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Power Grid Congestion Management via Topology Optimization with AlphaZero

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arxiv 2211.05612 v1 pith:JFAIQV7M submitted 2022-11-10 cs.AI cs.LG

classification cs.AIcs.LG
keywords gridpowercongestionenergymanagementoptimizationredispatchingtopology
verification ladder T0 review T1 audit T2 compute T3 formal
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The energy sector is facing rapid changes in the transition towards clean renewable sources. However, the growing share of volatile, fluctuating renewable generation such as wind or solar energy has already led to an increase in power grid congestion and network security concerns. Grid operators mitigate these by modifying either generation or demand (redispatching, curtailment, flexible loads). Unfortunately, redispatching of fossil generators leads to excessive grid operation costs and higher emissions, which is in direct opposition to the decarbonization of the energy sector. In this paper, we propose an AlphaZero-based grid topology optimization agent as a non-costly, carbon-free congestion management alternative. Our experimental evaluation confirms the potential of topology optimization for power grid operation, achieves a reduction of the average amount of required redispatching by 60%, and shows the interoperability with traditional congestion management methods. Our approach also ranked 1st in the WCCI 2022 Learning to Run a Power Network (L2RPN) competition. Based on our findings, we identify and discuss open research problems as well as technical challenges for a productive system on a real power grid.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning

    math.OC 2026-05 unverdicted novelty 7.0 of 10

    The block algorithm exactly enumerates the Pareto front for sequential multi-objective transmission topology planning in polynomial time for fixed bounds and outperforms a customized NSGA-III heuristic on real TenneT data.

  2. Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning

    math.OC 2026-05 unverdicted novelty 6.0 of 10

    Exact block algorithm enumerates complete Pareto fronts for sequential multi-objective transmission topology control under N-1 constraints; outperforms tested NSGA-III heuristic on congested real-grid instance.

  3. Power Grid Control with Graph-Based Distributed Reinforcement Learning

    cs.LG 2025-09 conditional novelty 6.0 of 10

    A two-layer distributed RL system with one GNN-observing agent per power line and a learned manager keeps the Grid2Op case14 grid alive far longer than the do-nothing baseline.

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