Exploration-enhanced multi-agent reinforcement learning for distributed PV-ESS scheduling with incomplete data

This paper investigates the scheduling problem in smart distribution networks equipped with distributed photovoltaic energy storage systems (PV-ESS) to address excessive power losses, economic revenue, and over-voltage issues. Accurately modeling the grid structure and ensuring adequate sensor coverage pose significant challenges in network settings of this nature, and therefore, we put forth a novel approach known as Principal Component Analysis-based incomplete data equivalence (PIDE) for constructing a data-driven power flow model under incomplete data. Moreover, the presence of distributed PV-ESS, coupled with the lack of data sharing, introduces a hybrid cooperation-competition dynamic, resulting in suboptimal solutions and local optima. To address this challenge, we approach the scheduling problem by formulating it as a multi-agent reinforcement learning task. Meanwhile, we present Counterfactual Multi-agent Soft Actor–Critic (COSAC), which incorporates stochastic policy learning to enhance exploration and facilitates credit assignment in the continuous action space, so as to accurately determine the individual contributions of agents involved in the task. Simulation results conducted on the IEEE 33 and 123 bus systems demonstrate the effectiveness of the proposed method. Specifically, we find that PIDE achieves a substantial reduction in the necessary data sampling coverage, and COSAC outperforms state-of-the-art multi-agent reinforcement learning methods by at least 4.14%.