A two-level load-utility balancing model for multi-source smart grids based on multi-agent reinforcement learning algorithm

In smart grids, the interactions among diverse participants significantly affect system efficiency and social welfare. Considering that the real-time electricity pricing (RTP) mechanism under traditional social welfare maximization fails to account for the independent interests of various entities in the power system, a two-level load-utility balancing model for real-time pricing in smart grids is proposed in this paper, where the welfare of the demand side and the multi-energy supply side is collaboratively optimized and balanced. Furthermore, a multi-agent reinforcement learning (MARL) algorithm based on the centralized training and decentralized execution (CTDE) framework is designed for this model, and a multi-agent electricity market environment is constructed accordingly, comprising users, an aggregated power supplier, and a power market scheduling center (PMSC). The user agent is modeled with a heterogeneous utility function, the supplier agent is modeled with a profit function coordinating both traditional and renewable energy, while the PMSC agent is responsible for real-time pricing and cross-agent welfare balance coordination. Finally, simulation results show the effectiveness of the proposed model and algorithm in achieving welfare balance between the supplier and users. Compared with the pricing scheme without a welfare-balancing mechanism, the proposed model reduces the welfare gap between the supplier and users by approximately 46.9%. Compared with the non-dominated sorting genetic algorithm II (NSGA-II), the proposed method can achieve a comparable level of total social welfare.