Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions

With the proliferation of electric vehicles (EVs), the EV aggregator (EVA) operating multiple EV charging stations, has emerged to provide charging services to EV users by setting differentiated charging prices to respond to time-varying electricity prices. Existing studies on the profitable strategy of EVA mostly focus on the formulation of bidding or pricing schemes or the optimization of single time-period coordination, failing to fully combine these sequential strategies and exploit the spatial-temporal shifting characteristics of EVs. To address this bottleneck, a novel multi-period joint bidding and pricing strategy for EVA considering the interactions with distribution system operator (DSO) and EV users is formulated. Firstly, a stochastic bi-level bidding and pricing joint optimization model is established, where the market clearing process considering baseline load uncertainties is simulated at the lower level. Secondly, to estimate the dynamic charging behaviors of EV users under differentiated charging prices, a robust semi-dynamic traffic assignment (SDTA) model is constructed to derive EV charging loads considering their coupling effect under traffic restrictions. Finally, an iterative method based on fixed-point theory is designed to obtain the optimal bidding and pricing strategy of EVA, which derives the equilibrium solution by sequentially solving the stochastic bi-level bidding and pricing subproblem and the robust SDTA subproblem. Numerical results verify the effectiveness of the proposed method in obtaining a profitable and stable strategy of EVA when embedded with the practical decision-making process of DSO and EV users.