avoid solving all the periods and scenarios simultaneously if not needed

Author: arght

CHANGELOG.rst

@@ -1,8 +1,9 @@
 Change Log
 =============
 
-[4.18.4] - 2025-03-21
+[4.18.4] - 2025-03-31
 -----------------------
+- [FIXED] avoid solving all the periods and scenarios simultaneously if not needed
 - [CHANGED] added GenerationConsumptionRatio output file
 - [FIXED] error in some terms of the operation costs
 - [FIXED] error in CostRecovery output file

README.rst

@@ -120,7 +120,7 @@ Miniconda (recommended)
 2. **Packages and Solver**:
 
    1. Launch a new Anaconda prompt (or terminal in any IDE)
-   2. The `HiGHS 1.7.2 <https://ergo-code.github.io/HiGHS/dev/interfaces/python/#python-getting-started>`_ is our recommendation if you want a free and open-source solver.
+   2. The `HiGHS <https://ergo-code.github.io/HiGHS/dev/interfaces/python/#python-getting-started>`_ is our recommendation if you want a free and open-source solver.
    3. Install openTEPES via pip by ``pip install openTEPES``
 
 Continue at `Get Started <#get-started>`_ and see the `Tips <#tips>`_.

openTEPES/openTEPES.py

@@ -548,7 +548,7 @@ def ProcessParameter(pDataFrame: pd.DataFrame, pTimeStep: int) -> pd.DataFrame:
 
     #%% defining subsets: active load levels (n,n2), thermal units (t), RES units (r), ESS units (es), candidate gen units (gc), candidate ESS units (ec), all the electric lines (la), candidate electric lines (lc), candidate DC electric lines (cd), existing DC electric lines (cd), electric lines with losses (ll), reference node (rf), and reactive generating units (gq)
     mTEPES.p      = Set(doc='periods'                          , initialize=[pp     for pp   in mTEPES.pp  if pPeriodWeight       [pp] >  0.0 and sum(pDuration[pp,sc,n] for sc,n in mTEPES.scc*mTEPES.nn)])
-    mTEPES.sc     = Set(doc='scenarios'                        , initialize=[scc    for scc  in mTEPES.scc                                    ])
+    mTEPES.sc     = Set(doc='scenarios'                        , initialize=[scc    for scc  in mTEPES.scc                                   ])
     mTEPES.ps     = Set(doc='periods/scenarios'                , initialize=[(p,sc) for p,sc in mTEPES.p*mTEPES.sc if pScenProb [p,sc] >  0.0 and sum(pDuration[p,sc,n ] for    n in            mTEPES.nn)])
     mTEPES.st     = Set(doc='stages'                           , initialize=[stt    for stt  in mTEPES.stt if pStageWeight       [stt] >  0.0])
     mTEPES.n      = Set(doc='load levels'                      , initialize=[nn     for nn   in mTEPES.nn  if sum(pDuration  [p,sc,nn] for p,sc in mTEPES.ps) > 0])
@@ -1327,18 +1327,18 @@ def filter_rows(df, set):
     mTEPES.pIndHydrogen          = Param(initialize=pIndHydrogen        , within=Binary,              doc='Indicator of hydrogen demand and pipeline network'                      )
     mTEPES.pIndHeat              = Param(initialize=pIndHeat            , within=Binary,              doc='Indicator of heat     demand and pipe     network'                      )
 
-    mTEPES.pENSCost              = Param(initialize=pENSCost            , within=NonNegativeReals,    doc='ENS cost'                                          )
-    mTEPES.pH2NSCost             = Param(initialize=pH2NSCost           , within=NonNegativeReals,    doc='HNS cost'                                          )
-    mTEPES.pHeatNSCost           = Param(initialize=pHeatNSCost         , within=NonNegativeReals,    doc='HTNS cost'                                         )
-    mTEPES.pCO2Cost              = Param(initialize=pCO2Cost            , within=NonNegativeReals,    doc='CO2 emission cost'                                 )
-    mTEPES.pAnnualDiscRate       = Param(initialize=pAnnualDiscRate     , within=UnitInterval,        doc='Annual discount rate'                              )
-    mTEPES.pUpReserveActivation  = Param(initialize=pUpReserveActivation, within=UnitInterval,        doc='Proportion of upward   reserve activation'         )
-    mTEPES.pDwReserveActivation  = Param(initialize=pDwReserveActivation, within=UnitInterval,        doc='Proportion of downward reserve activation'         )
-    mTEPES.pMinRatioDwUp         = Param(initialize=pMinRatioDwUp       , within=UnitInterval,        doc='Minimum ration between upward and downward reserve')
-    mTEPES.pMaxRatioDwUp         = Param(initialize=pMaxRatioDwUp       , within=UnitInterval,        doc='Maximum ration between upward and downward reserve')
-    mTEPES.pSBase                = Param(initialize=pSBase              , within=PositiveReals,       doc='Base power'                                        )
-    mTEPES.pTimeStep             = Param(initialize=pTimeStep           , within=PositiveIntegers,    doc='Unitary time step'                                 )
-    mTEPES.pEconomicBaseYear     = Param(initialize=pEconomicBaseYear   , within=PositiveIntegers,    doc='Base year'                                         )
+    mTEPES.pENSCost              = Param(initialize=pENSCost            , within=NonNegativeReals,    doc='ENS cost'                                           )
+    mTEPES.pH2NSCost             = Param(initialize=pH2NSCost           , within=NonNegativeReals,    doc='HNS cost'                                           )
+    mTEPES.pHeatNSCost           = Param(initialize=pHeatNSCost         , within=NonNegativeReals,    doc='HTNS cost'                                          )
+    mTEPES.pCO2Cost              = Param(initialize=pCO2Cost            , within=NonNegativeReals,    doc='CO2 emission cost'                                  )
+    mTEPES.pAnnualDiscRate       = Param(initialize=pAnnualDiscRate     , within=UnitInterval,        doc='Annual discount rate'                               )
+    mTEPES.pUpReserveActivation  = Param(initialize=pUpReserveActivation, within=UnitInterval,        doc='Proportion of upward   reserve activation'          )
+    mTEPES.pDwReserveActivation  = Param(initialize=pDwReserveActivation, within=UnitInterval,        doc='Proportion of downward reserve activation'          )
+    mTEPES.pMinRatioDwUp         = Param(initialize=pMinRatioDwUp       , within=UnitInterval,        doc='Minimum ratio downward to upward operating reserves')
+    mTEPES.pMaxRatioDwUp         = Param(initialize=pMaxRatioDwUp       , within=UnitInterval,        doc='Maximum ratio downward to upward operating reserves')
+    mTEPES.pSBase                = Param(initialize=pSBase              , within=PositiveReals,       doc='Base power'                                         )
+    mTEPES.pTimeStep             = Param(initialize=pTimeStep           , within=PositiveIntegers,    doc='Unitary time step'                                  )
+    mTEPES.pEconomicBaseYear     = Param(initialize=pEconomicBaseYear   , within=PositiveIntegers,    doc='Base year'                                          )
 
     mTEPES.pReserveMargin        = Param(mTEPES.par,   initialize=pReserveMargin.to_dict()            , within=NonNegativeReals,    doc='Adequacy reserve margin'                             )
     mTEPES.pEmission             = Param(mTEPES.par,   initialize=pEmission.to_dict()                 , within=NonNegativeReals,    doc='Maximum CO2 emission'                                )

openTEPES/openTEPES_InputData.py

@@ -519,7 +519,7 @@
 # conveyed by you (or copies made from those copies), or (b) primarily
 # for and in connection with specific products or compilations that
 # contain the covered work, unless you entered into that arrangement,
-# or that patent license was granted, prior to 28 March 2107.
+# or that patent license was granted, prior to 28 March 2007.
 #
 #   Nothing in this License shall be construed as excluding or limiting
 # any implied license or other defenses to infringement that may
@@ -660,7 +660,7 @@
 # For more information on this, and how to apply and follow the GNU AGPL, see
 # <https://www.gnu.org/licenses/>.
 
-# Open Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES) - March 21, 2025
+# Open Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES) - March 31, 2025
 # simplicity and transparency in power systems planning
 
 # Developed by
@@ -686,7 +686,7 @@
 # import pkg_resources
 from .openTEPES import openTEPES_run
 
-print('\033[1;32mOpen Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES) - Version 4.18.4 - March 21, 2025\033[0m')
+print('\033[1;32mOpen Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES) - Version 4.18.4 - March 31, 2025\033[0m')
 print('\033[34m#### Academic research license - for non-commercial use only ####\033[0m \n')
 
 parser = argparse.ArgumentParser(description='Introducing main parameters...')

openTEPES/openTEPES_Main.py

@@ -1,5 +1,5 @@
 """
-Open Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES) - March 21, 2025
+Open Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES) - March 31, 2025
 """
 
 import time
@@ -2195,10 +2195,12 @@ def Transformation1(df, _name):
         GenCost = pd.Series(data=[sum(mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelDuration[p,sc,n]() * mTEPES.pLinearVarCost  [p,sc,n,gc] * OptModel.vTotalOutput   [p,sc,n,gc]() +
                                       mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelDuration[p,sc,n]() * mTEPES.pConstantVarCost[p,sc,n,gc] * OptModel.vCommitment    [p,sc,n,gc]() +
                                       mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelWeight  [p,sc,n]() * mTEPES.pStartUpCost    [       gc] * OptModel.vStartUp       [p,sc,n,gc]() +
-                                      mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelWeight  [p,sc,n]() * mTEPES.pShutDownCost   [       gc] * OptModel.vShutDown      [p,sc,n,gc]() for p,sc,n in mTEPES.psn if (p,gc) in mTEPES.pgc) for gc in mTEPES.gc], index=mTEPES.gc, dtype='float64')
+                                      mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelWeight  [p,sc,n]() * mTEPES.pShutDownCost   [       gc] * OptModel.vShutDown      [p,sc,n,gc]() for p,sc,n in mTEPES.psn if (p,gc) in mTEPES.pgc) if  gc in mTEPES.nr else
+                                  sum(mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelDuration[p,sc,n]() * mTEPES.pLinearVarCost  [p,sc,n,gc] * OptModel.vTotalOutput   [p,sc,n,gc]() for p,sc,n in mTEPES.psn if (p,gc) in mTEPES.pgc) for gc in mTEPES.gc], index=mTEPES.gc, dtype='float64')
         EmsCost = pd.Series(data=[sum(mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelDuration[p,sc,n]() * mTEPES.pEmissionVarCost[p,sc,n,gc] * OptModel.vTotalOutput   [p,sc,n,gc]() for p,sc,n in mTEPES.psn if (p,gc) in mTEPES.pgc) for gc in mTEPES.gc], index=mTEPES.gc, dtype='float64')
         OpGCost = pd.Series(data=[sum(mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelWeight  [p,sc,n]() * mTEPES.pOperReserveCost[       gc] * OptModel.vReserveUp     [p,sc,n,gc]() +
-                                      mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelWeight  [p,sc,n]() * mTEPES.pOperReserveCost[       gc] * OptModel.vReserveDown   [p,sc,n,gc]() for p,sc,n in mTEPES.psn if (p,gc) in mTEPES.pgc) for gc in mTEPES.gc], index=mTEPES.gc, dtype='float64')
+                                      mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelWeight  [p,sc,n]() * mTEPES.pOperReserveCost[       gc] * OptModel.vReserveDown   [p,sc,n,gc]() for p,sc,n in mTEPES.psn if (p,gc) in mTEPES.pgc) if  gc in mTEPES.nr else
+                                  0.0                                                                                                                                                                                                                             for gc in mTEPES.gc], index=mTEPES.gc, dtype='float64')
         CnsCost = pd.Series(data=[sum(mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelDuration[p,sc,n]() * mTEPES.pLinearVarCost  [p,sc,n,ec] * OptModel.vESSTotalCharge[p,sc,n,ec]() for p,sc,n in mTEPES.psn if (p,ec) in mTEPES.pec) for ec in mTEPES.ec], index=mTEPES.ec, dtype='float64')
         OpCCost = pd.Series(data=[sum(mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelWeight  [p,sc,n]() * mTEPES.pOperReserveCost[       ec] * OptModel.vESSReserveUp  [p,sc,n,ec]() +
                                       mTEPES.pDiscountedWeight[p] * mTEPES.pScenProb[p,sc]() * mTEPES.pLoadLevelWeight  [p,sc,n]() * mTEPES.pOperReserveCost[       ec] * OptModel.vESSReserveDown[p,sc,n,ec]() for p,sc,n in mTEPES.psn if (p,ec) in mTEPES.pec) for ec in mTEPES.ec], index=mTEPES.ec, dtype='float64')