  As electric vehicles (EV) become more prevalent and advances in electric
vehicle electronics continue, vehicle-to-grid (V2G) techniques and large-scale
scheduling strategies are increasingly important to promote renewable energy
utilization and enhance the stability of the power grid. This study proposes a
hierarchical multistakeholder V2G coordination strategy based on safe
multi-agent constrained deep reinforcement learning (MCDRL) and the
Proof-of-Stake algorithm to optimize benefits for all stakeholders, including
the distribution system operator (DSO), electric vehicle aggregators (EVAs) and
EV users. For DSO, the strategy addresses load fluctuations and the integration
of renewable energy. For EVAs, energy constraints and charging costs are
considered. The three critical parameters of battery conditioning, state of
charge (SOC), state of power (SOP), and state of health (SOH), are crucial to
the participation of EVs in V2G. Hierarchical multi-stakeholder V2G
coordination significantly enhances the integration of renewable energy,
mitigates load fluctuations, meets the energy demands of the EVAs, and reduces
charging costs and battery degradation simultaneously.
