Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control

TL;DR

This paper introduces a scalable multi-objective optimisation method using reinforcement learning for real-world power grid topology control, enabling fast, efficient, and cost-effective operational planning.

Contribution

A novel two-phase MOO approach combining RL and rapid planning, tailored for large-scale power grid management, with demonstrated real-world effectiveness.

Findings

Plans generated within 4-7 minutes for unseen scenarios

Supports real-world grid management with minimal deployment time

Potential to save millions of euros annually for TSOs

Abstract

Power grid operators face increasing difficulties in the control room as the increase in energy demand and the shift to renewable energy introduce new complexities in managing congestion and maintaining a stable supply. Effective grid topology control requires advanced tools capable of handling multi-objective trade-offs. While Reinforcement Learning (RL) offers a promising framework for tackling such challenges, existing Multi-Objective Reinforcement Learning (MORL) approaches fail to scale to the large state and action spaces inherent in real-world grid operations. Here we present a two-phase, efficient and scalable Multi-Objective Optimisation (MOO) method designed for grid topology control, combining an efficient RL learning phase with a rapid planning phase to generate day-ahead plans for unseen scenarios. We validate our approach using historical data from TenneT, a European Transmission System Operator (TSO), demonstrating minimal deployment time, generating day-ahead plans within 4-7 minutes with strong performance. These results underline the potential of our scalable method to support real-world power grid management, offering a practical, computationally efficient, and time-effective tool for operational planning. Based on current congestion costs and inefficiencies in grid operations, adopting our approach by TSOs could potentially save millions of euros annually, providing a compelling economic incentive for its integration in the control room.