基于低秩逼近代理模型的N-1安全约束经济调度快速计算方法

doi:10.16183/j.cnki.jsjtu.2023.131

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (10): 1524-1533.doi: 10.16183/j.cnki.jsjtu.2023.131

• 新型电力系统与综合能源 • 上一篇下一篇

基于低秩逼近代理模型的N-1安全约束经济调度快速计算方法

陈熠¹, 王晗¹(), 曾丹², 严正¹, 薛必克², 赵乐³, 熊雪君³, 冯煜尧³

1.上海交通大学电力传输与功率变换控制教育部重点实验室,上海 200240
2.中国电力科学研究院(南京分院),南京 210003
3.国网上海市电力公司, 上海 200122

收稿日期:2023-04-13 修回日期:2023-06-16 接受日期:2023-07-24 出版日期:2024-10-28 发布日期:2024-11-01
通讯作者: 王晗,助理研究员;E-mail:wanghan9894@sjtu.edu.cn.
作者简介:陈熠(1999—),硕士生,从事电力市场优化调度研究.
基金资助:
国家电网公司总部科技项目(5100-202055494A-0-0-00)

A Fast Calculation Method for N-1 Security-Constrained Economic Dispatch via Low-Rank Approximation Surrogate Model

CHEN Yi¹, WANG Han¹(), ZENG Dan², YAN Zheng¹, XUE Bike², ZHAO Le³, XIONG Xuejun³, FENG Yuyao³

1. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
2. China Electric Power Research Institute (Nanjing Branch), Nanjing 210003, China
3. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China

Received:2023-04-13 Revised:2023-06-16 Accepted:2023-07-24 Online:2024-10-28 Published:2024-11-01

摘要/Abstract

摘要：

随着新能源并网比例不断提高,为保障电力系统可靠运行,安全约束经济调度(SCED)需要考虑海量的N-1安全约束,对模型求解造成极大的计算负担. N-1安全约束中只有少量约束在计算过程中起作用,剔除大量冗余约束有助于提高SCED模型的求解效率.提出基于低秩逼近(LRA)代理模型的SCED模型快速计算方法,首先构建考虑风力发电、光伏的SCED模型,并根据SCED模型的历史运行信息建立LRA代理模型;其次基于LRA代理模型的估计结果辨识关键约束和构建积极约束集,并提出基于LRA的SCED模型迭代求解流程;最后在IEEE 39节点系统下进行算例仿真.仿真结果表明,LRA代理模型与SCED模型的求解结果误差小于10%,约束辨识准确率高,所提求解流程的平均迭代求解时间降低了50%以上,显著提高了SCED模型的求解效率.

关键词: 安全约束经济调度, 低秩逼近, 约束辨识, 积极约束集

Abstract:

With the increase in the proportion of renewable energy connected to the grid, a large number of N-1 security constraints should be considered in the security-constrained economic dispatch (SCED) to ensure the reliable operation of the power system, which causes a great computational burden. However, only a small number of constraints in the N-1 security constraints are contributing factors. Therefore, the elimination of redundant constraints can significantly improve the solution efficiency of the SCED model. First, a new SCED model considering wind power and photovoltaic is established, and a low-rank approximation (LRA) surrogate model is constructed based on the historical operation information of the SCED model. Then, the active constraints are identified based on the estimation results of the LRA surrogate model and the active constraint set is constructed. Next, an iterative solution process of the SCED model based on LRA surrogate model is proposed. Finally, the simulations are conducted on an IEEE 39-bus system. The simulation results show that the error between the solution results of the LRA surrogate and the SCED models is less than 10%, the active constraint identification accuracy is raised, and the average iterative solution time of the proposed solution process is reduced by more than 50%, which indicates the improvement of the solution efficiency of the SCED model.

Key words: security-constrained economic dispatch (SCED), low-rank approximation (LRA), constraints identification, active constraint set

中图分类号:

TM732

陈熠, 王晗, 曾丹, 严正, 薛必克, 赵乐, 熊雪君, 冯煜尧. 基于低秩逼近代理模型的N-1安全约束经济调度快速计算方法[J]. 上海交通大学学报, 2024, 58(10): 1524-1533.

CHEN Yi, WANG Han, ZENG Dan, YAN Zheng, XUE Bike, ZHAO Le, XIONG Xuejun, FENG Yuyao. A Fast Calculation Method for N-1 Security-Constrained Economic Dispatch via Low-Rank Approximation Surrogate Model[J]. Journal of Shanghai Jiao Tong University, 2024, 58(10): 1524-1533.

图/表 11

图1

图2

表1

图3

图4

图5

图6

图7

图8

图9

表2

参考文献 33

[1]	黄强, 郭怿, 江建华, 等. “双碳”目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509. doi: 10.16183/j.cnki.jsjtu.2021.272
	HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[2]	姜海洋, 杜尔顺, 金晨, 等. 高比例清洁能源并网的跨国互联电力系统多时间尺度储能容量优化规划[J]. 中国电机工程学报, 2021, 41(6): 2101-2114.
	JIANG Haiyang, DU Ershun, JIN Chen, et al. Optimal planning of multi-time scale energy storage capacity of cross-national interconnected power system with high proportion of clean energy[J]. Proceedings of the CSEE, 2021, 41(6): 2101-2114.
[3]	CHENG J H, YI J H, DAI S, et al. Can low-carbon city construction facilitate green growth? Evidence from China’s pilot low-carbon city initiative[J]. Journal of Cleaner Production, 2019, 231: 1158-1170.
[4]	WANG Y, ZHANG D Y, JI Q, et al. Regional renewable energy development in China: A multidimensional assessment[J]. Renewable and Sustainable Energy Reviews, 2020, 124: 109797.
[5]	陈文溆乐, 向月, 彭光博, 等. “双碳”目标下电力系统供给侧形态发展系统动力学建模与分析[J]. 上海交通大学学报, 2021, 55(12): 1567-1576. doi: 10.16183/j.cnki.jsjtu.2021.294
	CHEN Wenxule, XIANG Yue, PENG Guangbo, et al. System dynamic modeling and analysis of power system supply side morphological development with dual carbon targets[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1567-1576.
[6]	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2818.
	ZHANG Zhigang, KANG Chongqing. Challenges and prospects of building a new power system under the goal of carbon neutrality[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2818.
[7]	DU E S, ZHANG N, HODGE B M, et al. The role of concentrating solar power toward high renewable energy penetrated power systems[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6630-6641.
[8]	魏利屾, 冯宇昂, 方家琨, 等. 现货市场环境下新能源并网接入对市场出清的影响[J]. 上海交通大学学报, 2021, 55(12): 1631-1639. doi: 10.16183/j.cnki.jsjtu.2021.329
	WEI Lishen, FENG Yuang, FANG Jiakun, et al. Impact of renewable energy integration on market-clearing results in spot market environment[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1631-1639.
[9]	WU H Y, KRAD I, FLORITA A, et al. Stochastic multi-timescale power system operations with variable wind generation[J]. IEEE Transactions on Power Systems, 2017, 32(5): 3325-3337.
[10]	GU Y Z, XIE L. Stochastic look-ahead economic dispatch with variable generation resources[J]. IEEE Transactions on Power Systems, 2017, 32(1): 17-29.
[11]	崔杨, 于世鹏, 张节潭, 等. 考虑光热电站调峰补偿的高比例新能源电力系统经济调度[J]. 中国电机工程学报, 2023, 43(13): 4922-4934.
	CUI Yang, YU Shipeng, ZHANG Jietan, et al. Economic dispatch of high-proportion renewable energy power system considering peak-shaving compensation of concentrating solar power plant[J]. Proceedings of the CSEE, 2023, 43(13): 4922-4934.
[12]	崔杨, 姜涛, 仲悟之, 等. 考虑风电消纳的区域综合能源系统源荷协调经济调度[J]. 电网技术, 2020, 44(7): 2474-2482.
	CUI Yang, JIANG Tao, ZHONG Wuzhi, et al. Coordinated economic dispatch of source and load of regional comprehensive energy system considering wind power consumption[J]. Power System Technology, 2020, 44(7): 2474-2482.
[13]	张衡, 程浩忠, 张建平, 等. 高比例风电背景下计及N-1安全网络约束的发输电优化规划[J]. 中国电机工程学报, 2018, 38(20): 5929-5936.
	ZHANG Heng, CHENG Haozheng, ZHANG Jianping, et al. Generation and transmission expansion planning considering N-1 security constraints with high penetration of wind power[J]. Proceedings of the CSEE, 2018, 38(20): 5929-5936.
[14]	赵博石, 胡泽春, 宋永华. 考虑N-1安全约束的含可再生能源输电网结构鲁棒优化[J]. 电力系统自动化, 2019, 43(4): 16-24.
	ZHAO Boshi, HU Zechun, SONG Yonghua. Robust optimization of transmission topology with renewable energy sources considering N-1 security constraint[J]. Automation of Electric Power Systems, 2019, 43(4): 16-24.
[15]	宋嗣博, 郭红霞, 杨苹, 等. 基于节点边际电价的电力市场分区策略研究[J]. 电力建设, 2017, 38(9): 132-138. doi: 10.3969/j.issn.1000-7229.2017.09.019
	SONG Sibo, GUO Hongxia, YANG Ping, et al. Electricity market partitioning strategy based on locational marginal price[J]. Electric Power Construction, 2017, 38(9): 132-138. doi: 10.3969/j.issn.1000-7229.2017.09.019
[16]	TAN Z F, ZHONG H W, XIA Q, et al. Non-iterative multi-area coordinated dispatch via condensed system representation[J]. IEEE Transactions on Power Systems, 2021, 36(2): 1594-1604.
[17]	王砚平. 安全约束机组组合问题的冗余约束削减方法研究[D]. 杭州: 浙江大学, 2021.
	WANG Yanping. Research on redundancy constraint reduction method for unit commitment problem with safety constraints[D]. Hangzhou: Zhejiang University, 2021.
[18]	SANTOS XAVIER A, QIU F, WANG F Y, et al. Transmission constraint filtering in large-scale security-constrained unit commitment[J]. IEEE Transactions on Power Systems, 2019, 34(3): 2457-2460.
[19]	朱正春, 杨知方, 余娟, 等. 面向小样本场景的数据驱动安全约束经济调度快速计算方法[J]. 中国电机工程学报, 2022, 42(12): 4430-4439.
	ZHU Zhengchun, YANG Zhifang, YU Juan, et al. Fast calculation method of data-driven security constrained economic scheduling for small sample scenarios[J]. Proceedings of the CSEE, 2022, 42(12): 4430-4439.
[20]	NI F, NGUYEN P H, COBBEN J F G. Basis-adaptive sparse polynomial chaos expansion for probabilistic power flow[J]. IEEE Transactions on Power Systems, 2017, 32(1): 694-704.
[21]	WANG X T, LIU R P, WANG X Z, et al. A data-driven uncertainty quantification method for stochastic economic dispatch[J]. IEEE Transactions on Power Systems, 2022, 37(1): 812-815.
[22]	孙鑫, 陈金富, 段献忠, 等. 计及高维随机变量的随机响应面法概率潮流计算[J]. 中国电机工程学报, 2018, 38(9): 2551-2560.
	SUN Xin, CHEN Jinfu, DUAN Xianzhong, et al. Probabilistic power flow calculation by random response surface method considering high-dimensional random variables[J]. Proceedings of the CSEE, 2018, 38(9): 2551-2560.
[23]	XIA B, REN Z Y, KOH C S. Utilizing kriging surrogate models for multi-objective robust optimization of electromagnetic devices[J]. IEEE Transactions on Magnetics, 2014, 50(2): 693-696.
[24]	陈乾, 张沈习, 程浩忠, 等. 基于径向基函数随机响应面法的综合能源系统概率能流计算[J]. 中国电机工程学报, 2022, 42(22): 8075-8088.
	CHEN Qian, ZHANG Shenxi, CHENG Haozhong, et al. Probabilistic energy flow calculation for integrated energy systems based on radial basis function-stochastic response surface method[J]. Proceedings of the CSEE, 2022, 42(22): 8075-8088.
[25]	何琨, 徐潇源, 严正, 等. 基于稀疏多项式混沌展开的孤岛微电网概率潮流计算[J]. 电力系统自动化, 2019, 43(2): 67-75.
	HE Kun, XU Xiaoyuan, YAN Zheng, et al. Probabilistic power flow calculation of islanded microgrid based on sparse polynomial chaos expansion[J]. Automation of Electric Power Systems, 2019, 43(2): 67-75.
[26]	方晓涛, 严正, 王晗, 等. 考虑源-荷随机-模糊特征的配电网潮流不确定性量化方法[J]. 中国电机工程学报, 2022, 42(20): 7509-7523.
	FANG Xiaotao, YAN Zheng, WANG Han, et al. Uncertainty quantification method of distribution network power flow considering the random and fuzzy characteristics of source-load[J]. Proceedings of the CSEE, 2022, 42(20): 7509-7523.
[27]	潘忠美, 刘健, 吴建中, 等. 基于低秩逼近法的下垂控制孤岛微电网三相概率潮流计算[J]. 中国电机工程学报, 2020, 40(20): 6506-6515.
	PAN Zhongmei, LIU Jian, WU Jianzhong, et al. Three-phase probabilistic power flow calculation of sag control islet microgrid based on low rank approximation method[J]. Proceedings of the CSEE, 2020, 40(20): 6506-6515.
[28]	KONAKLI K, SUDRET B. Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions[J]. Journal of Computational Physics, 2016, 321: 1144-1169.
[29]	DOOSTAN A, VALIDI A, IACCARINO G. Non-intrusive low-rank separated approximation of high-dimensional stochastic models[J]. Computer Methods in Applied Mechanics and Engineering, 2013, 263: 42-55.
[30]	SHENG H, WANG X Z. Probabilistic power flow calculation using non-intrusive low-rank approximation method[J]. IEEE Transactions on Power Systems, 2019, 34(4): 3014-3025.
[31]	CHEVREUIL M, LEBRUN R, NOUY A, et al. A least-squares method for sparse low rank approximation of multivariate functions[J]. ASA Journal on Uncertainty Quantification, 2015, 3(1): 897-921.
[32]	RAMOS C J, MARTINS A P, CARVALHO A S. Frequency and phase-angle estimation using ordinary least squares[J]. IEEE Transactions on Industrial Electronics, 2015, 62(9): 5677-5688.
[33]	MARELLI S, SUDRET B. UQLab: A framework for uncertainty quantification in Matlab[C]// Proc. 2nd Int. Conf. on Vulnerability, Risk Analysis and Management (ICVRAM2014). Liverpool, United Kingdom: American Society of Civil Engineers, 2014: 2554-2563.

发电机	所在节点	二次项发电成本系数/ [元·(MW²·h)^-1]	一次项发电成本系数/ [元·(MW·h)^-1]	常数项发电成本系数/ (元·h^-1)	备用成本系数/ [元·(MW·h)^-1]
G₁	节点30	0.226	30.42	786.80	20
G₂	节点31	0.588	65.12	451.32	15
G₃	节点32	0.665	60.40	1049.50	10
G₄	节点33	0.468	63.12	251.32	12
G₅	节点34	0.687	54.24	670.11	16
G₆	节点35	0.698	55.48	469.31	13

求解方法	平均求解时间/s				总平均求解时间/s
求解方法	e_Itr=0	e_Itr=1	e_Itr=2	e_Itr≥3	总平均求解时间/s
普通迭代法		5.48	7.92	15.27	11.54
基于LRA的迭代法	2.94	5.17	7.33	10.04	5.11

基于低秩逼近代理模型的N-1安全约束经济调度快速计算方法

A Fast Calculation Method for N-1 Security-Constrained Economic Dispatch via Low-Rank Approximation Surrogate Model

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 33

相关文章 15

编辑推荐

Metrics

本文评价

[1]	卜强生, 吕朋蓬, 李炜祺, 罗飞, 俞婧雯, 窦晓波, 胡秦然. 基于SLM-RBF的配电网分布式光伏集群智能划分策略[J]. 上海交通大学学报, 2024, 58(10): 1534-1543.
[2]	张良, 郑丽冬, 冷祥彪, 吕玲, 蔡国伟. 基于灰狼算法的风-光-抽水蓄能联合系统多目标优化策略[J]. 上海交通大学学报, 2024, 58(10): 1554-1566.
[3]	林森, 文书礼, 朱淼, 戴群, 鄢伦, 赵耀, 叶惠丽. 考虑碳交易机制的海港综合能源系统电-热混合储能优化配置[J]. 上海交通大学学报, 2024, 58(9): 1344-1356.
[4]	周思怡, 杨欢红, 黄文焘, 周泽, 焦伟, 杨镇瑜. 集装箱港口综合能源系统日前-日内两阶段滚动优化调度[J]. 上海交通大学学报, 2024, 58(9): 1357-1369.
[5]	王于波, 郝玲, 徐飞, 陈文彬, 郑利斌, 陈磊, 闵勇. 分布式光伏集群发电功率波动模式识别与超短期概率预测[J]. 上海交通大学学报, 2024, 58(9): 1334-1343.
[6]	米阳, 陈宇阳, 陈博洋, 韩云昊, 袁明瀚. 考虑微能网接入主动配电网的共享储能多目标配置[J]. 上海交通大学学报, 2024, 58(9): 1309-1322.
[7]	李冰洁, 袁晓昀, 史静, 徐华池, 罗子萱. 综合能源系统电气热多能量流建模及优化[J]. 上海交通大学学报, 2024, 58(9): 1297-1308.
[8]	米阳, 付起欣, 赵海辉, 马思源, 王育飞. 考虑源荷多区间不确定集和综合需求响应的微能源网鲁棒优化调度[J]. 上海交通大学学报, 2024, 58(9): 1323-1333.
[9]	王金锋, 王琪, 任正某, 孙晓晨, 孙毅, 赵一伊. 基于联邦强化学习的电热综合能源系统能量管理策略[J]. 上海交通大学学报, 2024, 58(6): 904-915.
[10]	段佳南, 谢俊, 邢单玺. 风电-光伏-抽蓄-电制氢多主体能源系统增益的合作博弈分配策略[J]. 上海交通大学学报, 2024, 58(6): 872-880.
[11]	陈晖, 何耿生, 刘昱良, 曾红梅, 张世旭, 李姚旺. 考虑热力系统等效储能特性的低碳园区储能容量优化配置[J]. 上海交通大学学报, 2024, 58(6): 863-871.
[12]	刘舒, 周敏, 高元海, 徐潇源, 严正. 融合拓扑信息的配电网电压-功率灵敏度估计数据驱动方法[J]. 上海交通大学学报, 2024, 58(6): 855-862.
[13]	范宏, 邢梦晴, 王兰坤, 田书欣. 考虑氢储的风光氢综合能源系统多时间尺度随机生产模拟[J]. 上海交通大学学报, 2024, 58(6): 881-892.
[14]	邱革非, 冯泽华, 沈赋, 何超, 何虹辉, 刘铠铭. 考虑车网互动的园区电网动态双层能量管理策略[J]. 上海交通大学学报, 2024, 58(6): 916-925.
[15]	宋梦, 林固静, 蒙璟, 高赐威, 陈涛, 夏世威, 班明飞. 共享储能关键技术与应用[J]. 上海交通大学学报, 2024, 58(5): 585-599.