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风水储联合运行双层滚动优化调度方法

阮宏华 邓子琦 陈飞雄 林俊杰

中国电力2026,Vol.59Issue(4):1-11,11.
中国电力2026,Vol.59Issue(4):1-11,11.DOI:10.11930/j.issn.1004-9649.202504088

风水储联合运行双层滚动优化调度方法

Double layer rolling based optimization model for joint operation of wind-hydro-storage system

阮宏华 1邓子琦 1陈飞雄 2林俊杰2

作者信息

  • 1. 福建华电电力工程有限公司,福建 福州 350000
  • 2. 福州大学 电气工程与自动化学院,福建 福州 350000
  • 折叠

摘要

Abstract

With the gradual increase in the penetration rate of wind power and other renewable energy sources,the uncertainty of power grids operation has intensified accordingly.How to safely and efficiently exert the regulating effect of hydropower is of great significance to the stable operation of power grids.To this end,a bi-level rolling optimal scheduling method for the joint operation of wind-hydro-storage systems is proposed.First,a bi-level rolling optimal control model for wind-hydro-storage joint operation is constructed.The upper-level optimization is based on a long time scale,with the goal of minimizing the system operation cost;the lower-level optimization is based on a short time scale,aiming to minimize the system output deviation.Second,the model predictive control(MPC)method is adopted to solve the optimization model.The real-time rolling optimization of the lower level is used to feedback and correct the upper-level scheduling plan,thereby reducing the impact of uncertainty on the system.Finally,a case study is carried out on a cascade hydropower system in South China.The simulation results verify the effectiveness of the proposed method in improving energy utilization efficiency,reducing operation costs and addressing system uncertainty.

关键词

可再生能源/不确定性/梯级水电站/模型预测控制/双层滚动

Key words

renewable energy/uncertainty/cascade hydro-power stations/model predictive control/two-layer rolling

引用本文复制引用

阮宏华,邓子琦,陈飞雄,林俊杰..风水储联合运行双层滚动优化调度方法[J].中国电力,2026,59(4):1-11,11.

基金项目

This work is supported by National Natural Science Foundation of China(No.52107080),Natural Science Foundation of Fujian Province(No.2021J05135). 国家自然科学基金资助项目(52107080) (No.52107080)

福建省自然科学基金资助项目(2021J05135). (2021J05135)

中国电力

1004-9649

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