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Update openTEPES_OutputResults.py
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@erikfilias
erikfilias committed Dec 5, 2024
1 parent 0142c04 commit 5d19551
Showing 1 changed file with 19 additions and 19 deletions.
38 changes: 19 additions & 19 deletions openTEPES/openTEPES_OutputResults.py
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Original file line number Diff line number Diff line change
Expand Up @@ -604,12 +604,12 @@ def GenerationOperationHeatResults(DirName, CaseName, OptModel, mTEPES, pIndTech
for gt,chp in mTEPES.t2g:
g2t[gt].append(chp)

for p,sc,n,ch in mTEPES.psnch:
if ch not in mTEPES.bo:
OptModel.vTotalOutputHeat[p,sc,n,ch] = OptModel.vTotalOutput[p,sc,n,ch]/mTEPES.pPower2HeatRatio[ch]

for p,sc,n,hp in mTEPES.psnhp:
OptModel.vTotalOutputHeat[p,sc,n,hp] = OptModel.vESSTotalCharge[p,sc,n,hp]/mTEPES.pProductionFunctionHeat[hp]
# for p,sc,n,ch in mTEPES.psnch:
# if ch not in mTEPES.bo:
# OptModel.vTotalOutputHeat[p,sc,n,ch] = OptModel.vTotalOutput[p,sc,n,ch] / mTEPES.pPower2HeatRatio[ch]
#
# for p,sc,n,hp in mTEPES.psnhp:
# OptModel.vTotalOutputHeat[p,sc,n,hp] = OptModel.vESSTotalCharge[p,sc,n,hp] / mTEPES.pProductionFunctionHeat[hp]

OutputToFile = pd.Series(data=[OptModel.vTotalOutputHeat[p,sc,n,chp]() for p,sc,n,chp in mTEPES.psnchp], index=pd.Index(mTEPES.psnchp))
OutputToFile *= 1e3
Expand All @@ -618,7 +618,7 @@ def GenerationOperationHeatResults(DirName, CaseName, OptModel, mTEPES, pIndTech
# tolerance to consider 0 a number
pEpsilon = 1e-6

sPSNG = [(p,sc,n,ch) for p,sc,n,ch in mTEPES.psnch if OptModel.vTotalOutputHeat[p,sc,n,ch].ub - OptModel.vTotalOutputHeat[p,sc,n,ch]() > pEpsilon]
sPSNG = [(p,sc,n,ch) for p,sc,n,ch in mTEPES.psnch if OptModel.vTotalOutputHeat[p,sc,n,ch].ub and OptModel.vTotalOutputHeat[p,sc,n,ch].ub - OptModel.vTotalOutputHeat[p,sc,n,ch]() > pEpsilon]
OutputToFile = pd.Series(data=[(OptModel.vTotalOutputHeat[p,sc,n,ch].ub*OptModel.vGenerationInvest[p,ch]() - OptModel.vTotalOutputHeat[p,sc,n,ch]()) if ch in mTEPES.gc or ch in mTEPES.bc else
(OptModel.vTotalOutputHeat[p,sc,n,ch].ub - OptModel.vTotalOutputHeat[p,sc,n,ch]()) for p,sc,n,ch in sPSNG], index=pd.Index(sPSNG))
OutputToFile *= 1e3
Expand Down Expand Up @@ -1102,7 +1102,7 @@ def NetworkHeatOperationResults(DirName, CaseName, OptModel, mTEPES):
OutputToFile.reset_index().to_csv(_path+f'/oT_Result_NetworkEnergyHeatTotalPerArea_{CaseName}.csv', index=False, sep=',')

if len(mTEPES.ha):
OutputResults = pd.Series(data=[OptModel.vFlowHeat[p,sc,n,ni,nf,cc]()*(mTEPES.pLoadLevelDuration[p,sc,n]()*mTEPES.pPeriodProb[p,sc]())*(mTEPES.pLineLength[ni,nf,cc]()*1e-3) for p,sc,n,ni,nf,cc in mTEPES.psnha], index=pd.Index(mTEPES.psnha))
OutputResults = pd.Series(data=[OptModel.vFlowHeat[p,sc,n,ni,nf,cc]()*(mTEPES.pLoadLevelDuration[p,sc,n]()*mTEPES.pPeriodProb[p,sc]())*(mTEPES.pHeatPipeLength[ni,nf,cc]()*1e-3) for p,sc,n,ni,nf,cc in mTEPES.psnha], index=pd.Index(mTEPES.psnha))
OutputResults.index.names = ['Scenario', 'Period', 'LoadLevel', 'InitialNode', 'FinalNode', 'Circuit']
OutputResults = OutputResults.reset_index().groupby(['InitialNode', 'FinalNode', 'Circuit']).sum(numeric_only=True)[0]
OutputResults.to_frame(name='GWh-Mkm').rename_axis(['InitialNode', 'FinalNode', 'Circuit'], axis=0).reset_index().to_csv(_path+f'/oT_Result_NetworkEnergyHeatTransport_{CaseName}.csv', index=False, sep=',')
Expand All @@ -1117,7 +1117,7 @@ def NetworkHeatOperationResults(DirName, CaseName, OptModel, mTEPES):
sPSNND = [(p,sc,n,nd) for p,sc,n,nd in mTEPES.psnnd if sum(1 for ch in c2n[nd]) + sum(1 for hp in h2n[nd]) + sum(1 for nf,cc in lout[nd]) + sum(1 for ni,cc in lin[nd])]
OutputToFile = pd.Series(data=[OptModel.vHeatNS[p,sc,n,nd]() for p,sc,n,nd in sPSNND], index=pd.Index(sPSNND))
OutputToFile *= 1e3
OutputToFile.to_frame(name='MW').reset_index().pivot_table(index=['level_0','level_1','level_2'], columns='level_3', values='MW').rename_axis(['Period', 'Scenario', 'LoadLevel'], axis=0).rename_axis([None], axis=1).to_csv(_path+f'/oT_Result_NetworkHTNS_{CaseName}.csv', sep=',')
OutputToFile.to_frame(name='MW').reset_index().pivot_table(index=['level_0','level_1','level_2'], columns='level_3', values='MW').rename_axis(['Period', 'Scenario', 'LoadLevel'], axis=0).rename_axis([None], axis=1).to_csv(_path+f'/oT_Result_NetworkHeatNS_{CaseName}.csv', sep=',')

# plot heat network map
# Sub functions
Expand Down Expand Up @@ -1306,7 +1306,7 @@ def OperationSummaryResults(DirName, CaseName, OptModel, mTEPES):
NetInvCostVRESInsCap = 0.0
# Rate of return for VRE technologies
# warning division and multiplication
VRETechRevenue = sum(mTEPES.pDuals["".join([f"eBalanceElec_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vTotalOutput[p,sc,n,gc]() for p,sc,st,n,nd,gc in mTEPES.s2n*mTEPES.nd*mTEPES.gc if gc in g2n[nd] and gc in mTEPES.re and (p,gc) in mTEPES.pgc and (p,sc,n) in mTEPES.psn and sum(1 for g in g2n[nd]) + sum(1 for nf,cc in lout[nd]) + sum(1 for ni,cc in lin[nd]))
VRETechRevenue = sum(mTEPES.pDuals["".join([f"eBalanceElec_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vTotalOutput[p,sc,n,gc]() for p,sc,st,n,nd,gc in mTEPES.s2n*mTEPES.nd*mTEPES.gc if gc in g2n[nd] and gc in mTEPES.re and (p,gc) in mTEPES.pgc and (p,sc,n) in mTEPES.psn and sum(1 for g in g2n[nd]) + sum(1 for nf,cc in lout[nd]) + sum(1 for ni,cc in lin[nd]))
VREInvCostCapacity = sum(mTEPES.pGenInvestCost[gc]*OptModel.vGenerationInvest[p,gc]() for p,gc in mTEPES.pgc if gc in mTEPES.re)

K1 = pd.Series(data={'Ratio Fossil Fuel Generation/Total Generation [%]' : FossilFuelGeneration / TotalGeneration *1e2}).to_frame(name='Value')
Expand Down Expand Up @@ -1624,7 +1624,7 @@ def MarginalResults(DirName, CaseName, OptModel, mTEPES, pIndPlotOutput):

#%% outputting the LSRMC
sPSSTNND = [(p,sc,st,n,nd) for p,sc,st,n,nd in mTEPES.s2n*mTEPES.nd if sum(1 for g in g2n[nd]) + sum(1 for nf,cc in lout[nd]) + sum(1 for ni,cc in lin[nd]) and (p,sc,n) in mTEPES.psn]
OutputResults = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() for p,sc,st,n,nd in sPSSTNND], index=pd.Index(sPSSTNND))
OutputResults = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() for p,sc,st,n,nd in sPSSTNND], index=pd.Index(sPSSTNND))
OutputResults *= 1e3
OutputResults.to_frame(name='LSRMC').reset_index().pivot_table(index=['level_0','level_1','level_3'], columns='level_4', values='LSRMC').rename_axis(['Period', 'Scenario', 'LoadLevel'], axis=0).rename_axis([None], axis=1).to_csv(_path+f'/oT_Result_NetworkSRMC_{CaseName}.csv', sep=',')

Expand All @@ -1646,7 +1646,7 @@ def MarginalResults(DirName, CaseName, OptModel, mTEPES, pIndPlotOutput):

#%% outputting the LSRMC of H2
sPSSTNND = [(p,sc,st,n,nd) for p,sc,st,n,nd in mTEPES.s2n*mTEPES.nd if sum(1 for el in e2n[nd]) + sum(1 for nf,cc in lout[nd]) + sum(1 for ni,cc in lin[nd]) and (p,sc,n) in mTEPES.psn]
OutputResults = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceH2_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() for p,sc,st,n,nd in sPSSTNND], index=pd.Index(sPSSTNND))
OutputResults = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceH2_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() for p,sc,st,n,nd in sPSSTNND], index=pd.Index(sPSSTNND))
OutputResults *= 1e3
OutputResults.to_frame(name='LSRMCH2').reset_index().pivot_table(index=['level_0','level_1','level_3'], columns='level_4', values='LSRMCH2').rename_axis(['Period', 'Scenario', 'LoadLevel'], axis=0).rename_axis([None], axis=1).to_csv(_path+f'/oT_Result_NetworkSRMCH2_{CaseName}.csv', sep=',')

Expand All @@ -1667,7 +1667,7 @@ def MarginalResults(DirName, CaseName, OptModel, mTEPES, pIndPlotOutput):

#%% outputting the LSRMC of heat
sPSSTNND = [(p,sc,st,n,nd) for p,sc,st,n,nd in mTEPES.s2n*mTEPES.nd if sum(1 for ch in c2n[nd]) + sum(1 for hp in h2n[nd]) + sum(1 for nf,cc in lout[nd]) + sum(1 for ni,cc in lin[nd]) and (p,sc,n) in mTEPES.psn]
OutputResults = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceHeat_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() for p,sc,st,n,nd in sPSSTNND], index=pd.Index(sPSSTNND))
OutputResults = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceHeat_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() for p,sc,st,n,nd in sPSSTNND], index=pd.Index(sPSSTNND))
OutputResults *= 1e3
OutputResults.to_frame(name='LSRMCHeat').reset_index().pivot_table(index=['level_0','level_1','level_3'], columns='level_4', values='LSRMCHeat').rename_axis(['Period', 'Scenario', 'LoadLevel'], axis=0).rename_axis([None], axis=1).to_csv(_path+f'/oT_Result_NetworkSRMCHeat_{CaseName}.csv', sep=',')

Expand Down Expand Up @@ -2045,31 +2045,31 @@ def Transformation1(df, _name):
OutputResults.to_csv(_path+f'/oT_Result_CostSummary_{ar}_{CaseName}.csv', sep=',', index=False)

sPSSTNNDG = [(p,sc,st,n,nd,g) for p,sc,st,n,nd,g in mTEPES.s2n*mTEPES.n2g if (p,g) in mTEPES.pg and (p,sc,n) in mTEPES.psn]
OutputResults = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vTotalOutput [p,sc,n,g]() for p,sc,st,n,nd,g in sPSSTNNDG], index=pd.Index(sPSSTNNDG))
OutputResults = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vTotalOutput [p,sc,n,g]() for p,sc,st,n,nd,g in sPSSTNNDG], index=pd.Index(sPSSTNNDG))
OutputResults.to_frame(name='MEUR').reset_index().pivot_table(index=['level_0','level_1','level_3'], columns='level_5', values='MEUR').rename_axis(['Period', 'Scenario', 'LoadLevel'], axis=0).rename_axis([None], axis=1).to_csv(_path+f'/oT_Result_RevenueEnergyGeneration_{CaseName}.csv', sep=',')

if len(mTEPES.eh):
sPSSTNNDES = [(p,sc,st,n,nd,eh) for p,sc,st,n,nd,eh in mTEPES.s2n*mTEPES.n2g if eh in mTEPES.eh if (p,eh) in mTEPES.peh and (p,sc,n) in mTEPES.psn]
OutputResults = -pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vESSTotalCharge[p,sc,n,eh]() for p,sc,st,n,nd,eh in sPSSTNNDES], index=pd.Index(sPSSTNNDES))
OutputResults = -pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vESSTotalCharge[p,sc,n,eh]() for p,sc,st,n,nd,eh in sPSSTNNDES], index=pd.Index(sPSSTNNDES))
OutputResults.to_frame(name='MEUR').reset_index().pivot_table(index=['level_0','level_1','level_3'], columns='level_5', values='MEUR').rename_axis(['Period', 'Scenario', 'LoadLevel'], axis=0).rename_axis([None], axis=1).to_csv(_path+f'/oT_Result_RevenueEnergyConsumption_{CaseName}.csv', sep=',')

if len(mTEPES.gc):
GenRev = []
ChargeRev = []
sPSSTNNDGC1 = [(p,sc,st,n,nd,gc) for p,sc,st,n,nd,gc in mTEPES.s2n*mTEPES.n2g if gc in mTEPES.gc if (p,gc) in mTEPES.pgc and (p,sc,n) in mTEPES.psn]
OutputToGenRev = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vTotalOutput [p,sc,n,gc]() for p,sc,st,n,nd,gc in sPSSTNNDGC1], index=pd.Index(sPSSTNNDGC1))
OutputToGenRev = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vTotalOutput [p,sc,n,gc]() for p,sc,st,n,nd,gc in sPSSTNNDGC1], index=pd.Index(sPSSTNNDGC1))
GenRev.append(OutputToGenRev)
if len([(p,sc,n,nd,gc) for p,sc,n,nd,gc in mTEPES.psn*mTEPES.n2g if gc in mTEPES.gc for ot in mTEPES.ot if (p,gc) in mTEPES.pgc and gc in o2e[ot]]):
sPSSTNNDGC2 = [(p,sc,st,n,nd,gc) for p,sc,st,n,nd,gc in sPSSTNNDGC1 for ot in mTEPES.ot if (p,gc) in mTEPES.pgc and gc in o2e[ot] and (p,sc,n) in mTEPES.psn]
OutputChargeRevESS = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vESSTotalCharge[p,sc,n,gc]() for p,sc,st,n,nd,gc in sPSSTNNDGC2], index=pd.Index(sPSSTNNDGC2))
OutputChargeRevESS = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]()*OptModel.vESSTotalCharge[p,sc,n,gc]() for p,sc,st,n,nd,gc in sPSSTNNDGC2], index=pd.Index(sPSSTNNDGC2))
ChargeRev.append(OutputChargeRevESS)
if len([(p,sc,n,nd,gc) for p,sc,n,nd,gc in mTEPES.psn*mTEPES.n2g if gc in mTEPES.gc for rt in mTEPES.rt if (p,gc) in mTEPES.pgc and gc in r2r[rt]]):
sPSSTNNDGC3 = [(p,sc,st,n,nd,gc) for p,sc,st,n,nd,gc in sPSSTNNDGC1 for rt in mTEPES.rt if (p,gc) in mTEPES.pgc and gc in r2r[rt] and (p,sc,n) in mTEPES.psn]
OutputChargeRevRES = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() * 0.0 for p,sc,st,n,nd,gc in sPSSTNNDGC3], index=pd.Index(sPSSTNNDGC3))
OutputChargeRevRES = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() * 0.0 for p,sc,st,n,nd,gc in sPSSTNNDGC3], index=pd.Index(sPSSTNNDGC3))
ChargeRev.append(OutputChargeRevRES)
if len([(p,sc,n,nd,gc) for p,sc,n,nd,gc in mTEPES.psn*mTEPES.n2g if gc in mTEPES.gc for ot in mTEPES.ot if (p,gc) in mTEPES.pgc and gc in o2e[ot]]):
sPSSTNNDGC4 = [(p,sc,st,n,nd,gc) for p,sc,st,n,nd,gc in sPSSTNNDGC1 for ot in mTEPES.ot if (p,gc) in mTEPES.pgc and gc in o2e[ot] and (p,sc,n) in mTEPES.psn]
OutputChargeRevThr = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{p}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() * 0.0 for p,sc,st,n,nd,gc in sPSSTNNDGC4], index=pd.Index(sPSSTNNDGC4))
OutputChargeRevThr = pd.Series(data=[mTEPES.pDuals["".join([f"eBalanceElec_{int(p)}_{sc}_{st}('{n}', '{nd}')"])]/mTEPES.pPeriodProb[p,sc]()/mTEPES.pLoadLevelDuration[p,sc,n]() * 0.0 for p,sc,st,n,nd,gc in sPSSTNNDGC4], index=pd.Index(sPSSTNNDGC4))
ChargeRev.append(OutputChargeRevThr)
GenRev = pd.concat(GenRev)
ChargeRev = -pd.concat(ChargeRev)
Expand Down

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