An optimization scheduling method for green power self-contained systems in remote areas of China
YANG Zhenchen1, ZHAO Tianyu2, HOU Yongqing3, CHEN Chong2, JIA Limin2,4
1. Shanghai Electric Power Co., Ltd, Shanghai 201200; 2. China Institute of Energy and Transportation Integrated Development, North China Electric Power University, Beijing 102206; 3. Beijing Nego Automation Technology Co., Ltd, Beijing 100044; 4. State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044
Abstract：To address the issue of optimization in green power self-contained energy systems in remote areas of China, an optimization scheduling method oriented towards economic objectives is proposed, based on weak grids’ varying power supply capabilities. First, energy units such as wind, solar, and energy storage are coupled to construct an energy unit model, and a self-contained system topology and its operation modes are introduced. Then, the optimization objective is set to minimize day-ahead operation costs and daily self-contained rate is taken as the evaluation objective. The Gurobi toolbox is used for optimization. Finally, using measured data from a station on the Qinghai-Tibet Railway and considering diesel price fluctuations and renewable energy output volatility, the operational costs and power generation patterns of energy units in the self-contained system are analyzed and validated across four weak-grid scenarios. The scheduling results under various scenarios shows that as the power supply reliability decreases, both operating costs and system self-contained rate increase. Minor diesel price fluctuations only impact the operational costs under Scenarios 3 and 4, without affecting system self-contained rates across all scenarios. Declines in renewable energy output negatively impact self-contained rates in all four scenarios.
杨振辰, 赵天宇, 侯永清, 陈冲, 贾利民. 中国偏远区域绿电自洽系统优化调度方法[J]. 电气技术, 2025, 26(12): 1-8.
YANG Zhenchen, ZHAO Tianyu, HOU Yongqing, CHEN Chong, JIA Limin. An optimization scheduling method for green power self-contained systems in remote areas of China. Electrical Engineering, 2025, 26(12): 1-8.
2025, Vol. 26 
