transformers = [pp_coal, pp_lig, pp_gas, pp_oil, pp_chp]
renew_sources = [pv, wind_on]
sinks = [demand_th, demand_el]
components = transformers + renew_sources + sinks
entities = components + buses
simulation = es.Simulation(solver='glpk', timesteps=timesteps,
stream_solver_output=True,
objective_name='minimize_costs')
energysystem = es.EnergySystem(entities=entities, simulation=simulation)
om = OptimizationModel(energysystem=energysystem)
om.solve(solver='gurobi', debug=True, tee=True, duals=False)
pp.results_to_objects(om)
components = transformers + renew_sources
if __name__ == "__main__":
def plot_dispatch(bus_to_plot):
# plotting: later as multiple pdf with pie-charts and topology?
import numpy as np
import matplotlib as mpl
import matplotlib.cm as cm
plot_data = renew_sources+transformers
# data preparation
x = np.arange(len(timesteps))
commodities = [rgas]
storages = [storage]
sinks = [demand]
# groupt components
components = transformers + renewable_sources + storages + sinks + commodities
# create list of all entities
entities = components + buses
# TODO: other solver libraries should be passable
simulation = es.Simulation(solver="glpk", timesteps=timesteps, stream_solver_output=True)
energysystem = es.EnergySystem(entities=entities, simulation=simulation)
energysystem.optimize()
# write results back to objects
pp.results_to_objects(energysystem.optimization_model)
if __name__ == "__main__":
import postprocessing as pp
data = renewable_sources + transformers + storages
pp.plot_dispatch("bel", timesteps, data, storage, demand)
# pp.plot_dispatchplt.show()
pp.print_results("bel", data, demand, transformers, storage, energysystem)
for pp in region.renew_pps:
entities.append(pp)
components.append(pp)
for pp in region.sinks:
entities.append(pp)
components.append(pp)
timesteps = [t for t in range(876)]
om = OptimizationModel(entities=entities, timesteps=timesteps,
options={'invest': False, 'slack': {
'excess': True, 'shortage': True}})
om.solve(solver='gurobi', debug=True, tee=False, duals=True)
postprocessing.results_to_objects(om)
df = pd.DataFrame()
for c in components:
for k in c.results["out"].keys():
df[c.uid] = c.results["out"][k]
#for t in transmissions:
# for k in t.results["out"].keys():
# df[t.uid] = t.results["out"][k]
#for c in components:
# for k in c.results["in"].keys():
# df[c.uid] = c.results["in"][k]
#
#for c in components:
# c.calc_emissions()
