no code implementations • 23 Dec 2020 • Xiren Zhou, Siqi Wang, Ruisheng Diao, Desong Bian, Jiahui Duan, Di Shi
Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain. In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering. To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process.
no code implementations • 11 Dec 2020 • Ruisheng Diao, Di Shi, Bei Zhang, Siqi Wang, Haifeng Li, Chunlei Xu, Tu Lan, Desong Bian, Jiajun Duan
Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid.
Optimization and Control Systems and Control Systems and Control