Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning
This work addresses cascading outage risks in power transmission systems, which is a domain-specific problem for grid operators, but it is incremental as it builds on existing influence graph methods.
The paper tackles the problem of real-time cascade mitigation in power systems with renewable energy by extending an influence graph into a Markov decision process model solved with reinforcement learning, showing that proactive line disconnections reduce cascading risk, with validation on IEEE 14-bus and 118-bus systems.
Despite high reliability, modern power systems with growing renewable penetration face an increasing risk of cascading outages. Real-time cascade mitigation requires fast, complex operational decisions under uncertainty. In this work, we extend the influence graph into a Markov decision process model (MDP) for real-time mitigation of cascading outages in power transmission systems, accounting for uncertainties in generation, load, and initial contingencies. The MDP includes a do-nothing action to allow for conservative decision-making and is solved using reinforcement learning. We present a policy gradient learning algorithm initialized with a policy corresponding to the unmitigated case and designed to handle invalid actions. The proposed learning method converges faster than the conventional algorithm. Through careful reward design, we learn a policy that takes conservative actions without deteriorating system conditions. The model is validated on the IEEE 14-bus and IEEE 118-bus systems. The results show that proactive line disconnections can effectively reduce cascading risk, and certain lines consistently emerge as critical in mitigating cascade propagation.