Intelligent and rapid event-based load shedding pre-determination for large-scale power systems: Knowledge-enhanced parallel branching dueling Q-network approach

With the increasing penetration of renewable energy, power system instability factors are rising. Transient voltage instability is a common power system problem that can cause blackouts and severe economic losses. An important measure to ensure transient voltage stability during emergencies is event-based load shedding (ELS). However, formulating ELS measures by experts gradually becomes inadaptable and time-consuming currently. With the increased complexity and uncertainty of modern new power systems, there is an urgent need for more intelligent and rapid ELS. This paper proposes a knowledge-enhanced parallel branching dueling Q-network (BDQ) framework for intelligent and rapid ELS against transient voltage instability. Firstly, an event-based Markov decision process (MDP) that differs from the conventional response-based MDP is established, which can effectively guide the training process. Secondly, to condense the huge conventional exponential decision space, a multi-branch BDQ structure is designed, which has higher training effectiveness and decision capability compared to branchless agents. Then, the domain proprietary knowledge that low-voltage buses are prioritized for ELS is incorporated into the BDQ agent. In comparison with a purely data-driven BDQ approach, incorporating knowledge can significantly enhance both training effectiveness and decision capability. Next, to further improve the applicability in large-scale real power systems, the parallel BDQ is proposed. Finally, the advantages of the proposed approach are demonstrated in the China Electric Power Research Institute 36-bus system and the Western Electricity Coordinating Council 179-bus system.