An Open Source Stochastic Unit Commitment Tool using the PyPSA-Framework

TL;DR

This paper introduces an open source stochastic unit commitment tool built on the PyPSA framework, enabling advanced energy system optimization under market and load uncertainties, demonstrated through a waste-to-energy case study in Germany.

Contribution

It provides a modular, open source stochastic UC tool integrated with PyPSA, including market mechanisms, stochastic optimization, and multistage capabilities, for practical energy system applications.

Findings

Successful implementation of stochastic UC for waste-to-energy in Germany

Demonstrated handling of market and heat load uncertainties

Enhanced PyPSA with modular stochastic optimization features

Abstract

This paper presents an open source stochastic unit commitment (UC) optimization tool, which is available on GitHub. In addition, it presents an example use case in which UC optimization is done for a waste-to-energy plant with heat storage and a battery energy storage system (BESS) in Germany, under uncertain day-ahead and balancing power (aFRR) market prices as well as heat load uncertainty. The tool consists of multiple modular extensions for the Python for Power System Analysis (PyPSA) framework, namely the implementation of market and bidding mechanisms, stochastic optimization and multistaging.