Deep Reinforcement Learning-Based Battery Conditioning Hierarchical V2G
  Coordination for Multi-Stakeholder Benefits
Yubao Zhang Xin Chen Yi Gu Zhicheng Li Wu Kai
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.