Cooperative multi-agent deep reinforcement learning based decentralized framework for dynamic renewable hosting capacity assessment in distribution grids

Abstract

Renewable hosting capacity (RHC) means the total renewable power that can be integrated into the power grid without violation of network constraints. In this paper, a cooperative multi-agent deep reinforcement learning (CMADRL) based decentralized method is proposed to assess the dynamic renewable hosting capacity (RHC) of distribution grids, aiming to duly make decisions for renewable energy interconnection requests and ensure consistent power grids reliability simultaneously. According to the time-varying load-generation operation conditions, the proposed CMADRL method can continuously derive multi-timescale operation strategies for volt-var control devices, e.g., static var compensators (SVCs) and on-load tap changer (OLTC), improving the RHC of distribution grids. With three independent agents (SVC, OLTC and renewable agents), the proposed CMADRL method follows the manner of centralized training and decentralized execution, which guarantees the algorithm convergence under time-varying load-generation uncertainties and meanwhile ensures the feasibility of online applications. The case studies are carried out on a modified IEEE 37-node distribution system to demonstrate the effectiveness of the proposed real-time RHC assessment method. Numerical results verify that the proposed CMADRL method has a better performance than conventional optimization methods on computation efficiency.