def test_bus_to_sink_outputs_in_results_dataframe(self):
bus = Bus(uid="bus")
source = FS(uid="source", outputs=[bus], val=[0.5], out_max=[1])
sink = Sink(uid="sink", inputs=[bus], val=[1])
es = self.es
om = OM(es)
es.results = om.results()
es.results[bus][sink] = [0.7]
rdf = RDF(energy_system=es)
try:
eq_(rdf.loc[(slice(None), slice(None), slice(None), "sink"), :
].val[0],
0.7,
"Output from bus to sink does not have the correct value.")
except KeyError:
self.failed = True
if self.failed:
ok_(False,
"Output from bus to sink does not appear in results dataframe.")
es.results[bus][bus] = [-1]
rdf = RDF(energy_system=es)
try:
eq_(rdf.loc[(slice(None), slice(None), slice(None), "sink"), :
].val[0],
0.7,
"Output from bus to sink does not have the correct value.")
except KeyError:
self.failed = True
if self.failed:
ok_(False,
"Output from bus (with duals) to sink " +
"does not appear in results dataframe.")
def optimize(self, om=None):
"""Start optimizing the energy system using solph.
Parameters
----------
om : :class:`OptimizationModel <oemof.solph.optimization_model.OptimizationModel>`, optional
The optimization model used to optimize the :class:`EnergySystem`.
If not given, an :class:`OptimizationModel
<oemof.solph.optimization_model.OptimizationModel>` instance local
to this method is created using the current :class:`EnergySystem`
instance as an argument.
You only need to supply this if you want to observe any side
effects that solving has on the `om`.
Returns
-------
self : :class:`EnergySystem`
"""
if om is None:
om = OM(energysystem=self)
om.solve(solver=self.simulation.solver, debug=self.simulation.debug,
verbose=self.simulation.verbose,
duals=self.simulation.duals,
solve_kwargs=self.simulation.solve_kwargs)
self.results = om.results()
return self
def optimize(self):
if self.optimization_model is None:
self.optimization_model = OM(energysystem=self)
self.optimization_model.solve(
solver=self.simulation.solver, debug=self.simulation.debug, tee=self.simulation.stream_solver_output
)
def test_issue_74(self):
Storage.optimization_options.update({'investment': True})
bus = Bus(uid="bus")
store = Storage(uid="store", inputs=[bus], outputs=[bus], c_rate_out=0.1,
c_rate_in=0.1)
sink = Sink(uid="sink", inputs=[bus], val=[1])
es = self.es
om = OM(es)
om.objective.set_value(-1)
es.results = om.results()
try:
es.dump()
except AttributeError as ae:
self.failed = ae
if self.failed:
ok_(False,
"EnergySystem#dump should not raise `AttributeError`: \n" +
" Error message: " + str(self.failed))
class EnergySystem:
r"""
"""
def __init__(self, **kwargs):
""
for attribute in ["regions", "entities", "simulation"]:
setattr(self, attribute, kwargs.get(attribute, {}))
self.optimization_model = kwargs.get("optimization_model", None)
# TODO: Condense signature (use Buse)
def connect(self, code1, code2, media, in_max, out_max, eta, transport_class):
""
if not transport_class == transport.Simple:
raise (
TypeError(
"Sorry, `EnergySystem.connect` currently only works with"
+ "a `transport_class` argument of"
+ str(transport.Simple)
)
)
for reg_out, reg_in in [(code1, code2), (code2, code1)]:
logging.debug("Creating simple {2} from {0} to {1}".format(reg_out, reg_in, transport_class))
uid = "_".join([reg_out, reg_in, media])
self.connections[uid] = transport_class(
uid=uid,
outputs=[self.regions[reg_out].buses["_".join(["b", reg_out, media])]],
inputs=[self.regions[reg_in].buses["_".join(["b", reg_in, media])]],
out_max={"_".join(["b", reg_out, media]): out_max},
in_max={"_".join(["b", reg_in, media]): in_max},
eta=[eta],
)
def optimize(self):
if self.optimization_model is None:
self.optimization_model = OM(energysystem=self)
self.optimization_model.solve(
solver=self.simulation.solver, debug=self.simulation.debug, tee=self.simulation.stream_solver_output
)
buses = [bcoal, bgas, boil, blig, b_el, b_th]
# group components
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
out_max=[40, 30],
eta=[0.4, 0.3], opex_fix=0, opex_var=50,
co2_var=em_gas)
# group busses
buses = [bcoal, bgas, boil, blig, b_el, b_th]
# group components
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
om = OptimizationModel(energysystem=energy_system)
#
om.solve(solve_kwargs={'tee':True,
'keepfiles':False},
solver_cmdline_options={'min':''})
energy_system.results = om.results()
es_df = tpd.EnergySystemDataFrame(energy_system=energy_system)
# plot
es_df.plot_bus(bus_uid="b_el", bus_type="el", type="input",
date_from="2012-01-01 00:00:00",
date_to="2012-01-31 00:00:00", kind='bar',
title="January 2016", xlabel="Power in MW",
ylabel="Date", tick_distance=24*7,
df_plot_kwargs={'stacked':True ,'width':1, 'lw':0.2})
else:
Regions.connect(ebus_from,
ebus_export,
in_max=capacity,
out_max=capacity,
eta=eta_trans,
transport_class=transport.Simple)
##############################################################################
############################### FINAL STEPS ##################################
# change uid tuples to strings
for entity in Regions.entities:
entity.uid = str(entity.uid)
# create optimisation model from Regions instance
om = OptimizationModel(energysystem=Regions)
# add constraints
constraints = hlsb.get_constraint_values(conn_oedb, scenario)
Export_Regions = ('MV', 'ST', 'SN', 'KJ', 'BE')
hlsb.add_constraint_export_minimum(om, Export_Regions, constraints)
hlsb.add_constraint_import_berlin(om, constraints)
hlsb.add_constraint_co2_emissions(om, co2_emissions, constraints)
hlsb.add_constraint_entities_BE(om)
# write LP file
#om.write_lp_file()
# solve problem
om.solve()
# save results to Regions instance
Regions.results = om.results()
# demand
demand_el = sink.Simple(uid="demand_el", inputs=[b_el],
val=[30, 10, 20, 40, 45, 50, 35, 25, 5, 12])
# Simple Transformer for b_el
pp_coal = transformer.Simple(uid='pp_lig', inputs=[bcoal], outputs=[b_el],
in_max=[None],
out_max=[10], eta=[0.41],
opex_fix=2, opex_var=40)
pp_gas = transformer.Simple(uid='pp_gas', inputs=[bgas], outputs=[b_el],
in_max=[None], out_max=[41], eta=[0.45],
opex_var=40)
# create optimization model
om = OptimizationModel(energysystem=energy_system)
#
energy_system.optimize(om)
#
# use outputlib to plot
esplot = tpd.DataFramePlot(energy_system=energy_system)
esplot.slice_unstacked(bus_uid="b_el", type="input")
esplot.plot(title="January 2016 Low coal costs",
stacked=True,
width=1, lw=0.1,
kind='bar')
plt.show()
# update variable costs and objective, solve again!