Workshop: Reinforcement Learning for Real Life (RL4RealLife) Workshop

Power Grid Congestion Management via Topology Optimization with AlphaZero

Matthias Dorfer · Anton R. Fuxjaeger · Kristián Kozák · Patrick Blies · Marcel Wasserer


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. 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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