含风电电力系统源荷储协同分布鲁棒优化调度

doi:10.16180/j.cnki.issn1007-7820.2025.08.001

电子科技 ›› 2025, Vol. 38 ›› Issue (8): 1-10.doi: 10.16180/j.cnki.issn1007-7820.2025.08.001

• • 下一篇

含风电电力系统源荷储协同分布鲁棒优化调度

张子见¹, 何宇¹(), 张靖¹, 郭元萍², 王志杨¹, 胡祥谢¹

1.贵州大学电气工程学院,贵州贵阳 550025
2.贵州电网有限责任公司电网规划研究中心,贵州贵阳 550002

收稿日期:2024-01-02 修回日期:2024-02-08 出版日期:2025-08-15 发布日期:2025-07-10
通讯作者: 何宇(1978-),女,E-mail:heyu@gzu.edu.cn,副教授。研究方向:电力系统规划、电力系统稳定与运行等。
作者简介:张子见(1997-),男,硕士研究生。研究方向:电力系统保护与运行。
基金资助:
黔科合支撑（[2022]一般013）;黔科合支撑（[2022]一般014）;黔科合平台人才(GCC[2022]016-1);黔教技([2022]043)

Robust Optimal Scheduling of Source-Load-Storage Cooperative Distribution in Power System with Wind Power

ZHANG Zijian¹, HE Yu¹(), ZHANG Jing¹, GUO Yuanping², WANG Zhiyang¹, HU Xiangxie¹

1. College of Electrical Engineering,Guizhou University,Guiyang 550025, China
2. Power Grid Planning Research Center,Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China

Received:2024-01-02 Revised:2024-02-08 Online:2025-08-15 Published:2025-07-10
Supported by:
Science and Technology Foundation of Guizhou ([2022]General013);Science and Technology Foundation of Guizhou ([2022]General014);Science and Technology Foundation of Guizhou(GCC[2022]016-1);Educational Technology Foundation of Guizhou([2022]043)

摘要/Abstract

摘要：

针对随着电力市场化改革的不断推进,大用户直购过程对电力系统灵活性的影响问题,文中提出了考虑风电不确定性与大用户直购电的电力系统分布鲁棒优化调度模型。为降低风电不确定性对电力系统影响,考虑风电预测误差的时序性与区间性,提出一种具有区间特性的一阶马尔可夫链模型,构建数据驱动的日前两阶段分布鲁棒优化模型。模型的第一阶段以风电场场站总收益最大为目标函数,制定日前第一阶段鲁棒调度方案。日前第二阶段通过调整区域内的可控机组出力来灵活应对风电出力的不确定性。研究结果验证了分布鲁棒优化算法的有效性,并证明了大用户直购电参与调度能够有效提高系统的经济性和调峰能力。

关键词: 风电, 大用户直购电, 分布鲁棒优化, 马尔可夫链, 数据驱动, 优化调度, 调峰

Abstract:

In view of the problem of the influence of large user direct purchasing process on the flexibility of power system with the continuous advancement of power market reform, a distributed robust optimal scheduling model of power system considering wind power uncertainty and direct power purchase of large users is proposed in this study. In order to reduce the impact of wind power uncertainty on power system, considering the time series and interval of wind power prediction error, a first-order Markov chain model with interval characteristics is proposed to construct a data-driven day-ahead two-stage distribution robust optimization model. In the first stage of the model, the maximum total revenue of the wind farm station is taken as the objective function, and the first stage robust scheduling scheme is formulated. In the day-ahead second stage, the uncertainty of wind power output can be flexibly dealt with by adjusting the output of controllable units in the region. The results verify the effectiveness of the distributionally robust optimization algorithm, and prove that large users direct purchasing power dispatching can effectively improve the economy and peak load capacity of the system.

Key words: wind power, large consumers direct purchasing, distributionally robust optimization, Markov chain, data-driven, optimized scheduling, peaking shaving

中图分类号:

TM73

张子见, 何宇, 张靖, 郭元萍, 王志杨, 胡祥谢. 含风电电力系统源荷储协同分布鲁棒优化调度[J]. 电子科技, 2025, 38(8): 1-10.

ZHANG Zijian, HE Yu, ZHANG Jing, GUO Yuanping, WANG Zhiyang, HU Xiangxie. Robust Optimal Scheduling of Source-Load-Storage Cooperative Distribution in Power System with Wind Power[J]. Electronic Science and Technology, 2025, 38(8): 1-10.

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α₁	收益/10⁶美元
α₁	α_∞=0.20	α_∞=0.50	α_∞=0.80	α_∞=0.99
0.20	9.167 9	9.167 6	9.167 3	9.167 0
0.50	9.167 7	9.167 4	9.167 1	9.166 8
0.80	9.167 5	9.167 2	9.166 9	9.166 6
0.99	9.167 3	9.167 1	9.166 7	9.166 2

α₁	收益/10⁶美元
α₁	综合范数	∞-范数
0.20	9.167 0	9.165 2
0.50	9.166 8	9.165 2
0.80	9.166 6	9.165 2
0.99	9.166 2	9.165 2

α_∞	收益/10⁶美元
α_∞	综合范数	1-范数
0.20	9.167 3	9.065 6
0.50	9.167 1	9.065 6
0.80	9.166 7	9.065 6
0.99	9.166 2	9.065 6

直购电方式	SO		RO		DRO
直购电方式	总收益/10⁵美元	调峰容量/MW	总收益/10⁵美元	调峰容量/MW	总收益/10⁵美元	调峰容量/MW
无直购	8.957 1	162.906 3	8.589 8	205.914 7	8.740 1	173.358 0
电价1	9.703 9	173.226 3	9.337 8	215.914 7	9.482 9	183.358 0
电价2	9.548 1	172.906 3	9.182 3	217.914 7	9.327 5	185.358 0
电价3	9.386 0	187.066 3	9.021 7	227.914 7	9.166 3	195.358 0

含风电电力系统源荷储协同分布鲁棒优化调度

Robust Optimal Scheduling of Source-Load-Storage Cooperative Distribution in Power System with Wind Power

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