CommonPower: A Framework for Safe Data-Driven Smart Grid Control

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 implement a simulation environment. Another unique contribution of CommonPower is the automatic synthesis of model predictive controllers and safeguards. Beyond offering a unified interface for single-agent RL, multi-agent RL, and optimal control, CommonPower includes a training pipeline for machine-learning-based forecasters as well as a flexible mechanism for incorporating feedback of safeguards into the learning updates of RL controllers.

For the corresponding code repository, see https://github.com/TUMcps/commonpower

References in corpus (8)

• Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
• pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems
• Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
• CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
• Convex Restriction of Power Flow Feasibility Sets
• Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems
• Guarantees for Real Robotic Systems: Unifying Formal Controller Synthesis and Reachset-Conformant Identification
• Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education