Enhancing Resilience of Power-Transportation Coupled Networks Through Optimal Scheduling of Multi-Type Mobile Emergency Resources

Extreme events with high impact and low probability pose a serious threat to the safe operation of power and transportation networks. To effectively enhance the resilience of power-transportation coupled networks, this paper proposes a scheduling optimization strategy for multi-type mobile emergency resources based on a hybrid data-model driven approach. Firstly, considering the influence of power line outages, traffic road faults and dynamic changes of traffic flow distribution on the strategies of mobile energy storage system (MESS), line repair crew (LRC) and road repair crew (RRC), a stochastic optimization scheduling model of multi-type mobile emergency resources to minimize the total loss cost of the power-transportation coupled networks is constructed. Secondly, this paper proposes a hybrid data-model driven approach to solve the complex nonlinear stochastic optimization model. In the data-driven part, a novel graph diffusion attention network multi-agent reinforcement learning algorithm is proposed to solve the mobile emergency resources routing strategy. In the model-driven part, this paper converts the power system model and the multi-type mobile emergency resources scheduling model into a mixed integer second-order cone programming model to solve the mobile emergency resources optimal scheduling strategy. Finally, the effectiveness of the proposed method is validated in the modified IEEE 33-bus power network and 12-bus traffic network.