DYNAMIC ZONING PARTITIONING DECISION-MAKING METHOD FOR VIRTUAL POWER PLANTS TO SUPPORT REGIONAL BALANCE IN HIGH-PERMEABILITY AREAS

Kong Xiangyu; Yao Haoyang; Zhang Delong; Liu Gaohua; Wang Man

doi:10.19912/j.0254-0096.tynxb.2024-0110

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 166-175. DOI: 10.19912/j.0254-0096.tynxb.2024-0110

DYNAMIC ZONING PARTITIONING DECISION-MAKING METHOD FOR VIRTUAL POWER PLANTS TO SUPPORT REGIONAL BALANCE IN HIGH-PERMEABILITY AREAS

Kong Xiangyu^1,2, Yao Haoyang¹, Zhang Delong¹, Liu Gaohua², Wang Man¹

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Abstract

The integration of a large number of distributed energy resources to high-permeability regional power grids makes it more difficult for station areas to achieve self-management and interact with supply and demand. In order to optimize the allocation of regional resources and improve the capacity for new energy consumption in virtual power plants （VPPs）, this paper proposes a dynamic partitioning decision-making method to support the regional balance in high-permeability areas. Firstly, considering the load change characteristics of each edge station area, using the K-means++ clustering algorithm is used based on data feature distances, with the goal minimizing the transmission line capacity between partitions, and performing hourly rolling partitioning to distinguish the edge stations. Secondly, combined with the supply data uploaded by the edge station area and the hourly updated purchase and sale prices, a power market game strategy model is established with the goal of prioritizing digestion and transaction within the subordinate cooperative game area of VPP. Additionally, the master-slave game between VPP and subordinate regions is nested to maximize the interests of subordinate regions and minimize the overall operating cost of VPP. Finally, based on the improved IEEE62 node system and a comparison of the simulations of three different schemes, it is verified that the model achieves the effect of reducing the overall operating cost of VPP, increasing the utilization rate of distributed new energy in subordinate partitions, and reducing the transmission capacity of external transmission lines.

Key words

distributed energy resources / virtual power plants / electricity markets / dynamic partitioning / game strategy

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Kong Xiangyu, Yao Haoyang, Zhang Delong, Liu Gaohua, Wang Man. DYNAMIC ZONING PARTITIONING DECISION-MAKING METHOD FOR VIRTUAL POWER PLANTS TO SUPPORT REGIONAL BALANCE IN HIGH-PERMEABILITY AREAS[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 166-175 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0110

References

[1] 杨晓冉. 逐步推动新能源参与市场交易[N]. 中国能源报, 2023-11-06.
YANG X R. Gradually promote the participation of new energy in market transactions[N]. China energy news, 2023-11-06.
[2] GUO W S, LIU P K, SHU X L.Optimal dispatching of electric-thermal interconnected virtual power plant considering market trading mechanism[J]. Journal of cleaner production, 2021, 279: 123446.
[3] 杨博, 李玉林, 孙一心, 等. 基于博弈论的微电网电力交易综述[J]. 昆明理工大学学报(自然科学版), 2023, 48(3): 105-118.
YANG B, LI Y L, SUN Y X, et al.A critical review of microgrid power trading based on game theory[J]. Journal of Kunming University of Science and Technology (natural science), 2023, 48(3): 105-118.
[4] 丁筱, 郭创新. 考虑市场环境的多微网分散协同调度方法[J]. 现代电力, 2020, 37(3): 221-229.
DING X, GUO C X.Decentralized synergetic dispatching method for multi-microgrid under market environment[J]. Modern electric power, 2020, 37(3): 221-229.
[5] SAMADI E, BADRI A, EBRAHIMPOUR R.Decentralized multi-agent based energy management of microgrid using reinforcement learning[J]. International journal of electrical power & energy systems, 2020, 122: 106211.
[6] 孙利强, 耿少博, 常风然, 等. 促进主动配电网高效运维的虚拟微网分区划分方法研究[J]. 现代电力, 2019, 36(5): 10-16.
SUN L Q, GENG S B, CHANG F R, et al.Research on virtual microgrid partition method for promoting active distribution network optimization operation and maintenance[J]. Modern electric power, 2019, 36(5): 10-16.
[7] 杨钤, 王建学, 李旭霞, 等. 考虑线路负载率指标规划-运行协调一致性的电网规划双层模型[J]. 电网技术, 2024, 48(5): 1846-1854.
YANG Q, WANG J X, LI X X, et al.A bi-level transmission expansion planning model considering planning-operation coordination of line load rate indices[J]. Power system technology, 2024, 48(5): 1846-1854.
[8] 李英量, 王康, 高兆迪, 等. 考虑风电接入的电压控制区域修正方法研究[J]. 太阳能学报, 2022, 43(9): 258-266.
LI Y L, WANG K, GAO Z D, et al.Research on modification method of voltage control area considering wind power connection[J]. Acta energiae solaris sinica, 2022, 43(9): 258-266.
[9] 赵鹏臻, 谢宁, 殷佳敏, 等. 适应新型电力系统发展趋势的配电网集中-分布式形态及其分层分区方法[J]. 智慧电力, 2023, 51(1): 94-100.
ZHAO P Z, XIE N, YIN J M, et al.Centralized-distributed pattern of distribution network and its hierarchical partition method adapting to development trend of new power system[J]. Smart power, 2023, 51(1): 94-100.
[10] 王晶晶, 姚良忠, 刘科研, 等. 面向区域自治的配电网动态区域划分方法[J]. 电网技术, 2024, 48(11): 4699-4709.
WANG J J, YAO L Z, LIU K Y, et al.Dynamic network portioning method of distribution networks considering regional autonomy[J]. Power system technology, 2024, 48(11): 4699-4709.
[11] 仝杰, 齐子豪, 蒲天骄, 等. 电力物联网边缘智能:概念、架构、技术及应用[J]. 中国电机工程学报, 2024, 44(14): 5473-5496.
TONG J, QI Z H, PU T J, et al.Edge intelligence to power internet of things: concept, architecture, technology and application[J]. Proceedings of the CSEE, 2024, 44(14): 5473-5496.
[12] 刘林, 祁兵, 李彬, 等. 面向电力物联网新业务的电力通信网需求及发展趋势[J]. 电网技术, 2020, 44(8): 3114-3128.
LIU L, QI B, LI B, et al.Requirements and developing trends of electric power communication network for new services in electric Internet of Things[J]. Power system technology, 2020, 44(8): 3114-3128.
[13] 李彬, 贾滨诚, 曹望璋, 等. 边缘计算在电力需求响应业务中的应用展望[J]. 电网技术, 2018, 42(1): 79-87.
LI B, JIA B C, CAO W Z, et al.Application prospect of edge computing in power demand response business[J]. Power system technology, 2018, 42(1): 79-87.
[14] 李军祥, 周继儒, 何建佳. 基于区块链的电网实时定价混合博弈研究[J]. 电网技术, 2020, 44(11): 4183-4191.
LI J X, ZHOU J R, HE J J.Mixed game of real-time pricing based on block chain for power grid[J]. Power system technology, 2020, 44(11): 4183-4191.
[15] CHENG S Q, TENG Y, ZUO H, et al.Power balance partition control based on topology characteristics of multi-source energy storage nodes[J]. Frontiers in energy research, 2022, 10: 843536.
[16] 魏景东, 郭雁珩, 艾琳, 等. 我国构建新型电力系统实现路径分析[J]. 水力发电, 2023, 49(11): 11-15.
WEI J D, GUO Y H, AI L, et al.Analysis on the realization path of building a new power system in China[J]. Water power, 2023, 49(11): 11-15.
[17] 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 plants oriented to new power system[J]. Acta energiae solaris sinica, 2023, 44(3): 376-385.
[18] 刘顺成, 何禹清, 乔学博, 等. 基于合作博弈的交直流配电网光储协调规划方法[J]. 太阳能学报, 2024, 45(1): 534-544.
LIU S C, HE Y Q, QIAO X B, et al.Photovoltaic-storage coordinated planning method of AC/DC distribution network based on cooperative game[J]. Acta energiae solaris sinica, 2024, 45(1): 534-544.
[19] 李晓舟, 秦文萍, 景祥, 等. 计及不确定风险和多主体协同的虚拟电厂参与主辅市场联合优化策略[J]. 电网技术, 2024,48(11): 4553-4567.
LI X Z, QIN W P, JING X, et al.Joint optimi -zation strategy of virtual power plant participation in main and auxiliary markets considering uncertain risks and multi-agent coordination[J]. Power system technology, 2024,48(11): 4553-4567.
[20] 王译庆. 试论Power Query M语言在数据处理中的应用[J]. 中国管理信息化, 2022, 25(17): 199-204.
WANG Y Q.On the application of Power Query M language in data processing[J]. China management informationization, 2022, 25(17): 199-204.