Collaborative participation of wind power producer and charging station aggregator in electricity markets

The widespread adoption of electric vehicles (EVs) is hindered by two major challenges: limited fast-charging infrastructure and reliance on fossil-fuel-based electricity. Expanding fast-charging stations (FCSs) requires optimal scheduling, which is complicated by the stochastic behavior of EV users. Additionally, rapid fluctuations in renewable power availability, typically mitigated by fossil-fuel generation, can limit EVs’ environmental benefits. This paper addresses these challenges through the coordinated operation of a wind power producer (WPP) and an FCS aggregator, aiming to optimize the revenue of both parties while considering EV battery degradation and FCS charging limits. The problem is formulated as a bi-level optimization problem: the WPP and FCS aggregator maximize their own profits, linked via a peer-to-peer (P2P) energy trading agreement. It is then cast within a Lyapunov optimization framework, decomposing the problem into single-step subproblems, which reduces the impact of EV charging uncertainty. Collaboration with the aggregator decreases WPP’s imbalance by an average of 45.77 % in a case study, while the P2P energy trading increases the renewable share of power delivered to EVs by 11.17 % on average. Furthermore, a reinforcement learning agent is trained to improve FCS energy storage utilization. Simulation results show that the proposed approach can reduce daily FCS operating costs by up to 58 % and increase daily WPP profit by up to 31 %.