含多座抽水蓄能电站的省级电网日前计划分层递进优化算法

doi:10.11930/j.issn.1004-9649.202212044

中国电力 ›› 2023, Vol. 56 ›› Issue (5): 41-50.DOI: 10.11930/j.issn.1004-9649.202212044

• 双碳目标下的新型电力系统 • 上一篇下一篇

含多座抽水蓄能电站的省级电网日前计划分层递进优化算法

周云海¹, 张智颖¹, 徐飞², 郭琦³, 刘连德⁴, 贾倩¹

1. 三峡大学电气与新能源学院, 湖北宜昌 443002;
2. 清华大学电机工程与应用电子技术系, 北京 100084;
3. 内蒙古电力(集团)有限公司, 内蒙古呼和浩特 010010;
4. 呼和浩特抽水蓄能发电有限责任公司, 内蒙古呼和浩特 010060

收稿日期:2022-12-12 修回日期:2023-02-02 出版日期:2023-05-28 发布日期:2023-05-27
作者简介:周云海(1972-),男,博士,教授,从事新能源电力系统调度运行技术研究,E-mail:zhouyunhai@ctgu.edu.cn;张智颖(1997-),男,通信作者,硕士研究生,从事电力系统运行与控制技术研究,E-mail:837238136@qq.com

Hierarchical Progressive Optimization Algorithm for Day-Ahead Planning of Provincial Power Grid with Multiple Pumped Storage Power Stations

ZHOU Yunhai¹, ZHANG Zhiying¹, XU Fei², GUO Qi³, LIU Liande⁴, JIA Qian¹

1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China;
2. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;
3. Inner Mongolia Power (Group) Co., Ltd., Hohhot 010010, China;
4. Inner Mongolia Hohhot Pumped-Storage Power Generation Co., Ltd., Hohhot 010060, China

Received:2022-12-12 Revised:2023-02-02 Online:2023-05-28 Published:2023-05-27

摘要/Abstract

摘要： 考虑抽蓄机组启停优化的省级电网日前计划可以表述为一个大规模整数线性混合规划问题，直接求解困难。将影响新能源消纳的主要因素—网络约束和系统调峰能力约束解耦，构建日前调度优化的分层递进模型，将原问题转换成大规模线性规划和小规模整数线性规划求解。模型第一层不考虑抽蓄机组启停优化，将抽蓄电站出力松弛为连续变量，以切负荷、弃风弃光、机组运行费用总和最小为目标，对系统网络约束精确建模，初步得到系统中各机组96点出力曲线；第二层考虑抽蓄启停优化，新能源机组出力以第一层初步结果为其最大可能出力，风、光、火电、常规水电机组各等值为一台机组，不考虑网络约束，得到优化后的抽蓄机组启停组合；最后将确定的抽蓄机组启停组合代回第一层进行回代修正，最终计算出各台机组的96点出力曲线。所提模型已应用到某实际省级电网中，以实际运行数据验证了模型的正确性和实用性。

关键词: 抽水蓄能, 日前计划, 分层优化, 协调优化调度, 混合整数线性规划

Abstract: The day-ahead planning of the provincial power grid considering the optimization of pumped-storage unit commitment can be expressed as a large-scale mixed-integer linear programming problem, which is difficult to solve directly. Hence, the main factors affecting the consumption of new energy, namely, network constraints and the system’s peak-shaving capacity constraints, are decoupled, and a hierarchical progressive model for day-ahead scheduling optimization is constructed. The model converts the original problem into large-scale linear programming and small-scale integer linear programming. The first layer of the model does not consider the optimization of the pumped-storage unit commitment. The output of the pumped storage power station is relaxed as a continuous variable, and the system network constraints are accurately modeled to minimize the sum of load shedding, wind and photovoltaic power discarding, and unit operating costs. Then, 96-point output curves of each unit in the system are preliminarily obtained. The second layer considers the optimization of the pumped-storage unit commitment. The maximum possible output of new energy units is based on the preliminary results of the first layer. The wind, photovoltaic, thermal power and conventional hydropower units are all equivalent to one unit separately. The optimized pumped-storage unit commitment is obtained without the consideration of network constraints. Finally, the determined pumped-storage unit commitment is brought back to the first layer for back substitution correction, and the 96-point output curve of each unit is calculated. The proposed model has been applied to a provincial power grid, and the correctness and practicability are verified by the actual operation data.

Key words: pumped storage, day-ahead plan, hierarchical optimization, coordinate and optimize dispatching, mixed-integer linear programming

周云海, 张智颖, 徐飞, 郭琦, 刘连德, 贾倩. 含多座抽水蓄能电站的省级电网日前计划分层递进优化算法[J]. 中国电力, 2023, 56(5): 41-50.

ZHOU Yunhai, ZHANG Zhiying, XU Fei, GUO Qi, LIU Liande, JIA Qian. Hierarchical Progressive Optimization Algorithm for Day-Ahead Planning of Provincial Power Grid with Multiple Pumped Storage Power Stations[J]. Electric Power, 2023, 56(5): 41-50.

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含多座抽水蓄能电站的省级电网日前计划分层递进优化算法

Hierarchical Progressive Optimization Algorithm for Day-Ahead Planning of Provincial Power Grid with Multiple Pumped Storage Power Stations

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含多座抽水蓄能电站的省级电网日前计划分层递进优化算法

Hierarchical Progressive Optimization Algorithm for Day-Ahead Planning of Provincial Power Grid with Multiple Pumped Storage Power Stations

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