Multi-agent reinforcement mechanism design for dynamic pricing-based demand response in charging network

Dynamic pricing for the charging network needs to consider the interdependency of charging stations' decision -making, and acquire user's demand information and their price sensitivity on demands. Most existing pricing schemes assume users consistently follow stable and inferrable patterns, while it remains crucial to explore how they may be influenced by historic prices in a charging market, their private utility functions, and the best demand in response. To integrate the strategic user-station interactions and uncertainties into the network dynamic pricing, this paper proposes a multi-agent reinforcement mechanism design framework to simultaneously determine the optimal charging prices for multiple charging stations over a period considering power output limits, unexpected arriving requests, and undetermined charging demands of self-interested users who aim to maximize their own utility. Specifically, the station-user interaction is modelled as a mechanism design problem, and station-station cooperation is captured by the Markov game and solved by multi-agent deep deterministic policy gradient. The objective is to maximize the long-term network revenue considering the social welfare of all users. We evaluate our framework through an experimental study, and the results demonstrate that our approach outperforms the non-cooperative deep deterministic policy gradient algorithm and time-of-use pricing scheme.