def evaluateModel(modelInputs, scenarioInputs, scenarioName, temoa_path, project_path, solver):
# Unique filename
model_filename = scenarioName
# Build Model
tt.build(modelInputs, scenarioInputs, scenarioName, model_filename, path=project_path)
# Run Model
tt.run(model_filename, saveEXCEL=False, temoa_path=temoa_path, debug=True, solver=solver)
def evaluateModel(modelInputs, scenarioInputs, scenarioName, paths):
# Unique filename
model_filename = scenarioName
# Build Model
tt.build(modelInputs, scenarioInputs, scenarioName, model_filename, path='data')
# Run Model
saveEXCEL=True
tt.run(model_filename, paths, saveEXCEL=True, data_path='data', debug=False)
def evaluateMonteCarlo(modelInputs, scenarioXLSX, scenarioName, temoa_path, project_path, solver, cases, caseNum):
# Unique filename
model_filename = scenarioName + '_MC_' + str(caseNum)
# Prepare monte carlo inputs
cols = ['type', 'variable', 'tech', caseNum]
MCinputs = cases.loc[:, cols]
MCinputs = MCinputs.rename(columns={caseNum: 'value'})
# Build Model
tt.build(modelInputs, scenarioXLSX, scenarioName, model_filename, MCinputs=MCinputs, path=project_path,
mc_type='values')
# Run Model
error = tt.run(model_filename, saveEXCEL=False, temoa_path=temoa_path, debug=True, solver=solver)
# Analyze Model (w/ default monte carlo analysis function)
if not error:
folder = os.path.join(project_path, 'databases')
db = model_filename + '.sqlite'
results = tt.analyze_db(folder, db, scenario=scenarioName, iteration=caseNum, switch='tech', tod_analysis=True,
debug=False)
else:
results = pd.Dataframe()
return results
def evaluateModelSensitivity(modelInputs, scenarioXLSX, scenarioName, temoa_paths, cases, caseNum):
# Unique filename
model_filename = scenarioName + '_Sens_' + str(caseNum)
# Prepare sensitivity
sensitivity = cases.loc[caseNum]
# Build Model
tt.build(modelInputs, scenarioXLSX, scenarioName, model_filename, sensitivity=sensitivity, path='data')
# Run Model
saveEXCEL = False
tt.run(model_filename, temoa_paths, saveEXCEL=saveEXCEL)
# Analyze Results
folder = os.getcwd() + '\\databases'
db = model_filename + '.sqlite'
print("db: " + db)
yearlyCosts, LCOE = tt.getCosts(folder, db)
yearlyEmissions, avgEmissions = tt.getEmissions(folder, db)
# Move results to series
col = yearlyCosts.columns[0]
yearlyCosts = yearlyCosts[col]
LCOE = LCOE[col]
col = yearlyEmissions.columns[0]
yearlyEmissions = yearlyEmissions[col]
avgEmissions = avgEmissions[col]
# Package Outputs
output = sensitivity.copy() # Inputs
output['db'] = db
output['caseNum'] = caseNum
output['LCOE'] = LCOE
output['avgEmissions'] = avgEmissions
for ind in yearlyCosts.index:
label = 'cost_' + str(ind)
output[label] = yearlyCosts[ind]
for ind in yearlyEmissions.index:
label = 'emis_' + str(ind)
output[label] = yearlyEmissions[ind]
return output
def evaluateModelSensitivity(modelInputs, scenarioXLSX, scenarioName,
temoa_path, project_path, solver, cases, caseNum):
# Unique filename
model_filename = scenarioName + '_Sens_' + str(caseNum)
# Prepare sensitivity
sensitivity = cases.loc[caseNum]
# Build Model
tt.build(modelInputs,
scenarioXLSX,
scenarioName,
model_filename,
sensitivity=sensitivity,
path=project_path)
# Run Model
error = tt.run(model_filename,
temoa_path=temoa_path,
saveEXCEL=False,
solver=solver)
# Analyze Results
folder = os.path.join(project_path, 'databases')
db = model_filename + '.sqlite'
if not error:
yearlyCosts, LCOE = tt.getCosts(folder, db)
yearlyEmissions, avgEmissions = tt.getEmissions(folder, db)
# Package Outputs
output = pd.Series()
output['type'] = cases.loc[caseNum, 'type']
output['tech'] = cases.loc[caseNum, 'tech']
output['variable'] = cases.loc[caseNum, 'variable']
output['multiplier'] = cases.loc[caseNum, 'multiplier']
output['db'] = db
output['caseNum'] = caseNum
if not error:
output['LCOE'] = LCOE.loc[0, 'LCOE']
output['avgEmissions'] = avgEmissions.loc[0, 'avgEmissions']
else:
output['LCOE'] = np.nan
output['avgEmissions'] = np.nan
return output
def evaluateModel(modelInputs, scenarioInputs, scenarioName, combined_name, temoa_path, project_path, solver):
# Unique filename
model_filename = scenarioName + '_' + combined_name
# Build Model
tt.build(modelInputs, scenarioInputs, scenarioName, model_filename, path=project_path)
# Run Model
error = tt.run(model_filename, saveEXCEL=False, temoa_path=temoa_path, debug=True, solver=solver)
# Analyze Model (w/ default monte carlo analysis function)
if not error:
folder = os.path.join(project_path, 'databases')
db = model_filename + '.sqlite'
results = tt.analyze_db(folder, db, scenario=scenarioName, iteration='baseline', switch='tech',
tod_analysis=True, debug=False)
# Save results to a csv
os.chdir(os.path.join(project_path, 'results'))
df = pd.DataFrame()
df = df.append(results, ignore_index=True)
df.to_csv('BaselineResults_' + model_filename + '.csv')
os.chdir(project_path)
def evaluateMonteCarlo(modelInputs, scenarioXLSX, scenarioName, temoa_path,
project_path, solver, cases, caseNum):
# Unique filename
model_filename = scenarioName + '_MC_' + str(caseNum)
# Prepare monte carlo inputs
cols = ['type', 'variable', 'tech', caseNum]
MCinputs = cases.loc[:, cols]
MCinputs = MCinputs.rename(columns={caseNum: 'value'})
# Build Model
tt.build(modelInputs,
scenarioXLSX,
scenarioName,
model_filename,
MCinputs=MCinputs,
path=project_path,
mc_type='values')
# Run Model
error = tt.run(model_filename,
saveEXCEL=False,
temoa_path=temoa_path,
debug=True,
solver=solver)
# series to store results
folder = os.path.join(project_path, 'databases')
db = model_filename + '.sqlite'
output = pd.Series()
output['db'] = db
output['caseNum'] = caseNum
# Analyze Results
if not error:
yearlyCosts, LCOE = tt.getCosts(folder, db)
yearlyCosts = yearlyCosts.drop(columns=['database', 'scenario'])
yearlyEmissions, avgEmissions = tt.getEmissions(folder, db)
yearlyEmissions = yearlyEmissions.drop(
columns=['database', 'scenario'])
# Capacity and Activity by Fuel By Year
switch = 'tech'
capacityByFuel = tt.getCapacity(folder, db, switch=switch)
capacityByFuel = capacityByFuel.drop(columns=['database', 'scenario'])
capacityByFuel = capacityByFuel.set_index('fuelOrTech')
ActivityByYearFuel = tt.getActivity(folder, db, switch=switch)
ActivityByYearFuel = ActivityByYearFuel.drop(
columns=['database', 'scenario'])
ActivityByYearFuel = ActivityByYearFuel.set_index('fuelOrTech')
# Package Outputs
output['LCOE'] = LCOE.loc[0, 'LCOE']
output['avgEmissions'] = avgEmissions.loc[0, 'avgEmissions']
for col in yearlyCosts.columns:
label = 'cost-' + str(col)
output[label] = yearlyCosts.loc[0, col]
for col in yearlyEmissions.columns:
label = 'emis-' + str(col)
output[label] = yearlyEmissions.loc[0, col]
# CapacityByYearFuel
for ind in capacityByFuel.index:
for col in capacityByFuel.columns:
label = 'cap_' + str(col) + '-' + str(ind)
output[label] = capacityByFuel.loc[ind, col]
# ActivityByYearFuel
for ind in ActivityByYearFuel.index:
for col in ActivityByYearFuel.columns:
label = 'act_' + str(col) + '-' + str(ind)
output[label] = ActivityByYearFuel.loc[ind, col]
else: # if errors
output['LCOE'] = np.nan
output['avgEmissions'] = np.nan
return output
import temoatools as tt
model_filename = 'data_A.sqlite' # Expected to be in "\\databases"
temoa_path = 'C:/temoa/temoa' # path to temoa directory that contains temoa_model/
tt.run(model_filename,
temoa_path=temoa_path,
saveEXCEL=False,
debug=True,
solver='cplex')
scenarioNames = ['BATT']
paths = 'paths.csv'
temoa_path = os.path.normcase('C:\\Users\\benne\PycharmProjects\\temoatools\\temoa_stochastic')
project_path = os.path.normcase('C:\Users\\benne\\PycharmProjects\\temoatools\\projects\uva'')
#=======================================================
# Move modelInputs_XLSX to database
#=======================================================
modelInputs = tt.move_data_to_db(modelInputs_XLSX, path=project_path)
# Build Model
tt.build(modelInputs, scenarioInputs, 'Batt', 'Batt', path=project_path)
# Run Model
tt.run('Batt', saveEXCEL=False, temoa_path=temoa_path, debug=True, solver=solver)
#====================================
# Perform Simulations
option = 2 # 1 - Run single, 2 - Run all
#====================================
num_cores = multiprocessing.cpu_count() -1 # Save one core for other processes
if option==1:
# Perform single simulation
evaluateModel(modelInputs, scenarioInputs, scenarioNames[0], paths)
elif option == 2:
# Perform simulations in parallel
with parallel_backend('multiprocessing', n_jobs=num_cores):
Parallel(n_jobs=num_cores,verbose=5)(delayed(evaluateModel)(modelInputs, scenarioInputs, scenarioName, paths) for scenarioName in scenarioNames)
def evaluateMonteCarlo(modelInputs, scenarioXLSX, scenarioName, temoa_path, cases, caseNum):
# Unique filename
model_filename = scenarioName + '_MC_' + str(caseNum)
# Prepare monte carlo inputs
cols = ['type', 'variable', 'tech', caseNum]
MCinputs = cases.ix[:, cols]
MCinputs = MCinputs.rename(columns={caseNum: 'multiplier'})
# Build Model
tt.build(modelInputs, scenarioXLSX, scenarioName, model_filename, MCinputs=MCinputs, path='data')
# Run Model
tt.run(model_filename, temoa_path=temoa_path, saveEXCEL=False)
# Analyze Results
folder = os.getcwd() + '\\Databases'
db = model_filename + '.sqlite'
yearlyCosts, LCOE = tt.getCosts(folder, db)
yearlyEmissions, avgEmissions = tt.getEmissions(folder, db)
# Capacity and Activity by Fuel By Year
createPlots = 'N' # Create default plots
saveData = 'N' # Do not save data as a csv or xls
sectorName = 'electric' # Name of sector to be analyzed
switch = 'fuel'
capacityByFuel = tt.getCapacity(folder, db, switch=switch)
key = capacityByFuel.keys()[0]
cap = capacityByFuel[key]
ActivityByYearFuel = tt.getActivity(folder, db, switch=switch)
key = ActivityByYearFuel.keys()[0]
act = ActivityByYearFuel[key]
# Move results to series
col = yearlyCosts.columns[0]
yearlyCosts = yearlyCosts[col]
LCOE = LCOE[col]
col = yearlyEmissions.columns[0]
yearlyEmissions = yearlyEmissions[col]
avgEmissions = avgEmissions[col]
# Package Outputs
output = pd.Series()
output['db'] = db
output['caseNum'] = caseNum
output['LCOE'] = LCOE
output['avgEmissions'] = avgEmissions
for ind in yearlyCosts.index:
label = 'cost-' + str(ind)
output[label] = yearlyCosts.loc[ind]
for ind in yearlyEmissions.index:
label = 'emis-' + str(ind)
output[label] = yearlyEmissions.loc[ind]
# CapacityByYearFuel
for ind in cap.index:
for col in cap.columns:
label = 'cap_' + str(col) + '-' + str(ind)
output[label] = cap.loc[ind, col]
# ActivityByYearFuel
for ind in act.index:
for col in act.columns:
label = 'act_' + str(col) + '-' + str(ind)
output[label] = act.loc[ind, col]
return output
