With the growing prevalence of electric vehicles (EVs) and advancements in EV electronics, vehicle-to-grid (V2G) techniques and large-scale scheduling strategies have emerged to promote renewable energy utilization and power grid stability. This study proposes a multi-stakeholder hierarchical V2G coordination based on deep reinforcement learning (DRL) and the Proof of Stake algorithm. Furthermore, the multi-stakeholders include the power grid, EV aggregators (EVAs), and users, and the proposed strategy can achieve multi-stakeholder benefits. On the grid side, load fluctuations and renewable energy consumption are considered, while on the EVA side, energy constraints and charging costs are considered. The three critical battery conditioning parameters of battery SOX are considered on the user side, including state of charge, state of power, and state of health. Compared with four typical baselines, the multi-stakeholder hierarchical coordination strategy can enhance renewable energy consumption, mitigate load fluctuations, meet the energy demands of EVA, and reduce charging costs and battery degradation under realistic operating conditions.

翻译：随着电动汽车的日益普及及车载电子技术的进步，车辆到电网技术与大规模调度策略应运而生，以促进可再生能源利用和电网稳定。本研究提出一种基于深度强化学习和权益证明算法的多利益相关方分层V2G协调策略。多利益相关方包括电网、电动汽车聚合商和用户，该策略可保障多方利益。在电网侧，考虑负荷波动与可再生能源消纳；在聚合商侧，考虑能量约束与充电成本；在用户侧，考虑电池SOX的三个关键调节参数，即荷电状态、功率状态和健康状态。与四种典型基准方法相比，该多利益相关方分层协调策略在实际运行条件下能够增强可再生能源消纳能力、缓解负荷波动、满足聚合商能量需求，并降低充电成本与电池退化程度。