Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control

TL;DR

This paper explores the deployment of reinforcement learning agents for day-ahead planning and real-time control in power grids, demonstrating potential benefits and challenges in integrating AI into complex grid operations.

Contribution

It adapts RL-based agents to realistic power grid workflows, analyzes their performance, and discusses robustness, advancing towards practical AI-assisted grid management.

Findings

RL agents improve grid operation efficiency

Analysis shows potential robustness and limitations

Prototypes help bridge research and real-world deployment

Abstract

The ongoing transition to renewable energy is increasing the share of fluctuating power sources like wind and solar, raising power grid volatility and making grid operation increasingly complex and costly. In our prior work, we have introduced a congestion management approach consisting of a redispatching optimizer combined with a machine learning-based topology optimization agent. Compared to a typical redispatching-only agent, it was able to keep a simulated grid in operation longer while at the same time reducing operational cost. Our approach also ranked 1st in the L2RPN 2022 competition initiated by RTE, Europe's largest grid operator. The aim of this paper is to bring this promising technology closer to the real world of power grid operation. We deploy RL-based agents in two settings resembling established workflows, AI-assisted day-ahead planning and realtime control, in an attempt to show the benefits and caveats of this new technology. We then analyse congestion, redispatching and switching profiles, and elementary sensitivity analysis providing a glimpse of operation robustness. While there is still a long way to a real control room, we believe that this paper and the associated prototypes help to narrow the gap and pave the way for a safe deployment of RL agents in tomorrow's power grids.