Private Electric Vehicles for Emergency Load Pickup: A Multi-Network Stochastic Equilibrium Approach

Abstract

Large-scale electric vehicle (EV) adoption has the potential to offer flexible and distributed energy resources to support the power distribution system (DS) during emergent incidents. We developed a network-based multiagent stochastic optimization model with equilibrium constraints (N-MSOPEC) to investigate the potential value of EVs on the emergency load pickup in DSs, considering the uncertainties of line outage and EV participation. Decentralized stakeholders from both transportation and DSs, including the distribution system operator, distributed generator owners, charging station aggregator, and EV drivers, were modeled to reflect realistic decentralized decision making. EV participation was incentivized in a market equilibrium framework for DS support. An exact convex reformulation technique was additionally developed for the proposed N-MSOPEC model, which significantly improved the computational efficiency required to solve high-dimensional complementarity problems. Simulation results on coupled transportation and distribution test systems showed how EV participation could reduce load loss under different line outages and EV adoption scenarios, and demonstrated the effectiveness of our model in capturing the interdependencies of the coupled systems. The DS characteristics and load pickup needs influenced the incentives provided to EV drivers, resulting in different charging station selections and load pickup patterns. We also investigated the value of stochastic modeling in the multiagent framework by calculating metrics such as the value of stochastic solution and the expected value of perfect information for the stakeholders involved.