# initialise the operational model om = solph.Model(energysystem) # if tee_switch is true solver messages will be displayed logging.info("Solve the optimization problem") - om.write('my_model.lp', io_options={'symbolic_solver_labels': True}) + om.write("my_model.lp", io_options={"symbolic_solver_labels": True}) om.solve(solver="cbc", solve_kwargs={"tee": True}) ########################################################################## # Check and plot the results ##########################################################################

if not m.TSAM_MODE: self.storage_content = Var( self.INVESTSTORAGES, m.TIMESTEPS, within=NonNegativeReals ) else: - self.storage_content_inter = Var(self.INVESTSTORAGES, m.CLUSTERS_OFFSET) + self.storage_content_inter = Var( + self.INVESTSTORAGES, m.CLUSTERS_OFFSET + ) self.storage_content_intra = Var( self.INVESTSTORAGES, m.TIMEINDEX_TYPICAL_CLUSTER_OFFSET ) # set the initial intra storage content # first timestep in intra storage is always zero

* n.nominal_storage_capacity * m.timeincrement[t] ) expr += n.fixed_losses_absolute[t] * m.timeincrement[t] expr += ( - -m.flow[i[n], n, p, t] * n.inflow_conversion_factor[t] - ) * m.timeincrement[t] + -m.flow[i[n], n, p, t] * n.inflow_conversion_factor[t] + ) * m.timeincrement[t] expr += ( - m.flow[n, o[n], p, t] / n.outflow_conversion_factor[t] - ) * m.timeincrement[t] + m.flow[n, o[n], p, t] / n.outflow_conversion_factor[t] + ) * m.timeincrement[t] return expr == 0 def _intra_storage_balance_rule(block, n, p, k, g): """ Rule definition for the storage balance of every storage n and