PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks

TL;DR

PowRL is a reinforcement learning framework designed to enhance the robustness and reliability of power networks by managing overloads and optimizing topology to prevent blackouts amid increasing renewable integration and dynamic loads.

Contribution

The paper introduces a novel RL-based approach with a heuristic for overload management and topology optimization, achieving top performance in power network robustness challenges.

Findings

Outperforms existing methods in robustness challenges

Top leaderboard position in L2RPN NeurIPS 2020 challenge

State-of-the-art performance in test scenarios

Abstract

Power grids, across the world, play an important societal and economical role by providing uninterrupted, reliable and transient-free power to several industries, businesses and household consumers. With the advent of renewable power resources and EVs resulting into uncertain generation and highly dynamic load demands, it has become ever so important to ensure robust operation of power networks through suitable management of transient stability issues and localize the events of blackouts. In the light of ever increasing stress on the modern grid infrastructure and the grid operators, this paper presents a reinforcement learning (RL) framework, PowRL, to mitigate the effects of unexpected network events, as well as reliably maintain electricity everywhere on the network at all times. The PowRL leverages a novel heuristic for overload management, along with the RL-guided decision making on optimal topology selection to ensure that the grid is operated safely and reliably (with no overloads). PowRL is benchmarked on a variety of competition datasets hosted by the L2RPN (Learning to Run a Power Network). Even with its reduced action space, PowRL tops the leaderboard in the L2RPN NeurIPS 2020 challenge (Robustness track) at an aggregate level, while also being the top performing agent in the L2RPN WCCI 2020 challenge. Moreover, detailed analysis depicts state-of-the-art performances by the PowRL agent in some of the test scenarios.