How-to guides¶
Short, task-focused guides for common operations. Each guide assumes you are already familiar with the Quickstart.
Find the minimum demand curtailment needed to keep a network feasible under voltage, pressure, and temperature bounds, in one call or fully customised.
Read standard IEEE test cases or any .mat MATPOWER file, and persist
networks to/from the native OMEF JSON format.
Import an existing pandapower network into monee with a single function call. (Experimental: complex elements may not convert correctly.)
Plug in HiGHS, Gurobi, GLPK, or CBC as the solver back-end, required for MILP / MIQCP problems such as the MISOCP optimal power flow.
Enable multi-island solves with enable_islanding(), add grid-forming
generators or sources, and retrieve per-island results.
Drive a multi-energy network through hundreds or thousands of timesteps with time-varying load profiles, ramp constraints, and rich result queries.
Jointly optimize storage dispatch, CHP scheduling, and linepack usage over a full planning horizon, including rolling-horizon MPC.
Attach electric, gas, and thermal storage to a network. Prescribe a charge
schedule via TimeseriesData or let the optimizer choose the dispatch.
Drive a network step by step from an external co-simulation framework with
Stepper, using variable step sizes, data overrides, and persistent state.
Find out why a solve failed: bound-violation reports, Pyomo
InfeasibilityReport, and GEKKO APM diagnostics.
Build lines, rings, stars, and paired supply/return district-heating
structures with the monee.express structure builders.
Overlay gas and district-heating networks plus coupling points on any power grid to generate reproducible multi-energy test systems.
Import CIM/CGMES grid models and ESDL energy-system descriptions. (Experimental: see the per-import transparency reports.)
Load ready-made multi-energy test cases (urban district, industrial hub, regional MES) for benchmarking, tutorials, and quick experiments.