虚拟电厂规模化灵活资源集群优化配置

黄蔚亮; 王斐; 张扬; 莫理莉; 兰峻焜; 陈皓勇

doi:10.19912/j.0254-0096.tynxb.2024-0198

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (6) : 451-461. DOI: 10.19912/j.0254-0096.tynxb.2024-0198

虚拟电厂规模化灵活资源集群优化配置

黄蔚亮¹, 王斐², 张扬², 莫理莉^3,4, 兰峻焜³, 陈皓勇³

作者信息 +

OPTIMAL ALLOCATION OF LARGE SCALE FLEXIBLE RESOURCE CLUSTERS IN VIRTUAL POWER PLANT

Huang Weiliang¹, Wang Fei², Zhang Yang², Mo Lili^3,4, Lan Junkun³, Chen Haoyong³

Author information +

文章历史 +

摘要

针对大型城市电网中灵活资源存在聚合及应用困难的问题,提出一种资源集群优化配置算法。首先,提出基于灵活资源特征及辅助服务需求的分层分区集群策略,并采用基于密度的噪声应用空间聚类算法（DBSCAN）对资源进行聚类,以资源集群参与虚拟电厂任务;其次,根据虚拟电厂所需调节功率和资源集群容量潜力,优化配置其参与任务的时间段,实现资源利用效率和虚拟电厂运行效益的最大化;然后,引入状态势博弈理论和惩罚机制,优化时间段内资源配置确保任务过程中的稳定性和可靠性;最后,给出算例,证明该方案下虚拟电厂规模化灵活资源可高效地进行资源优化配置,算法是可行和有效的。

Abstract

To address the difficulty of aggregating and applying flexible resources in large urban power grids, a resource cluster optimization configuration algorithm is proposed. Firstly, a hierarchical partitioned clustering strategy based on the characteristics of flexible resources and auxiliary service demand is proposed, and the density-based spatial clustering of applications with noise （DBSCAN） clustering algorithm is used to cluster the resources to participate in the virtual power plant tasks with resource clusters. Secondly, according to the regulated power required by the virtual power plant and the capacity potential of the resource cluster, optimize the time period for its participation in tasks to maximize the resource utilization efficiency and the operational benefits of the virtual power plant. And then, the state potential game theory and penalty mechanism are introduced to optimize the resource allocation in the time period to ensure the stability and reliability of the task process. Finally, an arithmetic example is given to prove that under this scheme, the large-scale flexible resources of the virtual power plant can be efficiently optimized and allocated, and the algorithm is feasible and effective.

导出引用

黄蔚亮, 王斐, 张扬, 莫理莉, 兰峻焜, 陈皓勇. 虚拟电厂规模化灵活资源集群优化配置[J]. 太阳能学报. 2025, 46(6): 451-461 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0198

Huang Weiliang, Wang Fei, Zhang Yang, Mo Lili, Lan Junkun, Chen Haoyong. OPTIMAL ALLOCATION OF LARGE SCALE FLEXIBLE RESOURCE CLUSTERS IN VIRTUAL POWER PLANT[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 451-461 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0198

中图分类号： TM73

参考文献

[1] 康重庆, 陈启鑫, 苏剑, 等. 新型电力系统规模化灵活资源虚拟电厂科学问题与研究框架[J]. 电力系统自动化, 2022, 46(18): 3-14.
KANG C Q, CHEN Q X, SU J, et al.Scientific problems and research framework of virtual power plant with enormous flexible distributed energy resources in new power system[J]. Automation of electric power systems, 2022, 46(18): 3-14.
[2] 程韧俐, 周保荣, 史军, 等. 面向区域统一电力市场的超大城市虚拟电厂关键技术研究综述[J]. 南方电网技术, 2023, 17(4): 90-100,131.
CHENG R L, ZHOU B R, SHI J, et al.Review of key technologies for mega-city virtual power plants upon regional unified power market[J]. Southern power system technology, 2023, 17(4): 90-100,131.
[3] 张思博, 郭权利, 孔令伟, 等. 虚拟电厂内部资源优化调度综述[J]. 沈阳工程学院学报(自然科学版), 2022, 18(3): 43-50.
ZHANG S B, GUO Q L, KONG L W, et al.Review on internal resource optimal scheduling in virtual power plants[J]. Journal of Shenyang Institute of Engineering (natural science), 2022, 18(3): 43-50.
[4] 李鹏, 蒋正威, 邓一帆, 等. 虚拟电厂参与电力市场与调度控制技术研究综述[J]. 浙江电力, 2022, 41(6): 8-14.
LI P, JIANG Z W, DENG Y F, et al.A review of VPP participating in power market and dispatch control technology[J]. Zhejiang electric power, 2022, 41(6): 8-14.
[5] 陈会来, 张海波, 王兆霖. 不同类型虚拟电厂市场及调度特性参数聚合算法研究综述[J]. 中国电机工程学报, 2023, 43(1): 15-28.
CHEN H L, ZHANG H B, WANG Z L.A review of market and scheduling characteristic parameter aggregation algorithm of different types of virtual power plants[J]. Proceedings of the CESS, 2023, 43(1): 15-28.
[6] 高洪超, 王宣元, 邱小燕, 等. 新型电力系统环境下的虚拟电厂辅助调峰市场机制及其商业模式设计[J]. 太阳能学报, 2023, 44(3): 376-385.
GAO H C, WANG X Y, QIU X Y, et al.Ramping energy market mechanism and its business model of virtual power plans oriented to new power system[J]. Acta energiae solaris sinica, 2023, 44(3): 376-285.
[7] 张凯杰, 丁国锋, 闻铭, 等. 虚拟电厂的优化调度技术与市场机制设计综述[J]. 综合智慧能源, 2022, 44(2):60-72.
ZHANG K J, DING G F, WEN M, et al.Review of optimal dispatching technology and market mechanism design for virtual power plants[J]. Integrated intelligent energy, 2022, 44(2): 60-72.
[8] 余贻鑫, 刘艳丽, 秦超, 等. 分层分群电网体系结构[J]. 电力系统保护与控制, 2020, 48(22): 1-8.
YU Y X, LIU Y L, QIN C, et al.Layered and clustered grid architecture[J]. Power system protection and control, 2020, 48(22): 1-8.
[9] 卫璇, 潘昭光, 王彬, 等. 云管边端架构下虚拟电厂资源集群与协同调控研究综述及展望[J]. 全球能源互联网, 2020, 3(6): 539-551.
WEI X, PAN Z G, WANG B, et al.Review on virtual power plant resource aggregation and collaborative regulation using cloud-tube-edge-end architecture[J]. Journal of global energy interconnection, 2020, 3(6): 539-551.
[10] 王宣元, 高洪超, 张浩, 等. 面向新型电力系统的灵活资源聚合技术应用场景分析及建设启示[J]. 电力需求侧管理, 2022, 24(1): 73-80.
WANG X Y, GAO H C, ZHANG H, et al.Analysis and enlightenment of aggregation technology application scenarios of flexible distributed energy resources oriented to new power system[J]. Power demand side management, 2022, 24(1): 73-80.
[11] 张高. 含多种分布式能源的虚拟电厂竞价策略与协调调度研究[D]. 上海: 上海交通大学, 2019.
ZHANG G.Bidding strategy and coordinated dispatch of virtual power plant with multiple distributed energy resources[D]. Shanghai: Shanghai Jiao Tong University, 2019.
[12] 陈皓勇, 谭碧飞, 伍亮, 等. 分层集群的新型电力系统运行与控制[J]. 中国电机工程学报, 2023, 43(2): 581-595.
CHEN H Y, TAN B F, WU L, et al.Operation and control of the new power systems based on hierarchical clusters[J]. Proceedings of the CSEE, 2023, 43(2): 581-595.
[13] 张森豪. 考虑时空分布特性的可控负荷聚类分析及优化调度策略研究[D]. 南京: 南京邮电大学, 2022.
ZHANG S H.Cluster analysis of controllable load and optimal scheduling strategy considering space-time distribution characteristics[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2022.
[14] 高翔, 贺静, 陆嘉铭, 等. 基于密度聚类的电力设备位置信息分析方法研究[J]. 电力大数据, 2023, 26(7): 60-67.
GAO X, HE J, LU J M, et al.Research on location information analysis method of power equipment based on density clustering[J]. Power systems and big data, 2023, 26(7): 60-67.
[15] 王杨洋, 茆美琴, 杨铖, 等. 面向多场景辅助服务的大规模电动汽车聚合可调度容量建模[J]. 电力系统自动化, 2024, 48(7): 103-115.
WANG Y Y, MAO M Q, YANG C, et al.Aggregated and schedulable capacity modeling of large-scale electric vehicles for multi-scenario auxiliary services[J]. Automation of electric power systems, 2024, 48(7): 103-115.
[16] 麻吕斌, 潘国兵, 蒋群, 等. 基于EOF-DBSCAN-GRU的分布式光伏集群出力预测方法研究[J]. 太阳能学报, 2024, 45(1): 39-46.
MA L B, PAN G B, JIANG Q, et al.Research on distributed PV cluster power output forecasting method based on EOF-DBSCAN-GRU[J]. Acta energiae solaris sinica, 2024, 45(1): 39-46.
[17] 李翔宇, 赵冬梅. 计及可调资源动态特性的虚拟电厂多级优化配置[J]. 电力系统自动化, 2020, 44(13): 17-24.
LI X Y, ZHAO D M.Multi-level optimal configuration of virtual power plant considering dynamic characteristics of adjustable resources[J]. Automation of electric power systems, 2020, 44(13): 17-24.
[18] 袁桂丽, 贾新潮, 陈少梁, 等. 虚拟电厂源-荷协调多目标优化调度[J]. 太阳能学报, 2021, 42(5): 105-112.
YUAN G L, JIA X C, CHEN S L, et al.Multiobjective optimal dispatch considering source-load coordination for power plant[J]. Acta energiae solaris sinica, 2021, 42(5): 105-112.
[19] 郭亦宗. 虚拟电厂模式下考虑需求响应的电-热云储能综合优化配置[D]. 杭州: 浙江大学, 2023.
GUO Y Z.Comprehensive optimization configuration of electric-thermal cloud energy storage considering demand response in virtual power plant mode[D]. Hangzhou: Zhejiang University, 2023.
[20] 孙诗洁. 考虑工业负荷生产特性的虚拟电厂优化调度与储能配置研究[D]. 杭州: 浙江大学, 2023.
SUN S J.Research on optimal dispatch and energy storage configuration of virtual power plants considering production characteristics of industrial loads[D]. Hangzhou: Zhejiang University, 2023.
[21] 李翔宇. 虚拟电厂灵活可调资源优化配置与多层级协同优化调度[D]. 北京: 华北电力大学, 2023.
LI X Y.Flexible and adjustable resource optimal configuration and multiple-level coordinated optimal dispatch of virtual power plant[D]. Beijing: North China Electric Power University, 2023.
[22] MONDERER D, SHAPLEY L S.Potential games[J]. Games and economic behavior, 1996, 14(1): 124-143.
[23] MARDEN J R.State based potential games[J]. Automatica, 2012, 48(12): 3075-3088.
[24] 梁易乐, 刘锋, 梅生伟. 基于状态势博弈的电力系统分布式经济调度方法[J]. 系统科学与数学, 2016, 36(3): 413-425.
LIANG Y L, LIU F, MEI S W.A state-based potential game approach for distributed economic dispatch[J]. Journal of systems science and mathematical sciences, 2016, 36(3): 413-425.
[25] 曾君, 胡家健, 徐铭康, 等. 储能汇聚参与辅助调频服务的协同优化算法[J]. 控制理论与应用, 2021, 38(7): 1051-1060.
ZENG J, HU J J, XU M K, et al.Collaborative optimization algorithm for aggregating energy storage in frequency modulation service[J]. Control theory & applications, 2021, 38(7): 1051-1060.
[26] LIANG Y L, LIU F, MEI S W.Distributed real-time economic dispatch in smart grids: a state-based potential game approach[J]. IEEE transactions on smart grid, 2018, 9(5): 4194-4208.
[27] LIU X F, TANG D F, HE Q M.A state-based potential game-theoretic approach for massive residential consumers participating in demand response[J]. IEEE systems journal, 2023, 17(1): 1193-1203.