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

RUAN Honghua; DENG Ziqi; CHEN Feixiong; LIN Junjie

doi:10.11930/j.issn.1004-9649.202504088

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

更新时间：2026-04-30

- Double layer rolling based optimization model for joint operation of wind-hydro- storage system
- Vol. 59, Issue 4, Pages: 1-11(2026)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202504088
  CLC：
- Online First：20 April 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
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.

RUAN Honghua, DENG Ziqi, CHEN Feixiong, et al. Double layer rolling based optimization model for joint operation of wind-hydro- storage system[J]. 2026, 59(4): 1-11.
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. DOI： 10.11930/j.issn.1004-9649.202504088.

RUAN Honghua, DENG Ziqi, CHEN Feixiong, et al. Double layer rolling based optimization model for joint operation of wind-hydro- storage system[J]. 2026, 59(4): 1-11. DOI： 10.11930/j.issn.1004-9649.202504088.

摘要

随着风电等新能源的渗透率逐步提高，电网运行的不确定性也随之加剧，如何安全、高效地发挥水电的调节作用对电网的稳定运行具有重要意义。为此，提出一种风水储联合运行的双层滚动优化调度方法。首先，构建风水储联合运行的双层滚动优化控制模型，上层优化基于长时间尺度，以最小化系统运行成本为目标，下层优化基于短时间尺度，以最小化系统出力偏差为目标；其次，采用模型预测控制方法对优化模型进行求解，通过下层的实时滚动优化对上层调度计划进行反馈校正，降低了不确定性对系统的影响。最后，以中国南方某梯级水电系统为算例对象进行仿真分析，验证了所提方法在提升能源利用效率、降低运行成本和处理不确定性方面的有效性。

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

Keywords

references

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