SMA-PDPPO: Safe Multiagent Primal-Dual Deep Reinforcement Learning for Industrial Parks Energy Trading

Energy trading in industrial parks has great potential for reducing carbon emissions and lowering energy bills. This article proposes a safe multiagent deep reinforcement learning algorithm for optimizing the energy trading strategy in industrial parks to achieve less reliance on the main grid and save energy costs. Specifically, an industrial park that contains multiple industrial users with both thermal and electrical load requirements is considered, in which the different users can trade energy with each other and with the main grid based on their own strategies. Unlike the existing studies, the time-phased energy trading problem is transformed into a constrained partially observable Markov game, which models the industrial users and objectives of the buyers and sellers. Finally, a novel multiagent primal-dual proximal policy optimization algorithm that guarantees safety is developed to achieve the optimal trading strategies between the main grid and multiple users. Numerical simulations with real-world data demonstrate that the proposed algorithm allows higher total revenue for sellers and lower total costs for buyers in the park, limits each user's bid or offer to a relatively safe range, and increases the amount of electricity traded locally, while reducing trading with the grid.