Workflow for a district optimization¶

In this application of the ETHOS.FINE framework, a small district is modeled and optimized.

All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.

The workflow is structures as follows:

1. Required packages are imported and the input data path is set
2. An energy system model instance is created
3. Commodity sources are added to the energy system model
4. Commodity conversion components are added to the energy system model
5. Commodity storages are added to the energy system model
6. Commodity transmission components are added to the energy system model
7. Commodity sinks are added to the energy system model
8. The energy system model is optimized
9. Selected optimization results are presented

1. Import required packages and set input data path¶

The ETHOS.FINE framework is imported which provides the required classes and functions for modeling the energy system.

In [1]:

import fine as fn
from getData import getData

data = getData()

%load_ext autoreload
%autoreload 2
%matplotlib inline

import fine as fn from getData import getData data = getData() %load_ext autoreload %autoreload 2 %matplotlib inline

2. Create an energy system model instance¶

The structure of the energy system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.

The commodities are specified by a unit (i.e. 'GW_electric', 'GW_H2lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified.

In [2]:

locations = data["locations"]
commodityUnitDict = {"electricity": "kW_el", "methane": "kW_CH4_LHV", "heat": "kW_th"}
commodities = {"electricity", "methane", "heat"}
numberOfTimeSteps = 8760
hoursPerTimeStep = 1

locations = data["locations"] commodityUnitDict = {"electricity": "kW_el", "methane": "kW_CH4_LHV", "heat": "kW_th"} commodities = {"electricity", "methane", "heat"} numberOfTimeSteps = 8760 hoursPerTimeStep = 1

The modeled locations include 6 buildings (bd1 to bd6), which are connected to different grids (grid1 to grid7), and the location of the transformer.

In [3]:

nbval-check-output

locations

locations

Out[3]:

{'bd1',
 'bd2',
 'bd3',
 'bd4',
 'bd5',
 'bd6',
 'grid1',
 'grid2',
 'grid3',
 'grid4',
 'grid5',
 'grid6',
 'grid7',
 'transformer'}

In [4]:

esM = fn.EnergySystemModel(
    locations=locations,
    commodities=commodities,
    numberOfTimeSteps=8760,
    commodityUnitsDict=commodityUnitDict,
    hoursPerTimeStep=1,
    costUnit="€",
    lengthUnit="m",
    verboseLogLevel=2,
)

esM = fn.EnergySystemModel( locations=locations, commodities=commodities, numberOfTimeSteps=8760, commodityUnitsDict=commodityUnitDict, hoursPerTimeStep=1, costUnit="€", lengthUnit="m", verboseLogLevel=2, )

3. Add commodity sources to the energy system model¶

Electricity Purchase¶

In [5]:

esM.add(
    fn.Source(
        esM=esM,
        name="Electricity purchase",
        commodity="electricity",
        hasCapacityVariable=False,
        operationRateMax=data["El Purchase, operationRateMax"],
        commodityCost=0.298,
    )
)

esM.add( fn.Source( esM=esM, name="Electricity purchase", commodity="electricity", hasCapacityVariable=False, operationRateMax=data["El Purchase, operationRateMax"], commodityCost=0.298, ) )

Natural Gas Purchase¶

In [6]:

esM.add(
    fn.Source(
        esM=esM,
        name="NaturalGas purchase",
        commodity="methane",
        hasCapacityVariable=False,
        operationRateMax=data["NG Purchase, operationRateMax"],
        commodityCost=0.065,
    )
)

esM.add( fn.Source( esM=esM, name="NaturalGas purchase", commodity="methane", hasCapacityVariable=False, operationRateMax=data["NG Purchase, operationRateMax"], commodityCost=0.065, ) )

PV¶

In [7]:

esM.add(
    fn.Source(
        esM=esM,
        name="PV",
        commodity="electricity",
        hasCapacityVariable=True,
        hasIsBuiltBinaryVariable=True,
        operationRateMax=data["PV, operationRateMax"],
        capacityMax=data["PV, capacityMax"],
        interestRate=0.04,
        economicLifetime=20,
        investIfBuilt=1000,
        investPerCapacity=1400,
        opexIfBuilt=10,
        bigM=40,
    )
)

esM.add( fn.Source( esM=esM, name="PV", commodity="electricity", hasCapacityVariable=True, hasIsBuiltBinaryVariable=True, operationRateMax=data["PV, operationRateMax"], capacityMax=data["PV, capacityMax"], interestRate=0.04, economicLifetime=20, investIfBuilt=1000, investPerCapacity=1400, opexIfBuilt=10, bigM=40, ) )

4. Add conversion components to the energy system model¶

Boiler¶

In [8]:

esM.add(
    fn.Conversion(
        esM=esM,
        name="Boiler",
        physicalUnit="kW_th",
        commodityConversionFactors={"methane": -1.1, "heat": 1},
        hasIsBuiltBinaryVariable=True,
        hasCapacityVariable=True,
        interestRate=0.04,
        economicLifetime=20,
        investIfBuilt=2800,
        investPerCapacity=100,
        opexIfBuilt=24,
        bigM=200,
    )
)

esM.add( fn.Conversion( esM=esM, name="Boiler", physicalUnit="kW_th", commodityConversionFactors={"methane": -1.1, "heat": 1}, hasIsBuiltBinaryVariable=True, hasCapacityVariable=True, interestRate=0.04, economicLifetime=20, investIfBuilt=2800, investPerCapacity=100, opexIfBuilt=24, bigM=200, ) )

5. Add commodity storages to the energy system model¶

Thermal Storage¶

In [9]:

esM.add(
    fn.Storage(
        esM=esM,
        name="Thermal Storage",
        commodity="heat",
        selfDischarge=0.001,
        hasIsBuiltBinaryVariable=True,
        capacityMax=data["TS, capacityMax"],
        interestRate=0.04,
        economicLifetime=25,
        investIfBuilt=23,
        investPerCapacity=24,
        bigM=250,
    )
)

esM.add( fn.Storage( esM=esM, name="Thermal Storage", commodity="heat", selfDischarge=0.001, hasIsBuiltBinaryVariable=True, capacityMax=data["TS, capacityMax"], interestRate=0.04, economicLifetime=25, investIfBuilt=23, investPerCapacity=24, bigM=250, ) )

Battery Storage¶

In [10]:

esM.add(
    fn.Storage(
        esM=esM,
        name="Battery Storage",
        commodity="electricity",
        cyclicLifetime=10000,
        chargeEfficiency=0.95,
        dischargeEfficiency=0.95,
        chargeRate=0.5,
        dischargeRate=0.5,
        hasIsBuiltBinaryVariable=True,
        capacityMax=data["BS, capacityMax"],
        interestRate=0.04,
        economicLifetime=12,
        investIfBuilt=2000,
        investPerCapacity=700,
        bigM=110,
    )
)

esM.add( fn.Storage( esM=esM, name="Battery Storage", commodity="electricity", cyclicLifetime=10000, chargeEfficiency=0.95, dischargeEfficiency=0.95, chargeRate=0.5, dischargeRate=0.5, hasIsBuiltBinaryVariable=True, capacityMax=data["BS, capacityMax"], interestRate=0.04, economicLifetime=12, investIfBuilt=2000, investPerCapacity=700, bigM=110, ) )

6. Add commodity transmission components to the energy system model¶

Cable Electricty¶

In [11]:

esM.add(
    fn.Transmission(
        esM=esM,
        name="E_Distribution_Grid",
        commodity="electricity",
        losses=0.00001,
        distances=data["cables, distances"],
        capacityFix=data["cables, capacityFix"],
    )
)

esM.add( fn.Transmission( esM=esM, name="E_Distribution_Grid", commodity="electricity", losses=0.00001, distances=data["cables, distances"], capacityFix=data["cables, capacityFix"], ) )

Natural Gas Pipeline¶

In [12]:

esM.add(
    fn.Transmission(
        esM=esM,
        name="NG_Distribution_Grid",
        commodity="methane",
        distances=data["NG, distances"],
        capacityFix=data["NG, capacityFix"],
    )
)

esM.add( fn.Transmission( esM=esM, name="NG_Distribution_Grid", commodity="methane", distances=data["NG, distances"], capacityFix=data["NG, capacityFix"], ) )

7. Add commodity sinks to the energy system model¶

Electricity Demand¶

In [13]:

esM.add(
    fn.Sink(
        esM=esM,
        name="Electricity demand",
        commodity="electricity",
        hasCapacityVariable=False,
        operationRateFix=data["Electricity demand, operationRateFix"],
    )
)

esM.add( fn.Sink( esM=esM, name="Electricity demand", commodity="electricity", hasCapacityVariable=False, operationRateFix=data["Electricity demand, operationRateFix"], ) )

Heat Demand¶

In [14]:

esM.add(
    fn.Sink(
        esM=esM,
        name="BuildingsHeat",
        commodity="heat",
        hasCapacityVariable=False,
        operationRateFix=data["Heat demand, operationRateFix"],
    )
)

esM.add( fn.Sink( esM=esM, name="BuildingsHeat", commodity="heat", hasCapacityVariable=False, operationRateFix=data["Heat demand, operationRateFix"], ) )

8. Optimize energy system model¶

All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call.

In [15]:

esM.aggregateTemporally(numberOfTypicalPeriods=7)

esM.aggregateTemporally(numberOfTypicalPeriods=7)

In [16]:

# esM.optimize(timeSeriesAggregation=True, optimizationSpecs='cuts=0 method=2')
esM.optimize(
    timeSeriesAggregation=True, solver=fn.utils.ImplementedSolvers.STANDARD_SOLVER.value
)

# esM.optimize(timeSeriesAggregation=True, optimizationSpecs='cuts=0 method=2') esM.optimize( timeSeriesAggregation=True, solver=fn.utils.ImplementedSolvers.STANDARD_SOLVER.value )

GLPSOL--GLPK LP/MIP Solver 5.0
Parameter(s) specified in the command line:
 --write C:\Users\T58C8~1.GRO\AppData\Local\Temp\tmp43bqanym.glpk.raw --wglp
 C:\Users\T58C8~1.GRO\AppData\Local\Temp\tmp3oq8qsv1.glpk.glp --cpxlp C:\Users\T58C8~1.GRO\AppData\Local\Temp\tmpkcuvn3tw.pyomo.lp
Reading problem data from 'C:\Users\T58C8~1.GRO\AppData\Local\Temp\tmpkcuvn3tw.pyomo.lp'...
C:\Users\T58C8~1.GRO\AppData\Local\Temp\tmpkcuvn3tw.pyomo.lp:201320: warning: lower bound of variable 'x8' redefined
C:\Users\T58C8~1.GRO\AppData\Local\Temp\tmpkcuvn3tw.pyomo.lp:201320: warning: upper bound of variable 'x8' redefined
32542 rows, 15260 columns, 88192 non-zeros
32 integer variables, all of which are binary
201352 lines were read
Writing problem data to 'C:\Users\T58C8~1.GRO\AppData\Local\Temp\tmp3oq8qsv1.glpk.glp'...
170387 lines were written
GLPK Integer Optimizer 5.0
32542 rows, 15260 columns, 88192 non-zeros
32 integer variables, all of which are binary
Preprocessing...
30488 rows, 12838 columns, 78508 non-zeros
32 integer variables, all of which are binary
Scaling...
 A: min|aij| =  4.128e-03  max|aij| =  8.333e+02  ratio =  2.019e+05
GM: min|aij| =  3.663e-01  max|aij| =  2.730e+00  ratio =  7.454e+00
EQ: min|aij| =  1.342e-01  max|aij| =  1.000e+00  ratio =  7.454e+00
2N: min|aij| =  1.113e-01  max|aij| =  1.500e+00  ratio =  1.347e+01
Constructing initial basis...
Size of triangular part is 25424
Solving LP relaxation...
GLPK Simplex Optimizer 5.0
30488 rows, 12838 columns, 78508 non-zeros
      0: obj =  -1.925690861e+03 inf =   2.383e+10 (10343)
Perturbing LP to avoid stalling [1001]...
   8212: obj =   1.902965375e+05 inf =   4.336e-06 (0) 72
Removing LP perturbation [10910]...
* 10910: obj =   1.321555860e+05 inf =   5.083e-09 (0) 23
OPTIMAL LP SOLUTION FOUND
Integer optimization begins...
Long-step dual simplex will be used
+ 10910: mip =     not found yet >=              -inf        (1; 0)
+ 11844: >>>>>   1.332570326e+05 >=   1.327597180e+05   0.4% (18; 5)
+ 14302: >>>>>   1.332507742e+05 >=   1.328801486e+05   0.3% (57; 11)
+ 15103: >>>>>   1.332487877e+05 >=   1.328810415e+05   0.3% (65; 15)
+ 16659: >>>>>   1.332383678e+05 >=   1.329290858e+05   0.2% (79; 19)
+ 16680: >>>>>   1.329371437e+05 >=   1.329295671e+05 < 0.1% (77; 28)
+ 16725: mip =   1.329371437e+05 >=     tree is empty   0.0% (0; 199)
INTEGER OPTIMAL SOLUTION FOUND
Time used:   11.3 secs
Memory used: 44.7 Mb (46916100 bytes)
Writing MIP solution to 'C:\Users\T58C8~1.GRO\AppData\Local\Temp\tmp43bqanym.glpk.raw'...
47811 lines were written

9. Selected results output¶

Sources and Sink¶

In [17]:

esM.getOptimizationSummary("SourceSinkModel", outputLevel=2)

esM.getOptimizationSummary("SourceSinkModel", outputLevel=2)

Out[17]:

			bd1	bd2	bd3	bd4	bd5	bd6	grid1	grid2	grid3	grid4	grid5	grid6	grid7	transformer
Component	Property	Unit
BuildingsHeat	operation	[kW_th*h/a]	213008.3393	211961.37319	212160.201606	212865.469106	212987.945931	213360.620262	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
		[kW_th*h]	213008.3393	211961.37319	212160.201606	212865.469106	212987.945931	213360.620262	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Electricity demand	operation	[kW_el*h/a]	24718.958697	19409.339737	24892.818629	17439.830164	23310.014158	19718.252364	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
		[kW_el*h]	24718.958697	19409.339737	24892.818629	17439.830164	23310.014158	19718.252364	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Electricity purchase	NPVcontribution	[€]	0	0	0	0	0	0	0	0	0	0	0	0	0	30842.495427
	TAC	[€/a]	0	0	0	0	0	0	0	0	0	0	0	0	0	30842.495427
	commodCosts	[€/a]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	30842.495427
	operation	[kW_el*h/a]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	103498.306802
		[kW_el*h]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	103498.306802
NaturalGas purchase	NPVcontribution	[€]	0	0	0	0	0	0	0	0	0	0	0	0	0	91270.980537
	TAC	[€/a]	0	0	0	0	0	0	0	0	0	0	0	0	0	91270.980537
	commodCosts	[€/a]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	91270.980537
	operation	[kW_CH4_LHV*h/a]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1404168.931345
		[kW_CH4_LHV*h]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1404168.931345
PV	NPVcontribution	[€]	1926.398664	0.0	1863.363974	0.0	0.0	0.0	0	0	0	0	0	0	0	0
	TAC	[€/a]	1916.398664	0.0	1853.363974	0.0	0.0	0.0	0	0	0	0	0	0	0	0
	capacity	[kW_el]	17.888917	0.0	17.277015	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	capexCap	[€/a]	1842.816913	0.0	1779.782223	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	capexIfBuilt	[€/a]	73.58175	0.0	73.58175	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	commissioning	[kW_el]	17.888917	0.0	17.277015	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	invest	[€]	26044.483248	0.0	25187.821238	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	isBuilt	[-]	1.0	0.0	1.0	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	operation	[kW_el*h/a]	11358.66816	0.0	14752.64199	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
		[kW_el*h]	11358.66816	0.0	14752.64199	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	opexIfBuilt	[€/a]	10.0	0.0	10.0	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

In [18]:

fig, ax = fn.plotOperationColorMap(esM, "PV", "bd1")

fig, ax = fn.plotOperationColorMap(esM, "PV", "bd1")

In [19]:

fig, ax = fn.plotOperationColorMap(esM, "Electricity demand", "bd1")

fig, ax = fn.plotOperationColorMap(esM, "Electricity demand", "bd1")

In [20]:

fig, ax = fn.plotOperationColorMap(esM, "Electricity purchase", "transformer")

fig, ax = fn.plotOperationColorMap(esM, "Electricity purchase", "transformer")

In [21]:

fig, ax = fn.plotOperationColorMap(esM, "NaturalGas purchase", "transformer")

fig, ax = fn.plotOperationColorMap(esM, "NaturalGas purchase", "transformer")

Conversion¶

In [22]:

esM.getOptimizationSummary("ConversionModel", outputLevel=2)

esM.getOptimizationSummary("ConversionModel", outputLevel=2)

Out[22]:

			bd1	bd2	bd3	bd4	bd5	bd6	grid1	grid2	grid3	grid4	grid5	grid6	grid7	transformer
Component	Property	Unit
Boiler	NPVcontribution	[€]	883.607351	980.718203	937.643993	1004.157289	956.361348	955.452658	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	TAC	[€/a]	859.607351	956.718203	913.643993	980.157289	932.361348	931.452658	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	capacity	[kW_th]	88.823444	102.021126	96.1672	105.206574	98.71095	98.587456	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	capexCap	[€/a]	653.57845	750.689302	707.615092	774.128388	726.332447	725.423757	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	capexIfBuilt	[€/a]	206.028901	206.028901	206.028901	206.028901	206.028901	206.028901	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	commissioning	[kW_th]	88.823444	102.021126	96.1672	105.206574	98.71095	98.587456	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	invest	[€]	11682.344434	13002.1126	12416.720027	13320.65743	12671.094992	12658.745602	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	isBuilt	[-]	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	operation	[kW_th*h/a]	213048.223269	211990.635343	212185.711381	212891.770831	213017.011012	213383.858478	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
		[kW_th*h]	213048.223269	211990.635343	212185.711381	212891.770831	213017.011012	213383.858478	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	opexIfBuilt	[€/a]	24.0	24.0	24.0	24.0	24.0	24.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

In [23]:

fig, ax = fn.plotOperationColorMap(esM, "Boiler", "bd1")

fig, ax = fn.plotOperationColorMap(esM, "Boiler", "bd1")

Storage¶

In [24]:

esM.getOptimizationSummary("StorageModel", outputLevel=2)

esM.getOptimizationSummary("StorageModel", outputLevel=2)

Out[24]:

			bd1	bd2	bd3	bd4	bd5	bd6	grid1	grid2	grid3	grid4	grid5	grid6	grid7	transformer
Component	Property	Unit
Battery Storage	operationCharge	[kW_el*h/a]	-0.0	0.0	0.0	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
		[kW_el*h]	-0.0	0.0	0.0	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	operationDischarge	[kW_el*h/a]	-0.0	0.0	0.0	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
		[kW_el*h]	-0.0	0.0	0.0	0.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Thermal Storage	NPVcontribution	[€]	244.728977	223.545122	211.68068	214.875936	210.820358	210.313178	0	0	0	0	0	0	0	0
	TAC	[€/a]	244.728977	223.545122	211.68068	214.875936	210.820358	210.313178	0	0	0	0	0	0	0	0
	capacity	[kW_th*h]	158.340652	144.551657	136.828854	138.908711	136.268854	135.93872	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	capexCap	[€/a]	243.256702	222.072847	210.208405	213.403661	209.348083	208.840902	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	capexIfBuilt	[€/a]	1.472275	1.472275	1.472275	1.472275	1.472275	1.472275	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	commissioning	[kW_th*h]	158.340652	144.551657	136.828854	138.908711	136.268854	135.93872	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	invest	[€]	3823.175639	3492.239765	3306.892507	3356.809053	3293.452493	3285.529273	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	isBuilt	[-]	1.0	1.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	operationCharge	[kW_th*h/a]	23892.887954	15657.545754	19326.481737	14554.58571	19826.532299	26109.918425	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
		[kW_th*h]	23892.887954	15657.545754	19326.481737	14554.58571	19826.532299	26109.918425	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	operationDischarge	[kW_th*h/a]	23853.003985	15628.283602	19300.971962	14528.283984	19797.467218	26086.680208	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
		[kW_th*h]	23853.003985	15628.283602	19300.971962	14528.283984	19797.467218	26086.680208	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

In [25]:

fig, ax = fn.plotOperationColorMap(
    esM, "Thermal Storage", "bd1", variableName="stateOfChargeOperationVariablesOptimum"
)

fig, ax = fn.plotOperationColorMap( esM, "Thermal Storage", "bd1", variableName="stateOfChargeOperationVariablesOptimum" )

Transmission¶

In [26]:

esM.getOptimizationSummary("TransmissionModel", outputLevel=2)

esM.getOptimizationSummary("TransmissionModel", outputLevel=2)

Out[26]:

				bd1	bd2	bd3	bd4	bd5	bd6	grid1	grid2	grid3	grid4	grid5	grid6	grid7	transformer
Component	Property	Unit	LocationIn
E_Distribution_Grid	capacity	[kW_el]	bd1	NaN	NaN	NaN	NaN	NaN	NaN	10000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
			bd2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10000.0	NaN	NaN	NaN	NaN	NaN	NaN
			bd3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10000.0	NaN	NaN	NaN	NaN	NaN
			bd4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10000.0	NaN	NaN	NaN
			bd5	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10000.0	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
NG_Distribution_Grid	operation	[kW_CH4_LHV*h]	grid4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	NaN	703221.904353	NaN	NaN	NaN
			grid5	NaN	NaN	NaN	234180.947914	NaN	NaN	NaN	NaN	NaN	0.0	NaN	469040.956439	NaN	NaN
			grid6	NaN	NaN	NaN	NaN	234318.712113	NaN	NaN	NaN	NaN	NaN	0.0	NaN	234722.244326	NaN
			grid7	NaN	NaN	NaN	NaN	NaN	234722.244326	NaN	NaN	NaN	NaN	NaN	0.0	NaN	NaN
			transformer	NaN	NaN	NaN	NaN	NaN	NaN	1404168.931345	NaN	NaN	NaN	NaN	NaN	NaN	NaN

102 rows × 14 columns