省间电力中长期交易出清问题多解顺次寻优的高效计算方法

doi:10.19595/j.cnki.1000-6753.tces.240774

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1300 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract With the development of the national unified electricity market, the market scale has gradually increased. Take inter-provincial medium- and long-term power transaction as an example, the number of market participants is over a thousand. The similarity of the bid prices of numerous market participants is likely to lead to multiple purchasing and selling pairs having the same social welfare. As a result, the market clearing problem that maximizes social welfare may have multiple optimal solutions. To ensure the effectiveness and fairness of market clearing, the multi-level objective sequential optimization should be implemented under multiple-solution scenarios. However, the existing methods based on multi-objective optimization cannot balance effectiveness and efficiency. To address this issue, take inter-provincial medium- and long-term power transaction that may have multiple solutions as a research objective, an efficient multi-level objective sequential optimization method is proposed in this paper. The main contributions are illustrated as follows:
First, the market clearing model with multi-level objective sequential optimization is established. Four objective functions are considered according to the industrial practices, including maximizing social welfare, maximizing transaction volume of renewable energy, maximizing total transaction volume, and equally distributing tradable power among purchasing and selling pairs with the same social welfare. Market clearing models considering the aforementioned four objective functions are separately established. The optimal objective functions of the preorder model are used as the operating constraints of the subsequent model to ensure the optimality of the objective functions with high priorities. By sequential solving these four market clearing models, the market clearing effectiveness and fairness can be guaranteed even under multiple-solution scenarios.
Second, the multiple-solution judgment auxiliary optimization model for the market clearing problem is established based on the bound constraints of the optimal solution, according to which the multiple-solution characteristics of market clearing problems can be recognized. The recognized multiple-solution characteristics can provide support for market operators to design the measure for handling multiple-solution scenarios. For instance, more objective functions can be introduced if multiple-solution scenarios cannot be effectively avoided after the sequential optimization of four objective functions. Besides, regarding the computational burden caused by the solution to four market clearing models, the multiple-solution judgment auxiliary optimization model is embedded into the sequential optimization process to simplify the clearing process by avoiding unnecessary optimization.
Third, to meet the calculation efficiency demand, the lossless acceleration method for market clearing based on solution information of the preorder model is proposed. For the market clearing models with multi-level objective functions, the optimal solution of the preorder model is used as the high-quality initial feasible solution of the subsequent model, which can guide the warm-start accelerating process of the subsequent model without the loss of accuracy. For the multiple-solution judgment auxiliary optimization model, the optimal solution of the preorder model is used as the initial feasible solution. Based on this, the termination criterion for the calculation process is established according to the comparison between the initial objective function and the current objective function. In this way, the judgment process can be accelerated without affecting judgment accuracy.
Finally, case studies based on practical inter-provincial medium- and long-term transaction data in China demonstrate that the proposed method can greatly improve the market clearing effect for the subordinate objectives while ensuring the optimality of the primary objective. In addition, benefiting from the proposed model solution acceleration strategy and the sequential optimization process simplification strategy, the market clearing efficiency can be improved by 37 times without the loss of accuracy under the typical scenario.

Key words： Inter-provincial medium- and long-term power transactions multiple solutions of market clearing multi-level objective sequential optimization multiple solution judgment market clearing acceleration

Received: 14 May 2024

PACS:	TM73
	F426.61

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Xiang Mingxu
	Chen Hongfei
	Cao Xiaojun
	Yang Zhifang
	Jin Yiding

Cite this article:

Xiang Mingxu,Chen Hongfei,Cao Xiaojun等. Efficient Sequential Optimization Method for Inter-Provincial Medium- and Long-Term Power Transaction Clearing Problem under Multiple-Solution Scenarios[J]. Transactions of China Electrotechnical Society, 2025, 40(11): 3545-3559.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240774 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I11/3545

[1] 国家发展改革委, 国家能源局. 国家发展改革委国家能源局关于加快建设全国统一电力市场体系的指导意见[R/OL].北京: 国家发展改革委,国家能源局[2022-01-18]. https://www.gov.cn/zhengce/zhengceku/2022-01/30/content_5671296.htm.
[2] 夏清, 陈启鑫, 谢开, 等. 中国特色、全国统一的电力市场关键问题研究(2): 我国跨区跨省电力交易市场的发展途径、交易品种与政策建议[J]. 电网技术, 2020, 44(8): 2801-2808.
Xia Qing, Chen Qixin, Xie Kai, et al.Key issues of national unified electricity market with Chinese characteristics (2): development path, trading varieties and policy recommendations for inter-regional and inter-provincial electricity markets[J]. Power System Technology, 2020, 44(8): 2801-2808.
[3] 陈泓霏, 向明旭, 杨知方, 等. 大规模省间电力中长期交易出清的伴随模型引导加速方法[J/OL]. 中国电机工程学报, 2024: 1-14[2024-04-09]. http://kns.cnki.net/kcms/detail/11.2107.tm.20240408.0941.002.html.
Chen Hongfei, Xiang Mingxu, Yang Zhifang, et al. Adjoint model-guided acceleration method for large-scale inter-provincial medium and long-term power transactions clearance[J/OL]. Proceedings of the CSEE, 2024: 1-14[2024-05-06]. http://kns.cnki.net/kcms/detail/11.2107.tm.20240408.0941.002.html.
[4] Nemo Committee.EUPHEMIA public description: Single price coupling algorithm[R/OL]. EU: Nemo Committee[2020-10-12].https://www.nordpoolgroup.com/4adb91/globalassets/download-center/single-day-ahead-coupling/euphemia-public-description.pdf.
[5] 陈熠, 王晗, 严正, 等. 全国统一电力市场演进过程下省间-省内市场出清及定价模型[J]. 电工技术学报, 2024, 39(7): 2116-2131.
Chen Yi, Wang Han, Yan Zheng, et al.Inter-and intra-provincial electricity market clearing and pricing model under the evolution of national unified electricity market[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 2116-2131.
[6] 张汉斌. 线性规划多重最优解判别准则刍议[J]. 运筹与管理, 2002, 11(5): 43-46.
Zhang Hanbin.Discussion on the discriminate criterion of multiple optimum solution in the linear programming[J]. Operations Research and Management Science, 2002, 11(5): 43-46.
[7] Gan Deqiang, Litvinov E.Energy and reserve market designs with explicit consideration to lost opportunity costs[J]. IEEE Transactions on Power Systems, 2003, 18(1): 53-59.
[8] Krebs V, Schewe L, Schmidt M.Uniqueness and multiplicity of market equilibria on DC power flow networks[J]. European Journal of Operational Research, 2018, 271(1): 165-178.
[9] Ambrosius M, Grimm V, Kleinert T, et al.Endogenous price zones and investment incentives in electricity markets: an application of multilevel optimization with graph partitioning[J]. Energy Economics, 2020, 92: 104879.
[10] 林雨眠, 熊厚博, 张笑演, 等. 计及新能源机会约束与虚拟储能的电-热系统分布式多目标优化调度[J]. 电工技术学报, 2024, 39(16): 5042-5059.
Lin Yumian, Xiong Houbo, Zhang Xiaoyan, et al.Distributed multi-objective optimal scheduling of integrated electric-heat system considering chance constraint of new energy and virtual storage[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5042-5059.
[11] 李军徽, 安晨宇, 李翠萍, 等. 计及调峰市场交易的储能-新能源-火电多目标优化调度[J]. 电工技术学报, 2023, 38(23): 6391-6406.
Li Junhui, An Chenyu, Li Cuiping, et al.Multi-objective optimization scheduling method considering peak regulating market transactions for energy storage-new energy-thermal power[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6391-6406.
[12] 徐玖平, 李军. 多目标决策的理论与方法[M]. 北京: 清华大学出版社, 2005.
[13] 张世旭, 李姚旺, 刘伟生, 等. 面向微电网群的云储能经济-低碳-可靠多目标优化配置方法[J/OL]. 电力系统自动化, 2024: 1-14[2024-01-02]. http://kns.cnki.net/kcms/detail/32.1180.TP.20231123.1059.004.html.
Zhang Shixu, Li Yaowang, Liu Weisheng, et al. Multi-objective optimal configuration method of cloud energy storage for microgrid clusters driven by economy, low-carbon and reliability[J/OL]. Automation of Electric Power Systems, 2024: 1-14[2024-01-02]. http://kns.cnki.net/kcms/detail/32.1180.TP.20231123.1059.004.html.
[14] 冯陈, 周大庆, 郑源, 等. 基于精细化建模的抽水蓄能机组低水头背靠背启动多目标优化[J]. 中国电机工程学报, 2023, 43(8): 3059-3070, 15.
Feng Chen, Zhou Daqing, Zheng Yuan, et al.Multi-objective optimization of back-to-back starting process for pumped storage units at low head area based on refined model[J]. Proceedings of the CSEE, 2023, 43(8): 3059-3070, 15.
[15] 莫静山. 基于LCC-HVDC的交直流系统电压无功优化方法研究[D]. 重庆: 重庆大学, 2021.
[16] 曾丹, 杨争林, 冯树海, 等. 交直流混联电网下基于ATC的省间交易优化出清建模[J]. 电网技术, 2020, 44(10): 3893-3899.
Zeng Dan, Yang Zhenglin, Feng Shuhai, et al.Inter-provincial power exchange optimization modeling considering ATC constrains of hybrid AC/DC power system[J]. Power System Technology, 2020, 44(10): 3893-3899.
[17] 程海花, 杨辰星, 刘硕, 等. 基于路径组合计及ATC的省间中长期交易优化出清和系统研发[J]. 电网技术, 2022, 46(12): 4762-4770.
Cheng Haihua, Yang Chenxing, Liu Shuo, et al.Optimization clearing and system development of inter-provincial medium and long term trade considering ATC base on path combination[J]. Power System Technology, 2022, 46(12): 4762-4770.
[18] 胡晨旭, 关立, 罗治强, 等. 跨区域省间富余可再生能源现货交易优化出清模型[J]. 电力系统自动化, 2021, 45(13): 110-116.
Hu Chenxu, Guan Li, Luo Zhiqiang, et al.Optimal clearing model for trans-regional and cross-provincial spot trading of surplus renewable energy[J]. Automation of Electric Power Systems, 2021, 45(13): 110-116.
[19] Xiang Mingxu, Yang Zhifang, Yu Juan.Hierarchical AGC dispatch with detailed modeling of energy storage system behaviors[J]. IEEE Transactions on Power Systems, 2023, 38(2): 1689-1701.
[20] 潘平奇. 线性规划计算(下)[M]. 北京: 科学出版社, 2012.