CommonPower: A Framework for Safe Data-Driven Smart Grid Control
Michael Eichelbeck, Hannah Markgraf, Matthias Althoff

TL;DR
CommonPower is a comprehensive Python framework designed for modeling, simulation, and safe control of power systems using machine learning, facilitating evaluation of RL algorithms with integrated safeguards and forecasting tools.
Contribution
It introduces the first general, modular framework for power system management tailored for machine learning, including automatic synthesis of controllers and safeguards.
Findings
Enables evaluation of RL algorithms in power systems with safety guarantees
Provides a unified interface for various control and learning methods
Includes tools for forecasting and feedback integration into RL updates
Abstract
The growing complexity of power system management has led to an increased interest in reinforcement learning (RL). To validate their effectiveness, RL algorithms have to be evaluated across multiple case studies. Case study design is an arduous task requiring the consideration of many aspects, among them the influence of available forecasts and the level of decentralization in the control structure. Furthermore, vanilla RL controllers cannot themselves ensure the satisfaction of system constraints, which makes devising a safeguarding mechanism a necessary task for every case study before deploying the system. To address these shortcomings, we introduce the Python tool CommonPower, the first general framework for the modeling and simulation of power system management tailored towards machine learning. Its modular architecture enables users to focus on specific elements without having to…
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Taxonomy
TopicsSmart Grid Security and Resilience · Smart Grid Energy Management · Electricity Theft Detection Techniques
MethodsFocus
