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

• URL: http://arxiv.org/abs/2502.00034v1
• Date: Fri, 24 Jan 2025 21:40:19 GMT
• Title: Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
• Authors: Yassine El Manyari, Anton R. Fuxjager, Stefan Zahlner, Joost Van Dijk, Alberto Castagna, Davide Barbieri, Jan Viebahn, Marcel Wasserer,
• Abstract summary: We present a two-phase, efficient and scalable Multi-Objective optimisation (MOO) method designed for grid topology control. We validate our approach using historical data from TenneT, a European Transmission System Operator (TSO) Based on current congestion costs and inefficiencies in grid operations, adopting our approach by TSOs could potentially save millions of euros annually.
• Score: 0.1806830971023738
• License:
• 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.

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