AUTHOR=Sun Lixia , Tian Yiyun , Wu Yichao , Huang Wenzhe , Yan Chenhui , Jin Yuqing 

TITLE=Strategy optimization of emergency frequency control based on new load with time delay characteristics

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1065405

DOI=10.3389/fenrg.2022.1065405

ISSN=2296-598X

ABSTRACT=With the development of modern communication technology and the large number of new controllable loads connected to the power grid, the new controllable loads with flexible regulation characteristics can participate in the emergency frequency stability control. However, the communication state differences and spatial distribution characteristics will affect the actual effect of frequency control. In this paper, an emergency frequency control method based on deep reinforcement learning is proposed in order to consider controllable load shedding response time. The method evaluates response ability for emergency control of controlled loads by load response time, controllable load amount and controllable load buses. Then, the controllable load with smaller response time is cut out first to ensure rapid control, and the Markov Decision Process (MDP) is used to model the emergency frequency control problem. Finally, Rainbow algorithm of Deep Reinforcement Learning (DRL) is used to optimize the emergency frequency stability control strategy involving controllable load resources. The formation of emergency load shedding instruction is directly driven by high-dimensional operation state data after power grid failure, so that, the aim of minimizing the economic cost while keeping the system frequency stable is achieved. The effectiveness of the proposed method is verified in the IEEE 39-bus system.