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

TL;DR

This paper introduces a deep reinforcement learning-based hierarchical V2G coordination method that optimizes multi-stakeholder benefits, including grid stability, renewable energy use, and user costs, in electric vehicle integration.

Contribution

It proposes a novel multi-stakeholder hierarchical V2G coordination framework combining DRL and Proof of Stake, addressing grid, EVA, and user needs simultaneously.

Findings

Enhanced renewable energy consumption compared to baselines

Reduced load fluctuations and charging costs

Mitigated battery degradation under realistic conditions

Abstract

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.