"""
Copyright (C) 2013-2018 Calliope contributors listed in AUTHORS.
Licensed under the Apache 2.0 License (see LICENSE file).
"""
import pyomo.core as po # pylint: disable=import-error
[docs]def initialize_decision_variables(backend_model):
"""
Defines decision variables.
==================== ========================================
Variable Dimensions
==================== ========================================
energy_cap loc_techs
carrier_prod loc_tech_carriers_prod, timesteps
carrier_con loc_tech_carriers_con, timesteps
cost costs, loc_techs_cost
resource_area loc_techs_area,
storage_cap loc_techs_store
storage loc_techs_store, timesteps
resource_con loc_techs_supply_plus, timesteps
resource_cap loc_techs_supply_plus
carrier_export loc_tech_carriers_export, timesteps
cost_var costs, loc_techs_om_cost, timesteps
cost_investment costs, loc_techs_investment_cost
purchased loc_techs_purchase
units loc_techs_milp
operating\\_units loc_techs_milp, timesteps
unmet\\_demand loc_carriers, timesteps
unused\\_supply loc_carriers, timesteps
==================== ========================================
"""
model_data_dict = backend_model.__calliope_model_data
run_config = backend_model.__calliope_run_config
##
# Variables which are always assigned
##
if run_config['mode'] != 'operate':
backend_model.energy_cap = po.Var(backend_model.loc_techs, within=po.NonNegativeReals)
backend_model.carrier_prod = po.Var(backend_model.loc_tech_carriers_prod, backend_model.timesteps, within=po.NonNegativeReals)
backend_model.carrier_con = po.Var(backend_model.loc_tech_carriers_con, backend_model.timesteps, within=po.NegativeReals)
backend_model.cost = po.Var(backend_model.costs, backend_model.loc_techs_cost, within=po.Reals)
##
# Conditionally assigned variables
##
if 'loc_techs_area' in model_data_dict['sets'] and run_config['mode'] != 'operate':
backend_model.resource_area = po.Var(backend_model.loc_techs_area, within=po.NonNegativeReals)
if 'loc_techs_store' in model_data_dict['sets']:
if run_config['mode'] != 'operate':
backend_model.storage_cap = po.Var(backend_model.loc_techs_store, within=po.NonNegativeReals)
if hasattr(backend_model, 'clusters') and hasattr(backend_model, 'datesteps'):
backend_model.storage_inter_cluster = po.Var(backend_model.loc_techs_store, backend_model.datesteps, within=po.NonNegativeReals)
backend_model.storage_intra_cluster_max = po.Var(backend_model.loc_techs_store, backend_model.clusters, within=po.Reals)
backend_model.storage_intra_cluster_min = po.Var(backend_model.loc_techs_store, backend_model.clusters, within=po.Reals)
storage_within = po.Reals
else:
storage_within = po.NonNegativeReals
backend_model.storage = po.Var(backend_model.loc_techs_store, backend_model.timesteps, within=storage_within)
if 'loc_techs_supply_plus' in model_data_dict['sets']:
backend_model.resource_con = po.Var(backend_model.loc_techs_supply_plus, backend_model.timesteps, within=po.NonNegativeReals)
if run_config['mode'] != 'operate':
backend_model.resource_cap = po.Var(backend_model.loc_techs_supply_plus, within=po.NonNegativeReals)
if 'loc_techs_export' in model_data_dict['sets']:
backend_model.carrier_export = po.Var(backend_model.loc_tech_carriers_export, backend_model.timesteps, within=po.NonNegativeReals)
if 'loc_techs_om_cost' in model_data_dict['sets']:
backend_model.cost_var = po.Var(backend_model.costs, backend_model.loc_techs_om_cost, backend_model.timesteps, within=po.Reals)
if 'loc_techs_investment_cost' in model_data_dict['sets'] and run_config['mode'] != 'operate':
backend_model.cost_investment = po.Var(backend_model.costs, backend_model.loc_techs_investment_cost, within=po.Reals)
if 'loc_techs_purchase' in model_data_dict['sets'] and run_config['mode'] != 'operate':
backend_model.purchased = po.Var(backend_model.loc_techs_purchase, within=po.Binary)
if 'group_demand_share_per_timestep_decision' in model_data_dict['data'] and run_config['mode'] != 'operate':
backend_model.demand_share_per_timestep_decision = po.Var(backend_model.loc_tech_carriers_prod, within=po.NonNegativeReals)
if 'loc_techs_milp' in model_data_dict['sets']:
if run_config['mode'] != 'operate':
backend_model.units = po.Var(backend_model.loc_techs_milp, within=po.NonNegativeIntegers)
backend_model.operating_units = po.Var(backend_model.loc_techs_milp, backend_model.timesteps, within=po.NonNegativeIntegers)
# For any milp tech, we need to update energy_cap, as energy_cap_max and energy_cap_equals
# are replaced by energy_cap_per_unit
if run_config['mode'] == 'operate':
for k, v in backend_model.units.items():
backend_model.energy_cap[k] = v * backend_model.energy_cap_per_unit[k]
if 'loc_techs_asynchronous_prod_con' in model_data_dict['sets']:
backend_model.prod_con_switch = po.Var(backend_model.loc_techs_asynchronous_prod_con, backend_model.timesteps, within=po.Binary)
backend_model.bigM = run_config.get('bigM', 1e10)
if run_config.get('ensure_feasibility', False):
backend_model.unmet_demand = po.Var(backend_model.loc_carriers, backend_model.timesteps, within=po.NonNegativeReals)
backend_model.unused_supply = po.Var(backend_model.loc_carriers, backend_model.timesteps, within=po.NegativeReals)
backend_model.bigM = run_config.get('bigM', 1e10)