Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information

no code implementations • 29 Jan 2024 • Jinhao Li, Ruichang Zhang, Hao Wang, Zhi Liu, Hongyang Lai, Yanru Zhang

Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization.

Decision Making

Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information

no code implementations • 22 Dec 2021 • Jinhao Li, Ruichang Zhang, Hao Wang, Zhi Liu, Hongyang Lai, Yanru Zhang

Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal operation, i. e., optimal power flow (OPF), of power systems with a high uptake of RERs.

reinforcement-learning Reinforcement Learning (RL)