Source code for calliope.backend.pyomo.objective

Copyright (C) 2013-2018 Calliope contributors listed in AUTHORS.

Licensed under the Apache 2.0 License (see LICENSE file).

Objective functions.


import pyomo.core as po  # pylint: disable=import-error
from import load_function

[docs]def minmax_cost_optimization(backend_model, cost_class, sense): """ Minimize or maximise total system cost for specified cost class. If unmet_demand is in use, then the calculated cost of unmet_demand is added or subtracted from the total cost in the opposite sense to the objective. .. container:: scrolling-wrapper .. math:: min: z = \sum_{loc::tech_{cost}} cost(loc::tech, cost=cost_{k})) + \sum_{loc::carrier,timestep} unmet\_demand(loc::carrier, timestep) \\times bigM max: z = \sum_{loc::tech_{cost}} cost(loc::tech, cost=cost_{k})) - \sum_{loc::carrier,timestep} unmet\_demand(loc::carrier, timestep) \\times bigM """ def obj_rule(backend_model): if backend_model.__calliope_model_data__['attrs'].get('run.ensure_feasibility', False): unmet_demand = sum( backend_model.unmet_demand[loc_carrier, timestep] - backend_model.unused_supply[loc_carrier, timestep] for loc_carrier in backend_model.loc_carriers for timestep in backend_model.timesteps ) * backend_model.bigM if sense == 'maximize': unmet_demand *= -1 else: unmet_demand = 0 return ( sum( backend_model.cost[cost_class, loc_tech] for loc_tech in backend_model.loc_techs_cost ) + unmet_demand ) backend_model.obj = po.Objective(sense=load_function('pyomo.core.' + sense), rule=obj_rule) backend_model.obj.domain = po.Reals
[docs]def check_feasibility(backend_model, **kwargs): """ Dummy objective, to check that there are no conflicting constraints. .. container:: scrolling-wrapper .. math:: min: z = 1 """ def obj_rule(backend_model): return 1 backend_model.obj = po.Objective(sense=po.minimize, rule=obj_rule) backend_model.obj.domain = po.Reals