15 citations · 26 across the 8 of their papers we have counts for
11 papers
Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents
Xiumin Shang, Jinping Yang, Bingquan Zhu +5
Planning future operational scenarios of bulk power systems that meet security and economic constraints typically requires intensive labor efforts in performing massive simulations…
Rethink AI-based Power Grid Control: Diving Into Algorithm Design
Xiren Zhou, Siqi Wang, Ruisheng Diao +3
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 a…
On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control
Ruisheng Diao, Di Shi, Bei Zhang +6
Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid. This paper presen…
Evaluating Load Models and Their Impacts on Power Transfer Limits
Xinan Wang, Yishen Wang, Di Shi +4
Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comp…
Deriving AC OPF Solutions via Proximal Policy Optimization for Secure and Economic Grid Operation
Yuhao Zhou, Bei Zhang, Chunlei Xu +5
Optimal power flow (OPF) is a very fundamental but vital optimization problem in the power system, which aims at solving a specific objective function (ex.: generator costs) while…
AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs
Tu Lan, Jiajun Duan, Bei Zhang +4
This paper presents a novel AI-based approach for maximizing time-series available transfer capabilities (ATCs) via autonomous topology control considering various practical constr…