1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China;2.Southern Power Grid New Energy System (Beijing) Research Institute Co., Ltd., Beijing 102218, China;3.Power Dispatch and Control Center of China Southern Power Grid, Guangzhou 510663, China

To address the electric power balance issues in systems caused by intraday power deviations of renewable energies, power dispatch departments require minute-level revisions to the day-ahead plans from a look-ahead perspective. Therefore, a robust decision-making method for the look-ahead dispatch in power system is proposed based on the fast identification of redundant constraints. The method improves the renewable energy accommodation rate and supply capacity of the power system in extreme operation scenarios while ensuring efficient solving in a short time. First, in the look-ahead dispatch decision-making mode, a two-stage optimization model is constructed considering the uncertainty of wind and photovoltaic power, along with the coordinated control of multiple resources such as hydro, thermal, storage, and tie-lines. The first stage considers the economy and safety of the system, while the second stage considers the renewable energy accommodation and power supply guarantee. Furthermore, considering the model complexity added by the uncertainty of renewable energy, an identification and reduction method for redundant constraints based on graph convolution neural network and long short-term memory network is proposed, followed by a robust optimization solution for the reduced look-ahead dispatch model using column and constraint generation algorithm. Finally, the effectiveness of the proposed model for the fast identification of redundant constraints is validated through numerical results in the IEEE 118-bus system, and the proposed robust decision-making method can efficiently solve look-ahead dispatch strategies in various types of scenarios.