A Two‐Stage Strategy for Black‐Start Restoration by Coordinating Deep Reinforcement Learning and Mixed‐Integer Linear Programming
Year: 2025
Type: article
Abstract: ABSTRACT Extreme events can lead to extensive blackouts, emphasising the crucial significance of power system black‐start restoration. In this paper, a two‐stage strategy coordinating deep reinforcement learning (DRL) and mixed‐integer linear programming (MILP) for power system black‐start restoration is proposed. In the first stage, a convolutional neural network‐based (CNN‐based) invalid action masking assisted proximal policy optimisation (CI‐PPO) algorithm is developed to determine component connections. Furthermore, a MILP model for optimal power dispatch is formulated with the objective of minimising generation costs in the second stage. The effectiveness of the proposed method is validated using the New England 10‐unit 39‐bus power system. The results show strong learning ability and computational efficiency for varying system conditions.
Source: IET Renewable Power Generation
DOI: 10.1049/rpg2.70081
Authors: Lingyu Liang, Huanming Zhang, Wencong Xiao, Xiangyu Zhao, Junbin Chen, Shang Cao, Tao Yu, Hanju Li
Institution: South China University of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,500
Awards: G7758814565, G8702543137
OpenAlex ID: W4411432745