The power of collaboration: power grid control with multi-agent reinforcement learning

In our rapidly evolving world, effectively managing power grids has become increasingly challenging, primarily due to rising penetration of renewable energy sources and the growing energy demand. While renewable sources like wind and solar power are crucial on our path towards a 100% clean energy future, they introduce considerable uncertainty in power systems, thereby challenging conventional control strategies. Transmission line congestions are often mitigated using redispatch actions, which entail adjusting the power output of various controllable generators in the network. However, these actions are costly and may not fully resolve all issues. Adaptively changing the network using topological actions, such as line switching and bus switching, is an under-utilized yet very cost-effective strategy for network operators facing rapidly shifting energy patterns and contingencies. To navigate the complex and large combinatorial space of all topological actions, we propose a Hierarchical Multi-Agent Reinforcement Learning (MARL) framework in our paper "Multi-Agent Reinforcement Learning for Power Grid Topology Optimization" [1] (a preprint submitted to PSCC 2024).