def parameterSweep(sweep_inputs, index):
# Record time to solve
t0 = time.time()
# create and run model
data = pd.read_csv('model_inputs.csv')
model_inputs = ocaes.get_default_inputs(storage_type=sweep_inputs.loc[index, 'storage_type'])
if sweep_inputs.loc[index, 'storage_type'] == 'wind_only':
model_inputs['X_well'] = 0.0
model_inputs['X_cmp'] = 0.0
model_inputs['X_exp'] = 0.0
else:
model_inputs['X_well'] = sweep_inputs.loc[index, 'capacity']
model_inputs['X_cmp'] = sweep_inputs.loc[index, 'capacity']
model_inputs['X_exp'] = sweep_inputs.loc[index, 'capacity']
if sweep_inputs.loc[index, 'storage_type'] == 'OCAES-10':
model_inputs['pwr2energy'] = 10
if sweep_inputs.loc[index, 'storage_type'] == 'OCAES-24':
model_inputs['pwr2energy'] = 24
model = ocaes(data, model_inputs)
df, s = model.get_full_results()
s['revenue'], s['LCOE'], s['COVE'], s['emissions_avoided'] = model.post_process(s)
# Display Elapsed Time
t1 = time.time()
print("Time Elapsed: " + str(round(t1 - t0, 2)) + " s")
# Combine inputs and results into output and then return
return pd.concat([sweep_inputs.loc[index, :], s], axis=0)
def parameterSweep(sweep_inputs, index):
# Record time to solve
t0 = time.time()
# create and run model
data = pd.read_csv('data_manual.csv')
model_inputs = ocaes.get_default_inputs()
model_inputs['eta_storage'] = sweep_inputs.loc[index, 'efficiency']
model_inputs['X_well'] = sweep_inputs.loc[index, 'capacity']
model_inputs['X_cmp'] = sweep_inputs.loc[index, 'capacity']
model_inputs['X_exp'] = sweep_inputs.loc[index, 'capacity']
model = ocaes(data, model_inputs)
df, s = model.get_full_results()
s = model.calculate_LCOE(s)
# Display Elapsed Time
t1 = time.time()
print("Time Elapsed: " + str(round(t1 - t0, 2)) + " s")
# Combine inputs and results into output and then return
return pd.concat([sweep_inputs.loc[index,:]], axis=0), s
import pandas as pd
from OCAES import ocaes
# ----------------------
# wind only
# ----------------------
data = pd.read_csv('data_manual.csv')
inputs = ocaes.get_default_inputs()
inputs['X_well'] = 0
inputs['X_cmp'] = 0
inputs['X_exp'] = 0
model = ocaes(data, inputs)
df, s_wind = model.get_full_results()
s_wind = model.calculate_LCOE(s_wind)
# ----------------------
# OCAES
# ----------------------
data = pd.read_csv('data_manual.csv')
inputs = ocaes.get_default_inputs()
inputs['X_cmp'] = 500
inputs['X_exp'] = 500
model = ocaes(data, inputs)
df, s_ocaes = model.get_full_results()
s_ocaes = model.calculate_LCOE(s_ocaes)
# ----------------------
# battery
# ----------------------
data = pd.read_csv('data_manual.csv')
inputs = ocaes.get_default_inputs()
def parameter_sweep(sweep_input):
# Record time to solve
t0 = time.time()
# create model
data = pd.read_csv(sweep_input['timeseries_filename'])
model_inputs = ocaes.get_default_inputs()
model_inputs['objective'] = sweep_input['objective']
# scenario specific inputs
model_inputs['pwr2energy'] = sweep_input['pwr2energy']
model_inputs['eta_storage'] = sweep_input['eta_storage']
model_inputs['CC_exp'] = sweep_input['CC_exp']
model_inputs['C_well'] = 0.0
model_inputs['C_cmp'] = 0.0
model_inputs['C_exp'] = sweep_input['C_exp']
model_inputs['V_cmp'] = 0.0
model_inputs['V_exp'] = sweep_input['V_exp']
model_inputs['F_well'] = 0.0
model_inputs['F_cmp'] = 0.0
model_inputs['F_exp'] = sweep_input['F_exp']
model_inputs['L_well'] = sweep_input['L_well']
model_inputs['L_cmp'] = sweep_input['L_cmp']
model_inputs['L_exp'] = sweep_input['L_exp']
# capacity inputs
if sweep_input['scenario'] == 'wind_only':
model_inputs['X_well'] = 0.0
model_inputs['X_cmp'] = 0.0
model_inputs['X_exp'] = 0.0
model_inputs['eta_storage'] = 1.0
else:
model_inputs['X_well'] = sweep_input['capacity']
model_inputs['X_cmp'] = sweep_input['capacity']
model_inputs['X_exp'] = sweep_input['capacity']
if model_inputs['objective'] == 'CONST_DISPATCH_OPT':
model_inputs['X_dispatch'] = sweep_input['capacity']
print('Scenario: ' + str(sweep_input['scenario']))
print('X_wind: ' + str(model_inputs['X_wind']))
print('Capacity: ' + str(sweep_input['capacity']))
print('Objective: ' + str(sweep_input['objective']))
# run model
model = ocaes(data, model_inputs)
df, s = model.get_full_results()
revenue, LCOE, COVE, avoided_emissions, ROI = model.post_process(s)
# save results
results = pd.Series(index=('revenue', 'LCOE', 'COVE', 'avoided_emissions',
'solve_time'),
dtype='float64')
results['revenue'] = revenue
results['LCOE'] = LCOE
results['COVE'] = COVE
results['avoided_emissions'] = avoided_emissions
results['ROI'] = ROI
results['solve_time'] = time.time() - t0
# additional outputs
results['avoided_emissions_tonnes'] = s['avoided_emissions']
results['yearly_electricity_MWh'] = s['yearly_electricity']
results['yearly_electricity_generated_MWh'] = s[
'yearly_electricity_generated']
results['yearly_electricity_purchased_MWh'] = s[
'yearly_electricity_purchased']
results['yearly_exp_usage_MWh'] = s['yearly_exp_usage']
results['yearly_cmp_usage_MWh'] = s['yearly_cmp_usage']
results['yearly_curtailment_MWh'] = s['yearly_curtailment']
results['yearly_curtailment_fr'] = s['yearly_curtailment'] / s[
'yearly_electricity_generated']
results['yearly_electricity_revenue_dollars'] = s[
'yearly_electricity_revenue']
results['yearly_capacity_credit_dollars'] = s['yearly_capacity_credit']
results['yearly_total_revenue_dollars'] = s['yearly_total_revenue']
results['yearly_costs_dollars'] = s['yearly_costs']
results['yearly_profit_dollars'] = s['yearly_profit']
results['yearly_electricity_value_dollars'] = s['yearly_electricity_value']
results['price_grid_average_dollarsPerMWh'] = s['price_grid_average']
results['X_wind'] = s['X_wind']
results['X_well'] = s['X_well']
results['X_exp'] = s['X_exp']
results['X_cmp'] = s['X_cmp']
results['X_dispatch'] = s['X_dispatch']
results['storage_duration_hrs'] = s['E_well_duration']
results['E_well_init_fr'] = s['E_well_init_fr']
results['E_well_init'] = s['E_well_init']
# combine inputs and results to return in single series
single_output = pd.concat([sweep_input, results])
return single_output
