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arXiv · 2502.00040

Multi-Objective Reinforcement Learning for Power Grid Topology Control

Thomas Lautenbacher·Ali Rajaei·Davide Barbieri·Jan Viebahn·Jochen L. Cremer
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Abstract

Transmission grid congestion increases as the electrification of various sectors requires transmitting more power. Topology control, through substation reconfiguration, can reduce congestion but its potential remains under-exploited in operations. A challenge is modeling the topology control problem to align well with the objectives and constraints of operators. Addressing this challenge, this paper investigates the application of multi-objective reinforcement learning (MORL) to integrate multiple conflicting objectives for power grid topology control. We develop a MORL approach using deep optimistic linear support (DOL) and multi-objective proximal policy optimization (MOPPO) to generate a set of Pareto-optimal policies that balance objectives such as minimizing line loading, topological deviation, and switching frequency. Initial case studies show that the MORL approach can provide valuable insights into objective trade-offs and improve Pareto front approximation compared to a random search baseline. The generated multi-objective RL policies are 30% more successful in preventing grid failure under contingencies and 20% more effective when training budget is reduced - compared to the common single objective RL policy.

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Thomas Lautenbacher, Ali Rajaei, Davide Barbieri, Jan Viebahn, Jochen L. Cremer. 2025-05-01. Multi-Objective Reinforcement Learning for Power Grid Topology Control. https://arxiv.org/abs/2502.00040

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