大规模电网机组组合中线路传输约束有效性规律及相似性挖掘方法

doi:10.16183/j.cnki.jsjtu.2023.550

上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (9): 1260-1269.doi: 10.16183/j.cnki.jsjtu.2023.550

• 新型电力系统与综合能源 • 上一篇下一篇

大规模电网机组组合中线路传输约束有效性规律及相似性挖掘方法

郑雨翕¹, 曾龙¹^,²(), 刘健哲¹, 崔一阳¹, 朱红³, 曹亮⁴, 苏运⁴, 韦磊³

¹ 上海交通大学电子信息与电气工程学院, 上海 200240
² 湖南工程学院电气与信息工程学院, 湖南湘潭 411104
³ 国网江苏省电力有限公司, 南京 210000
⁴ 国网上海市电力公司, 上海 200122

收稿日期:2023-11-03 修回日期:2023-12-18 接受日期:2023-12-29 出版日期:2025-09-28 发布日期:2025-09-25
通讯作者: 曾龙,讲师;E-mail:zenglong2404@qq.com.
作者简介:郑雨翕(1999—),硕士生,从事电力系统调度研究.
基金资助:
国家重点研发计划资助项目(2022YFB2404200)

Line Transmission Constraints Effectiveness Patterns and Similarity Mining Methods in Large-Scale Power Grid Unit Commitment

ZHENG Yuxi¹, ZENG Long¹^,²(), LIU Jianzhe¹, CUI Yiyang¹, ZHU Hong³, CAO Liang⁴, SU Yun⁴, WEI Lei³

¹ School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
² College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411104, Hunan, China
³ State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
⁴ State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China

Received:2023-11-03 Revised:2023-12-18 Accepted:2023-12-29 Online:2025-09-28 Published:2025-09-25

摘要/Abstract

摘要：

针对大规模线路传输约束在机组组合问题中引发的有效约束筛选难题,总结线路约束在暂态和稳态下的作用规律,提出了一种基于负荷相似性挖掘的有效约束筛选方法,去除无效的线路传输约束以简化问题规模.针对不同节点对线路潮流的影响程度,构造距离函数挖掘历史负荷数据相似性,基于负荷相似性聚类典型电力负荷场景,根据线路约束的作用情况筛选有效的线路约束.针对系统中状态长期不变的线路采用预筛选方法,在挖掘算法前判定状态以降低挖掘过程计算量.在IEEE 118和Case2746wop系统上进行仿真,验证了所提方法的有效性并与现有方法进行对比,结果表明该方法能够高效地去除99%的无效线路约束,缩减了80%以上的求解时间.

关键词: 机组组合, 约束辨识, 线路传输约束, 积极约束集, 冗余约束削减方法

Abstract:

To address the challenge of effectively filtering constraints in the unit commitment problem constrained by large-scale line transmission networks, this paper reviews the operating principles of line constraints in both transient and steady states. An effective filtering method based on load similarity mining is proposed to eliminate redundant transmission constraints and reduce the complexity of the problem. Distance functions are developed to measure the similarity of historical load data according to the influence of different nodes on line flows. Based on the similarity analysis, typical power load scenarios are clustered, and effective line constraints are identified according to their operational significance. In addition, a pre-filtering strategy is applied to system states in which line statuses remain unchanged over time, thereby reducing the computational burden during the mining process. Simulations conducted on the IEEE 118 and Case2746wop systems validate the effectiveness of the proposed method, showing that the proposed method efficiently eliminates 99% of ineffective line constraints, and reduces solving time by over 80% compared to existing approaches.

Key words: unit commitment, constraints identification, transmission line congestion, active constraint set, redundant constraints reduction method

中图分类号:

TM732

郑雨翕, 曾龙, 刘健哲, 崔一阳, 朱红, 曹亮, 苏运, 韦磊. 大规模电网机组组合中线路传输约束有效性规律及相似性挖掘方法[J]. 上海交通大学学报, 2025, 59(9): 1260-1269.

ZHENG Yuxi, ZENG Long, LIU Jianzhe, CUI Yiyang, ZHU Hong, CAO Liang, SU Yun, WEI Lei. Line Transmission Constraints Effectiveness Patterns and Similarity Mining Methods in Large-Scale Power Grid Unit Commitment[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1260-1269.

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参考文献 21

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测试系统	平均优化时间/s	约束平均缩减数量	约束平均去除率/%	误去除样本率/%	最大误去除线路数
单时段SCUC问题	0.043	183.02	99.36	0.97	1
多时段SCUC问题	0.975	181.88	99.35	0	0

测试系统	目标函数值/美元	线路约束数量	优化计算时间/s	求解计算时间/s	总时间/s
原始SCUC问题	3 041 971.64	4 464	-	43.79	43.79
优化后SCUC问题	3 041 971.64	72	0.975	7.58	8.55

测试系统	平均优化时间/s	约束平均缩减数量	约束平均去除率/%	无误去除样本率/%	最大误去除线路数
单时段SCUC问题	12.74	3 489.76	100	100	0
多时段SCUC问题	305.71	3 456.33	100	100	0

测试系统	目标函数值/美元	线路约束数量	优化计算时间/s	求解计算时间/s	总时间/s
原始SCUC问题	36 100 953.043 3	84 336	—	8 403.36	8 403.36
优化后SCUC问题	36 100 953.043 3	1 392	305.71	737.17	1 042.88

K	平均优化时间/s	约束平均缩减数量	约束平均去除率/%	无误去除样本率/%
50	0.975	181.88	99.35	100
500	0.947	180.23	98.44	100
1 000	0.955	180.03	98.34	100

大规模电网机组组合中线路传输约束有效性规律及相似性挖掘方法

Line Transmission Constraints Effectiveness Patterns and Similarity Mining Methods in Large-Scale Power Grid Unit Commitment

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图/表 12

参考文献 21

相关文章 5

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K	平均优化时间/s	约束平均缩减数量	约束平均去除率/%	无误去除样本率/%
50	305.71	3 456.33	100	100
500	285.82	3 443.67	99.63	100
1 000	378.34	3 442.00	99.59	100

测试系统	约束去除率/%	简化后误差/%	约束误去除率/%	计算时间/s
约束生成类方法^[9]	100	0	0	36.30
基于优化的方法^[7]	79.4	0	0	33.18
机器学习类方法^[14]	99.42	0.9	1.14	8.38
本文方法	99.35	0	0	8.55

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