of file
diff --git a/data3.txt b/data3.txt
index 63e1112a4f93bee4ad7d5a5d5d7c0e9e6cfd67c3..e22206f35419e91a7f0560bcc7e3ab22861bad65 100644
--- a/data3.txt
+++ b/data3.txt
@@ -1,27 +1,26 @@
 #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
+  0      2
+  3600   2
+  7200   2
+  10800  3
+  14400  4
+  18000  5
+  21600  6
+  25200  4
+  28800  8
+  32400  9
+  36000  18
+  39600  18
+  43200  18
+  46800  13
+  50400  14
+  54000  2
+  57600  2
+  61200  0
+  64800  0
+  68400  1
+  72000  3
+  75600  6
+  79200  0
+  82800  1
\ No newline at end of file
diff --git a/data4.txt b/data4.txt
index ae085404420c79dcf265a9f88bec7e0243258226..71f6ed8ad2d0cdfa70090f282a061153c35b5572 100644
--- a/data4.txt
+++ b/data4.txt
@@ -1,27 +1,26 @@
 #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
+  0      1
+  3600   2
+  7200   3
+  10800  4
+  14400  3
+  18000  2
+  21600  1
+  25200  1
+  28800  1
+  32400  2
+  36000  3
+  39600  4
+  43200  3
+  46800  2
+  50400  1
+  54000  1
+  57600  1
+  61200  2
+  64800  3
+  68400  4
+  72000  3
+  75600  2
+  79200  1
+  82800  1
\ No newline at end of file