CommonPower: Supercharging Machine Learning for Smart Grids

The growing complexity of power system management has led to an increased interest in the use of reinforcement learning (RL). However, no tool for comprehensive and realistic benchmarking of RL in smart grids exists. One prerequisite for such a comparison is a safeguarding mechanism since vanilla RL controllers can not guarantee the satisfaction of system constraints. Other central requirements include flexible modeling of benchmarking scenarios, credible baselines, and the possibility to investigate the impact of forecast uncertainties. Our Python tool CommonPower is the first modular framework addressing these needs. CommonPower offers a unified interface for single-agent and multi-agent RL training algorithms and includes a built-in model predictive control approach based on a symbolic representation of the system equations. This makes it possible to combine model predictive controllers with RL controllers in the same system. Leveraging the symbolic system model, CommonPower facilitates the study of safeguarding strategies via the flexible formulation of safety layers. Furthermore equipped with a generic forecasting interface, CommonPower constitutes a versatile tool significantly augmenting the exploration of safe RL controllers in smart grids on several dimensions.