Optimisation

Optimization functions which can be called within a pypsa.Network via n.optimize or n.optimization.func. For example n.optimize.create_model().

Statistic methods

__call__([snapshots, ...])	Optimize the pypsa network using linopy.
create_model([snapshots, ...])	Create a linopy.Model instance from a pypsa network.
solve_model([extra_functionality, ...])	Solve an already created model and assign its solution to the network.
assign_solution()	Map solution to network components.
assign_duals([assign_all_duals])	Map dual values i.e. shadow prices to network components.
post_processing()	Post-process the optimized network.
optimize_transmission_expansion_iteratively([...])	Iterative linear optimization updating the line parameters for passive AC and DC lines.
optimize_security_constrained([snapshots, ...])	Computes Security-Constrained Linear Optimal Power Flow (SCLOPF).
optimize_with_rolling_horizon([snapshots, ...])	Optimizes the network in a rolling horizon fashion.
optimize_mga([snapshots, ...])	Run modelling-to-generate-alternatives (MGA) on network to find near- optimal solutions.
optimize_and_run_non_linear_powerflow([...])	Optimizes the network and then performs a non-linear power flow for all snapshots.
fix_optimal_capacities()	Fix capacities of extendable assets to optimized capacities.
fix_optimal_dispatch()	Fix dispatch of all assets to optimized values.
add_load_shedding([suffix, buses, sign, ...])	Add load shedding in form of generators to all or a subset of buses.