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With the growing prevalence of electric vehicles (EVs) and advancements in EVelectronics, vehicle-to-grid (V2G) techniques and large-scale schedulingstrategies have emerged to promote renewable energy utilization and power gridstability. This study proposes a multi-stakeholder hierarchical V2Gcoordination based on deep reinforcement learning (DRL) and the Proof of Stakealgorithm. Furthermore, the multi-stakeholders include the power grid, EVaggregators (EVAs), and users, and the proposed strategy can achievemulti-stakeholder benefits. On the grid side, load fluctuations and renewableenergy consumption are considered, while on the EVA side, energy constraintsand charging costs are considered. The three critical battery conditioningparameters of battery SOX are considered on the user side, including state ofcharge, state of power, and state of health. Compared with four typicalbaselines, the multi-stakeholder hierarchical coordination strategy can enhancerenewable energy consumption, mitigate load fluctuations, meet the energydemands of EVA, and reduce charging costs and battery degradation underrealistic operating conditions.

Deep Reinforcement Learning-Based Battery Conditioning Hierarchical V2G Coordination for Multi-Stakeholder Benefits