省间-省内两级市场协调下两阶段分布鲁棒经济调度模型

doi:10.16183/j.cnki.jsjtu.2022.121

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (9): 1114-1125.doi: 10.16183/j.cnki.jsjtu.2022.121

所属专题：《上海交通大学学报》2023年“新型电力系统与综合能源”专题

• 新型电力系统与综合能源 • 上一篇下一篇

省间-省内两级市场协调下两阶段分布鲁棒经济调度模型

陈熠¹, 王晗¹, 徐潇源¹(), 胡友琳², 严正¹, 曾丹³, 冯凯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.国网上海市电力公司电力调度控制中心,上海 200122
3.中国电力科学研究院(南京分院),南京 210003

收稿日期:2022-04-24 修回日期:2022-05-25 接受日期:2022-08-05 出版日期:2023-09-28 发布日期:2023-09-27
通讯作者: 徐潇源 E-mail:xuxiaoyuan@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:
国家电网有限公司科技项目(5108-202217033A-1-1-ZN)

A Two-Stage Distributionally Robust Economic Dispatch Model Under the Coordination of Inter-Provincial and Intra-Provincial Bi-Level Market

CHEN Yi¹, WANG Han¹, XU Xiaoyuan¹(), HU Youlin², YAN Zheng¹, ZENG Dan³, FENG Kai³

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

Received:2022-04-24 Revised:2022-05-25 Accepted:2022-08-05 Online:2023-09-28 Published:2023-09-27
Contact: XU Xiaoyuan E-mail:xuxiaoyuan@sjtu.edu.cn

摘要/Abstract

摘要：

为促进全国范围内资源优化配置,我国积极开展跨省电力交易,并将逐步形成省间-省内两级电力市场运作的模式.在此背景下,提出一种考虑省间-省内两级市场协调运行的日前-日内两阶段经济调度框架.在日前调度阶段,构建省间-省内双层日前经济调度模型;在日内调度阶段,建立考虑源-荷预测偏差的日内经济调度模型.进一步,为应对源-荷预测偏差不确定性对经济调度的影响,提出日前-日内两阶段分布鲁棒优化模型及其求解方法,实现随机场景模糊集表征下日前-日内两阶段经济调度.最后,利用IEEE 39节点和118节点系统构建多送端-多受端互联的测试系统,利用算例仿真验证了所提模型及方法的有效性.

关键词: 两级电力市场, 两阶段经济调度, 源-荷不确定性, 模糊集, 分布鲁棒优化

Abstract:

To promote the optimal allocation of resources across the country, China is actively developing inter-provincial electricity transactions, and will gradually form an inter-provincial and intra-provincial electricity market operation mode. In this context, a two-stage day-ahead, and intraday economic dispatch framework considering inter-provincial and intra-provincial bi-level market coordinated operation is proposed. In the day-ahead dispatch stage, an inter-provincial and intra-provincial bi-level economic dispatch model is constructed. In the intraday dispatch stage, an economic dispatch model considering the forecast error of source-load is constructed. To further deal with the influence of the uncertainty of source-load forecast on economic dispatch, a two-stage day-ahead and intraday distributionally robust economic dispatch model and its solution method are proposed, realizing the economic dispatch under random scene ambiguity set. Finally, a multi-sending ends and multi-receiving ends interconnected test system is constructed using IEEE 39-bus and 118-bus systems. The effectiveness of the proposed model and method is verified by simulation.

Key words: bi-level electricity market, two-stage economic dispatch, uncertainty of source-load, ambiguity set, distributionally robust optimization

中图分类号:

TM732

陈熠, 王晗, 徐潇源, 胡友琳, 严正, 曾丹, 冯凯. 省间-省内两级市场协调下两阶段分布鲁棒经济调度模型[J]. 上海交通大学学报, 2023, 57(9): 1114-1125.

CHEN Yi, WANG Han, XU Xiaoyuan, HU Youlin, YAN Zheng, ZENG Dan, FENG Kai. A Two-Stage Distributionally Robust Economic Dispatch Model Under the Coordination of Inter-Provincial and Intra-Provincial Bi-Level Market[J]. Journal of Shanghai Jiao Tong University, 2023, 57(9): 1114-1125.

图/表 14

图1

图2

表1

图3

图4

表2

图5

图6

表3

图7

表4

表5

表6

图8

参考文献 24

[1]	ZHANG Y, ZHANG J Y, YANG Z F, et al. Analysis of the distribution and evolution of energy supply and demand centers of gravity in China[J]. Energy Policy, 2012, 49: 695-706. doi: 10.1016/j.enpol.2012.07.012 URL
[2]	YI B W, XU J H, FAN Y. Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model[J]. Applied Energy, 2016, 184: 641-658. doi: 10.1016/j.apenergy.2016.11.021 URL
[3]	国家能源局. 清洁低碳, 能源结构这样转型[EB/OL]. (2021-04-09)[2022-04-15]. http://www.nea.gov.cn/2021-04/09/c_139869431.htm.
	National Energy Administration. Clean and low-carbon, energy structure transformation[EB/OL]. (2021-04-09)[2022-04-15]. http://www.nea.gov.cn/2021-04/09/c_139869431.htm.
[4]	郭立邦, 丁一, 包铭磊, 等. 两级电力市场环境下计及风险的省间交易商最优购电模型[J]. 电网技术, 2019, 43(8): 2726-2734.
	GUO Libang, DING Yi, BAO Minglei, et al. An optimal power purchase model of inter-provincial traders in two-level electricity market considering risk management[J]. Power System Technology, 2019, 43(8): 2726-2734.
[5]	许丹, 梁访, 黄国栋, 等. 考虑多级调度的跨省区大电网安全约束经济调度模型[J]. 电力系统自动化, 2019, 43(22): 94-100.
	XU Dan, LIANG Fang, HUANG Guodong, et al. Security constrained economic dispatch model of inter-provincial power grid considering multi-level dispatch[J]. Automation of Electric Power Systems, 2019, 43(22): 94-100.
[6]	李国庆, 李欣彤, 边竞, 等. 计及光伏-负荷预测不确定性的直流跨省互联电网双级调度策略[J]. 中国电机工程学报, 2021, 41(14): 4763-4776.
	LI Guoqing, LI Xintong, BIAN Jing, et al. Two level scheduling strategy for inter-provincial DC power grid considering the uncertainty of PV-load prediction[J]. Proceeding of the CSEE, 2021, 41(14): 4763-4776.
[7]	徐帆, 丁恰, 韩红卫, 等. 促进跨区新能源消纳的直流联络线功率优化模型及分析[J]. 电力系统自动化, 2017, 41(18): 152-159.
	XU Fan, DING Qia, HAN Hongwei, et al. Power optimization model and analysis of HVDC tie-line for promoting integration of inter-regional renewable energy accommodation[J]. Automation of Electric Power Systems, 2017, 41(18): 152-159.
[8]	李骥, 张慧媛, 程杰慧, 等. 基于源荷状态的跨区互联系统协调优化调度[J]. 电力系统自动化, 2020, 44(17): 26-33.
	LI Ji, ZHANG Huiyuan, CHENG Jiehui, et al. Coordinated and optimal scheduling of inter-regional interconnection system based on source and load status[J]. Automation of Electric Power Systems, 2020, 44(17): 26-33.
[9]	陈磊, 姜飞, 叶泽, 等. 考虑电网运行风险的省间转供电力交易补偿模型[J]. 电网技术, 2020, 44 (12): 4583-4592.
	CHEN Lei, JIANG Fei, YE Ze, et al. Compensation model of inter-provincial transfer electricity transaction considering grid operation risk[J]. Power System Technology, 2020, 44(12): 4583-4592.
[10]	ZHENG Q P, WANG J H, PARDALOS P M, et al. A decomposition approach to the two-stage stochastic unit commitment problem[J]. Annals of Operations Research, 2013, 210(1): 387-410. doi: 10.1007/s10479-012-1092-7 URL
[11]	LI T, SHAHIDEHPOUR M, LI Z Y, et al. Risk-constrained bidding strategy with stochastic unit commitment[J]. IEEE Transactions on Power Systems, 2007, 22(1): 449-458. doi: 10.1109/TPWRS.2006.887894 URL
[12]	徐秋实, 邓长虹, 赵维兴, 等. 含风电电力系统的多场景鲁棒调度方法[J]. 电网技术, 2014, 38(3): 653-661.
	XU Qiushi, DENG Changhong, ZHAO Weixing, et al. A multi-scenario robust dispatch method for power grid integrated with wind farms[J]. Power System Technology, 2014, 38 (3): 653-661.
[13]	LUBIN M, DVORKIN Y, BACKHAUS S. A robust approach to chance constrained optimal power flow with renewable generation[J]. IEEE Transactions on Power Systems, 2016, 31(5): 3840-3849. doi: 10.1109/TPWRS.2015.2499753 URL
[14]	STREET A, OLIVEIRA F, ARROYO J M. Contingency-constrained unit commitment with n-K security criterion: A robust optimization approach[J]. IEEE Transactions on Power Systems, 2011, 26(3): 1581-1590. doi: 10.1109/TPWRS.2010.2087367 URL
[15]	SHANG C, YOU F Q. Distributionally robust optimization for planning and scheduling under uncertainty[J]. Computers & Chemical Engineering, 2018, 110(2): 53-68. doi: 10.1016/j.compchemeng.2017.12.002 URL
[16]	ZHAO C Y, GUAN Y P. Data-driven stochastic unit commitment for integrating wind generation[J]. IEEE Transactions on Power Systems, 2016, 31(4): 2587-2596. doi: 10.1109/TPWRS.2015.2477311 URL
[17]	税月, 刘俊勇, 高红均, 等. 考虑风电不确定性的电热综合系统分布鲁棒协调优化调度模型[J]. 中国电机工程学报, 2018, 38(24): 7235-7247.
	SHUI Yue, LIU Junyong, GAO Hongjun, et al. A distributionally robust coordinated dispatch model for integrated electricity and heating systems considering uncertainty of wind power[J]. Proceedings of the CSEE, 2018, 38(24): 7235-7247.
[18]	曾丹, 谢开, 庞博, 等. 中国特色、全国统一的电力市场关键问题研究(3): 省间省内电力市场协调运行的交易出清模型[J]. 电网技术, 2020, 44(8): 2809-2819.
	ZENG Dan, XIE Kai, PANG Bo, et al. Key issues of national unified electricity market with Chinese characteristics (3): Transaction clearing models and algorithms adapting to the coordinated operation of provincial electricity markets[J]. Power System Technology, 2020, 44(8): 2809-2819.
[19]	陈之栩. 节点边际电价与阻塞管理算法的研究[D]. 北京: 华北电力大学(北京), 2007.
	CHEN Zhixu. Study on locational marginal prices and congestion management algorithm[D]. Beijing: North China Electric Power University, 2007.
[20]	TALBI E G. Metaheuristics for bi-level optimization[M]. Berlin: Springer, 2013.
[21]	WANG X F, LI F X, ZHANG Q W, et al. Profit-oriented BESS siting and sizing in deregulated distribution systems[J]. IEEE Transactions on Smart Grid, 2023, 14(2): 1528-1540. doi: 10.1109/TSG.2022.3150768 URL
[22]	吴杰, 丁明, 张晶晶. 基于云模型和k-means聚类的风电场储能容量优化配置方法[J]. 电力系统自动化, 2018, 42(24): 67-73.
	WU Jie, DING Ming, ZHANG Jingjing. Capacity configuration method of energy storage system for wind farm based on cloud model and k-means clustering[J]. Automation of Electric Power Systems, 2018, 42(24): 67-73.
[23]	ZENG B, ZHAO L. Solving two-stage robust optimization problems using a column-and-constraint generation method[J]. Operations Research Letters, 2013, 41(5): 457-461. doi: 10.1016/j.orl.2013.05.003 URL
[24]	ZIMMERMAN R D, MURILLO-SÁNCHEZ C E, THOMAS R J. MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education[J]. IEEE Transactions on Power Systems, 2011, 26(1): 12-19. doi: 10.1109/TPWRS.2010.2051168 URL

受端省份	新能源风光电厂接入点	送端C省联络线接入点	送端D省联络线接入点	送端E省联络线接入点
A省	节点20	节点10	节点18	节点16
B省	节点70	节点37	节点79	节点107

受端省份	省内发电成本/元	省间购电成本/元	备用成本/元	总成本/元
A省	492310	35136	265020	792466
B省	880837	115523	193154	1189514

发/购电	A省成本/元	B省成本/元	总成本/元
非阻塞-省内发电	492310	880837	1373147
阻塞-省内发电	482420	890610	1373030
非阻塞-省间购电	35136	115523	150659
阻塞-省间购电	45007	106033	151040

优化算法	备用成本/元	弃风弃光成本/元	削负荷成本/元	总成本/元
SO	187672	110132	307028	604832
RO	210744	250533	480136	941413
DRO(1-范数)	187548	109684	312424	609656
DRO(∞-范数)	187343	112650	312331	612324

优化算法	总成本平均值/元	总成本最大值/元	总成本最小值/元	标准差/ 元
SO	2465734	2676148	2233658	84397
DRO(1-范数)	2493761	2578582	2388515	71523
DRO(∞-范数)	2532489	2592486	2407077	73295

省间-省内两级市场协调下两阶段分布鲁棒经济调度模型

A Two-Stage Distributionally Robust Economic Dispatch Model Under the Coordination of Inter-Provincial and Intra-Provincial Bi-Level Market

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 24

相关文章 1

编辑推荐

Metrics

本文评价

α₁=α_∞	1-范数成本/元	∞-范数成本/元
0.2	2590724	2592887
0.4	2591283	2593754
0.6	2592070	2594977
0.8	2593415	2597066
0.99	2599231	2606097