This study proposes a hierarchical multistakeholder vehicle-to-grid (V2G) coordination strategy that addresses the challenges surrounding renewable energy utilization, grid stability, and the optimization of benefits for all stakeholders involved. The strategy is based on safe multi-agent constrained deep reinforcement learning (MCDRL) and the Proof-of-Stake algorithm.
One of the key stakeholders in this strategy is the distribution system operator (DSO). The DSO’s primary concern is load fluctuations and the integration of renewable energy into the grid. With the increasing adoption of electric vehicles, the demand for electricity is expected to surge. By implementing the proposed strategy, the DSO can better manage these load fluctuations and leverage the flexibility offered by EVs to integrate more renewable energy into the grid.
Electric vehicle aggregators (EVAs) are another vital stakeholder in this coordination strategy. EVAs face challenges related to energy constraints and charging costs. By participating in the V2G system, EVAs can efficiently manage the energy demands of electric vehicles under their aggregation and optimize charging schedules to minimize costs.
In order for electric vehicles to participate in V2G, three critical parameters must be considered: battery conditioning, state of charge (SOC), state of power (SOP), and state of health (SOH). These parameters play a crucial role in the performance and lifespan of the EV’s battery. By considering these parameters in the coordination strategy, the study ensures that the participation of electric vehicles in V2G is sustainable and minimizes battery degradation.
The proposed hierarchical multistakeholder V2G coordination strategy offers several benefits. Firstly, it significantly enhances the integration of renewable energy into the power grid, thereby reducing reliance on conventional fossil fuels and contributing to a more sustainable energy mix. Secondly, it mitigates load fluctuations, making the power grid more resilient and reliable. Thirdly, it meets the energy demands of the EVAs, ensuring a stable and cost-efficient operation of their electric vehicle fleets. Lastly, by optimizing charging schedules and considering battery conditioning, SOC, SOP, and SOH, the strategy reduces charging costs and minimizes battery degradation, promoting the long-term viability of V2G systems.
In conclusion, the proposed hierarchical multistakeholder V2G coordination strategy based on safe multi-agent constrained deep reinforcement learning and the Proof-of-Stake algorithm is a promising approach to optimize the benefits for all stakeholders in the electric vehicle ecosystem. By addressing the challenges associated with renewable energy utilization, load fluctuations, energy constraints, and battery degradation, this strategy paves the way for a more sustainable and efficient integration of electric vehicles into the power grid.