Multi-agent deep reinforcement learning with online and fair optimal dispatch of EV aggregators

The growing popularity of electric vehicles (EVs) and the unpredictable behavior of EV owners have attracted attention to real-time coordination of EVs charging management. This paper presents a hierarchical structure for charging management of EVs by integrating fairness and efficiency concepts within the operations of the distribution system operator (DSO) while utilizing a multi-agent deep reinforcement learning (MADRL) framework to tackle the complexities of energy purchasing and distribution among EV aggregators (EVAs). At the upper level, DSO calculates the maximum allowable power for each EVA based on power flow constraints to ensure grid safety. Then, it finds the optimal efficiency-jain tradeoff (EJT) point, where it sells the highest energy amount while ensuring equitable energy distribution. At the lower level, initially, each EVA acts as an agent employing a double deep Q-network (DDQN) with adaptive learning rates and prioritized experience replay to determine optimal energy purchases from the DSO. Then, the real-time smart dispatch (RSD) controller prioritizes EVs for energy dispatch based on relevant EVs information. Findings indicate the proposed enhanced DDQN outperforms deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) in cumulative rewards and convergence speed. Finally, the framework’s performance is evaluated against uncontrolled charging and the first come first serve (FCFS) scenario using the 118-bus distribution system, demonstrating superior performance in maintaining safe operation of the grid while reducing charging costs for EVAs. Additionally, the framework’s integration with renewable energy sources (RESs), such as photovoltaic (PV), demonstrates its potential to enhance grid reliability. • Introduces a scalable MADRL framework for real-time EV charging and energy distribution. • Ensures fairness via an Efficiency-Jain Tradeoff (EJT) strategy at the DSO level. • Enhances agent convergence with DDQN using adaptive learning rates and prioritized replay. • Preserves stakeholder privacy with decentralized control and minimal data sharing. • Balances grid reliability with equitable energy allocation under dynamic uncertainties.