Updated precompiled external libraries, documentation, post-processing

Dispa-SET/DispaSET/PostProcessing.py

@@ -84,9 +84,41 @@ def UnitFuel(Inputs,shrink=True):
         fuels[uu[i]] = f[j[0][0]]
     return fuels
 
+def GetLoadData(datain,c):
+    ''' 
+    Get the load curve, the residual load curve, and the net residual load curve of a specific country
+
+    :param datain:  Dispa-SET inputs formatted into a list of dataframes (output of the ds_to_df function)
+    :param c:       Country to consider (e.g. 'BE')
+    :return out:    Dataframe with the following columns:
+                        Load:               Load curve of the specified country
+                        ResidualLoad:       Load minus the production of variable renewable sources
+                        NetResidualLoad:    Residual netted from the interconnections with neightbouring countries
+    '''
+    out = pd.DataFrame(index = datain['Demand'].index)
+    out['Load'] = datain['Demand']['DA',c]
+    # Listing power plants with non-dispatchable power generation:
+    VREunits = []   
+    VRE = np.zeros(len(out))
+    for t in ['WTON','WTOF','PHOT','HROR']:
+        for u in datain['Technology']:
+            if datain['Technology'].loc[t,u]:
+                VREunits.append(u)
+                VRE = VRE + datain['AvailabilityFactor'][u].values * datain['PowerCapacity'].loc[u,'PowerCapacity']
+    Interconnections = np.zeros(len(out))
+    for l in datain['FlowMinimum']:
+        if l[:2] == c:
+            Interconnections = Interconnections - datain['FlowMinimum'][l].values        
+        elif l[-2:] == c:
+            Interconnections = Interconnections + datain['FlowMinimum'][l].values     
+    out['ResidualLoad'] = out['Load'] - VRE
+    out['NetResidualLoad'] = out['ResidualLoad'] - Interconnections
+    return out
+
+
 def GetDemand(Inputs,c):
     ''' 
-    Get the demand curve of a specific country
+    Get the load curve and the residual load curve of a specific country
 
     :param Inputs:  Dispa-SET inputs
     :param c:       Country to consider (e.g. 'BE')
@@ -105,7 +137,6 @@ def GetDemand(Inputs,c):
         sys.exit('Inputs variable no valid')    
     return pd.Series(data,index=index,name=c)
 
-
 
 def AggregateByFuel(PowerOutput,Inputs,SpecifyFuels=None):
     '''
@@ -441,7 +472,7 @@ def GetResults(path='.',cache = False, TempPath='.pickle'):
     index_long = pd.DatetimeIndex(start=pd.datetime(*StartDate),end=StopDate_long,freq='h')
 
     # Setting the proper index to the result dataframes:
-    for key in ['OutputPower','OutputSystemCost','OutputCommitted','OutputCurtailedPower','OutputFlow', 'OutputShedLoad','OutputSpillage','OutputStorageLevel','OutputStorageInput','LostLoad_Reserve2U', 'LostLoad_MaxPower','LostLoad_MinPower','LostLoad_RampUp','LostLoad_RampDown','LostLoad_Reserve2D']:
+    for key in ['OutputPower','OutputSystemCost','OutputCommitted','OutputCurtailedPower','OutputFlow', 'OutputShedLoad','OutputSpillage','OutputStorageLevel','OutputStorageInput','LostLoad_Reserve2U', 'LostLoad_MaxPower','LostLoad_MinPower','LostLoad_RampUp','LostLoad_RampDown','LostLoad_Reserve2D','ShadowPrice']:
         if key in results:
             if len(results[key]) == len(index_long):                # Case of variables for which the look-ahead period recorded (e.g. the lost loads)
                 results[key].index = index_long
@@ -457,6 +488,9 @@ def GetResults(path='.',cache = False, TempPath='.pickle'):
 
     #Clean power plant names:
     results['OutputPower'].columns = clean_strings(results['OutputPower'].columns.tolist())    
+    # Remove epsilons:
+    if 'ShadowPrice' in results:
+        results['ShadowPrice'][results['ShadowPrice'] == 5e300] = 0
 
     for key in results['OutputPower']:
         if key not in inputs['units'].index:

Dispa-SET/DispaSET/__init__.py

@@ -1,27 +1,36 @@
 import sys
 import platform
 
-# Try to import the GAMS api to write gdx files:
+
+# Try to import the GAMS api and gdxcc to write gdx files:
 try:
+    import gams
     import gdxcc
+    gams_ok = True
     gdxcc_ok = True
 except:
     if sys.platform == 'linux2' and platform.architecture()[0] == '64bit':
-        sys.path.append('../Externals/gdxcc/linux64/')
+        sys.path.append('../Externals/gams_api/linux64/')
     elif sys.platform == 'linux2' and platform.architecture()[0] == '32bit':
         sys.path.append('../Externals/gdxcc/linux32/')
     elif sys.platform == 'win32' and platform.architecture()[0] == '64bit':
-        sys.path.append('../Externals/gdxcc/win64/')
+        sys.path.append('../Externals/gams_api/win64/')
     elif sys.platform == 'win32' and platform.architecture()[0] == '32bit':
         sys.path.append('../Externals/gdxcc/win32/')
     elif sys.platform == 'darwin':
         sys.path.append('../Externals/gdxcc/osx64/')        
+    try:
+        import gams
+        gams_ok = True
+    except:
+        gams_ok = False     
     try:
         import gdxcc
         gdxcc_ok = True
     except:
         gdxcc_ok = False
-
+
+
 #from .DispaCheck import *
 from .DispaSET_io_data import *
 #from .DispaSolve import *      # importing dispasolve causes errors if pyomo is not installed

Dispa-SET/DispaSet.py

@@ -58,6 +58,9 @@
     SimData = ds.BuildSimulation(config)
 
 if simulate:
-    from DispaSET.DispaSolveGAMS import SolveMILP
-    r = SolveMILP(config['SimulationDirectory'],config['GAMS_folder'])
+    if ds.gams_ok:
+        from DispaSET.DispaSolveGAMS import SolveMILP
+        r = SolveMILP(config['SimulationDirectory'],config['GAMS_folder'])
+    else:
+        print('The gams library is required to run the GAMS versions of Dispa-SET. Please install if from the /apifiles/Python/api/ folder in the GAMS directory')
 

Dispa-SET/GAMS-files/UCM_h.gms

@@ -1,7 +1,7 @@
 $Title UCM model
 
 $eolcom //
-Option LimRow=0;
+Option LimRow=1000;
 Option IterLim=1000000000;
 Option ResLim = 10000000000;
 *Option SolPrint=On;
@@ -70,32 +70,32 @@ PARAMETERS
 AvailabilityFactor(u,h)          [%]      Availability factor
 CommittedInitial(u)              [n.a.]   Initial committment status
 Config
-*CostCurtailment(n,h)             [�\MW]  Curtailment costs
-CostFixed(u)                     [�\h]    Fixed costs
-CostRampUp(u)                    [�\MW\h] Ramp-up costs
-CostRampDown(u)                  [�\MW\h] Ramp-down costs
-CostShutDown(u)                  [�]      Shut-down costs
-CostStartUp(u)                   [�]      Start-up costs
-CostVariable(u,h)                [�\MW]   Variable costs
+*CostCurtailment(n,h)             [�\MW]  Curtailment costs
+CostFixed(u)                     [�\h]    Fixed costs
+CostRampUp(u)                    [�\MW\h] Ramp-up costs
+CostRampDown(u)                  [�\MW\h] Ramp-down costs
+CostShutDown(u)                  [�]      Shut-down costs
+CostStartUp(u)                   [�]      Start-up costs
+CostVariable(u,h)                [�\MW]   Variable costs
 Curtailment(n)                   [n.a]    Curtailment allowed or not {1 0} at node n
 Demand(mk,n,h)                   [MW]     Demand
 Efficiency(u)                    [%]      Efficiency
 EmissionMaximum(n,p)             [tP]     Emission limit
 EmissionRate(u,p)                [tP\MWh] P emission rate
 FlowMaximum(l,h)                 [MW]     Line limits
 FlowMinimum(l,h)                 [MW]     Minimum flow
-FuelPrice(n,f,h)                 [�\F]    Fuel price
+FuelPrice(n,f,h)                 [�\F]    Fuel price
 Fuel(u,f)                        [n.a.]   Fuel type {1 0}
 LineNode(l,n)                    [n.a.]   Incidence matrix {-1 +1}
 LoadShedding(n)                  [n.a.]   Load shedding capacity
 Location(u,n)                    [n.a.]   Location {1 0}
-Markup(u,h)                      [�\MW]   Markup
+Markup(u,h)                      [�\MW]   Markup
 OutageFactor(u,h)                [%]      Outage Factor (100% = full outage)
 PartLoadMin(u)                   [%]      Minimum part load
 PowerCapacity(u)                 [MW]     Installed capacity
 PowerInitial(u)                  [MW]     Power output before initial period
 PowerMinStable(u)                [MW]     Minimum power output
-PriceTransmission(l,h)           [�\MWh]  Transmission price
+PriceTransmission(l,h)           [�\MWh]  Transmission price
 StorageChargingCapacity(u)       [MW]     Storage capacity
 StorageChargingEfficiency(u)     [%]      Charging efficiency
 RampDownMaximum(u)               [MW\h]   Ramp down limit
@@ -127,7 +127,7 @@ $If %RetrieveStatus% == 1 CommittedCalc(u,z)               [n.a.]   Committment
 *Parameters as used within the loop
 PARAMETERS
 TimeUpLeft_JustStarted(u,h)      [h]     Required time up left at hour h if the Unit has just been started
-CostLoadShedding(n,h)            [�\MW]  Value of lost load
+CostLoadShedding(n,h)            [�\MW]  Value of lost load
 TimeUp(u,h)                      [h]     Hours up
 LoadMaximum(u,h)                 [%]     Maximum load given AF and OF
 PowerMustRun(u,h)                [MW]    Minimum power output
@@ -867,6 +867,7 @@ OutputSystemCost(h)
 OutputSpillage(s,h)
 OutputShedLoad(n,h)
 OutputCurtailedPower(n,h)
+ShadowPrice(n,h)
 ;
 
 OutputCommitted(u,z)=Committed.L(u,z);
@@ -880,6 +881,7 @@ OutputSystemCost(z)=SystemCost.L(z);
 OutputSpillage(s,z)  = Spillage.L(s,z) ;
 OutputShedLoad(n,z) = ShedLoad.L(n,z);
 OutputCurtailedPower(n,z)=CurtailedPower.L(n,z);
+ShadowPrice(n,z) = EQ_Demand_balance_DA.m(n,z);
 
 EXECUTE_UNLOAD "Results.gdx"
 OutputCommitted,
@@ -898,7 +900,8 @@ LostLoad_MinPower,
 LostLoad_Reserve2D,
 LostLoad_Reserve2U,
 LostLoad_RampUp,
-LostLoad_RampDown
+LostLoad_RampDown,
+ShadowPrice
 ;
 
 $onorder

Dispa-SET/Read_results.py

@@ -81,3 +81,13 @@
 bar plots with max demand, installed capacity in each country
 line capacities wrong for some countries
 '''
+
+
+#%%
+
+load = ds.GetLoadData(datain,'IE')
+
+
+
+
+

Docs/index.rst

@@ -11,7 +11,7 @@ Welcome to Dispa-SET's documentation!
 :Version: |version|
 :Date: |today|
 
-The Dispa-SET model is a unit commitment and dispatch model developed within the “Joint Research Centre” and focused on the balancing and flexibility problems in European grids [1]_. It is written in GAMS an Python (Pyomo) and Excel for input/output data handling and visualization. The selected Mixed-Integer Linear Programming (MILP) solver is CPLEX.
+The Dispa-SET model is a unit commitment and dispatch model developed within the “Joint Research Centre” and focused on the balancing and flexibility problems in European grids [1]_. It is written in GAMS an Python (Pyomo) and uses csv files for input data handling. The optimisation is defined as a Linear Programming (LP) or Mixed-Integer Linear Programming (MILP) problem, depending on the desired level of accuracy and complexity. 
 
 
 

Docs/model.rst

@@ -1,9 +1,9 @@
-.. _model:
+.. _model:
 
 Model Description
 =================
 
-The model is expressed as a MILP problem. Continuous variables include the individual unit dispatched power, the shedded load and the curtailed power generation. The binary variables are the commitment status of each unit. The main model features can be summarized as follows:
+The model is expressed as a MILP or LP problem. Continuous variables include the individual unit dispatched power, the shedded load and the curtailed power generation. The binary variables are the commitment status of each unit. The main model features can be summarized as follows:
 
 
 Variables
@@ -65,7 +65,7 @@ Parameters
 	PowerInitial(u)				MW	Power output before initial period
 	PowerMinStable(u)			MW	Minimum power for stable generation
 	PowerMustRun(u)				MW	Minimum power output
-	PriceTransmission(l,h)			€/MWh	Price of transmission between zones
+	PriceTransmission(l,h)			€/MWh	Price of transmission between zones
 	RampDownMaximum(u)			MW/h	Ramp down limit
 	RampShutDownMaximum(u)			MW/h	Shut-down ramp limit
 	RampStartUpMaximum(u)			MW/h	Start-up ramp limit
@@ -687,6 +687,6 @@ Since the start-up of individual units is not considered anymore, it is not usef
 References
 ^^^^^^^^^^
 
-.. [1] Hidalgo González, I., Quoilin, S., & Zucker, A. (2014). Dispa-SET 2.0: unit commitment and power dispatch model (EUR 27015 EN). Petten, Netherlands: European Commission. 
+.. [1] Hidalgo González, I., Quoilin, S., & Zucker, A. (2014). Dispa-SET 2.0: unit commitment and power dispatch model (EUR 27015 EN). Petten, Netherlands: European Commission. 
 .. [2] Quoilin, S., Nijs, W., Hidalgo, I., & Thiel, C. (2015). Evaluation of simplified flexibility evaluation tools using a unit commitment model. IEEE Digital Library. 
 .. [3] Quoilin, S., Gonzalez Vazquez, I., Zucker, A., & Thiel, C. (2014). Available technical flexibility for balancing variable renewable energy sources: case study in Belgium. Proceedings of the 9th Conference on Sustainable Development of Energy, Water and Environment Systems. 

Externals/gams_api/linux64/GAMS-1.0.egg-info

@@ -0,0 +1,10 @@
+Metadata-Version: 1.0
+Name: GAMS
+Version: 1.0
+Summary: GAMS API
+Home-page: UNKNOWN
+Author: GAMS
+Author-email: UNKNOWN
+License: UNKNOWN
+Description: UNKNOWN
+Platform: UNKNOWN

Externals/gams_api/linux64/cfgmcc-1.egg-info

@@ -0,0 +1,10 @@
+Metadata-Version: 1.0
+Name: cfgmcc
+Version: 1
+Summary: UNKNOWN
+Home-page: UNKNOWN
+Author: UNKNOWN
+Author-email: UNKNOWN
+License: UNKNOWN
+Description: UNKNOWN
+Platform: UNKNOWN