Year: 2022
Type: conference-paper
Abstract: With widespread deployment of renewables, the electric power grids are experiencing increasing dynamics and uncertainties, with its secure operation being threatened. Existing frequency control schemes based on day-ahead offline analysis and minute-level online sensitivity calculations are difficult to adapt to rapidly changing system states. In particular, they are unable to facilitate coordinated control of system frequency and power flows. A refined approach and tools are urgently needed to assist system operators to make timely decisions. This paper proposes a novel model-free coordinated frequency control framework based on safe reinforcement learning, with multiple control objectives considered. The load frequency control problem is modeled as a constrained Markov decision process, which can be solved by an AI agent continuously interacting with the grid to achieve subsecond decision making. Extensive numerical experiments conducted at East China Power Grid demonstrate the effectiveness and promise of the proposed method.
Authors: Yi Zhou, Liangcai Zhou, Di Shi, Xiaoying Zhao
Institution: State Grid Corporation of China (China)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4312945708