el1xr_opt

el1xr_opt#

Simplicity and Transparency: A modular optimization model for power‑system planning & operations

What is it?#

el1xr_opt is a Python library for optimization studies in power-system planning and operations. It builds a multi-vector model – electricity, hydrogen and heat with storage and demand-side flexibility – over a period/scenario/load-level horizon, and solves it with Pyomo.

Key features#

  • Multi-vector: electricity, hydrogen (electrolyser, fuel cell, storage) and a behind-the-meter heat sector (heat pump, boiler, thermal store, heat-to-power), coupled through the energy balances.

  • Time structure: period → scenario → load level (hours or representative periods, with per-level durations).

  • Investment + operation: optional capacity-sizing of candidate generators and storage, on the same discounted footing as operation.

  • Markets: day-ahead energy, retail tariffs (energy, capacity and a top-N peak-demand charge), and frequency reserves (FCR-D / FCR-N).

  • Solve modes: a single monolithic solve, or Benders decomposition (by scenario or by time window, with storage-boundary and peak-threshold linking).

  • Solvers: HiGHS by default; Gurobi, CBC, CPLEX and Ipopt are also supported.

  • Input/output: a case is a folder of CSV files or a single .duckdb file (read identically); results are written to results.duckdb by default, with CSV and plots available on request.

This documentation is organized around getting started, user guide, concepts, and an API reference generated from the source docstrings.

Index#

Get started

User guide

Concepts

API reference

Developer