Load optimization scheduling decision for virtual power plants with distributed energy accessed

doi:10.3969/j.issn.2097-0706.2025.01.008

Abstract

Abstract:

Aiming to solve the problems of the variation in load characteristics and the complication in optimization scheduling caused by distributed energy access，the potential role of electric vehicles（EVs）in load optimization scheduling of virtual power plant is discussed. Firstly，the upper and lower bounds of EV charging behavior are defined，and the power and capacity prediction model of EV aggregates is constructed based on the behavior boundary，providing guarantee for the load optimal scheduling of virtual power plants. Considering that EVs have different dynamic response characteristics in power regulation，an EV clustering method based on an improved SOM algorithm is proposed. The Davis-Bouldin index is used to evaluate its clustering result，so as to ensure the accuracy of EV clustering with different power regulation characteristics. Further，considering the adverse effects of wind power and grid-connected EVs，power balance and other constraints，the load optimization scheduling model for virtual power plants is constructed. Finally，the simulation results show that the virtual power plant load optimization scheduling model can ensure the safe operation of power grid，given the circumstances that the capacities of EV aggregates are reasonably allocated.

Key words: distributed energy, virtual power plant, load optimal scheduling, electric vehicle, smart grid, Davies-Bouldin index, improved SOM, integrated energy

CLC Number:

TK01

HU Jiacheng, ZHANG Ning, CAO Yutong, HU Cungang. Load optimization scheduling decision for virtual power plants with distributed energy accessed[J]. Integrated Intelligent Energy, 2025, 47(1): 62-69.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Fig.6

References 28

[1]	郑浩, 崔双喜, 程叶凡, 等. 考虑风电波动与电动汽车集群储能平抑控制策略[J]. 电测与仪表, 2021, 58(7): 12-18.
	ZHENG Hao, CUI Shuangxi, CHENG Yefan, et al. Control strategy of considering wind power fluctuation and the stabilization of electric vehicle cluster energy storage[J]. Electrical Measurement & Instrumentation, 2021, 58(7): 12-18.
[2]	刘建行, 刘方. 基于深度强化学习的梯级水蓄风光互补系统优化调度策略研究[J]. 广东电力, 2024, 37(5): 10-22.
	LIU Jianhang, LIU Fang. Research on optimized dispatching strategy of cascade hydropower-pumping-storage-wind-photovoltaic multi-energy complementary system based on deep reinforcement learning[J]. Guangdong Electric Power, 2024, 37(5): 10-22.
[3]	林彦旭, 高辉. 基于SSA-VMD-BiLSTM模型的充电站负荷预测方法[J]. 广东电力, 2024, 37(6): 53-61.
	LIN Yanxu, GAO Hui. Load prediction method of charging station based on SSA-VMD-BiLSTM model[J]. Guangdong Electric Power, 2024, 37(6): 53-61.
[4]	李明扬, 窦梦园. 基于强化学习的含电动汽车虚拟电厂优化调度[J]. 综合智慧能源, 2024, 46(6): 27-34. doi: 10.3969/j.issn.2097-0706.2024.06.004
	LI Mingyang, DOU Mengyuan. Optimal scheduling of virtual power plants integrating electric vehicles based on reinforcement learning[J]. Integrated Intelligent Energy, 2024, 46(6): 27-34. doi: 10.3969/j.issn.2097-0706.2024.06.004
[5]	项顶, 胡泽春, 宋永华, 等. 通过电动汽车与电网互动减少弃风的商业模式与日前优化调度策略[J]. 中国电机工程学报, 2015, 35(24): 6293-6303.
	XIANG Ding, HU Zechun, SONG Yonghua, et al. Business model and day-ahead dispatch strategy to reduce wind power curtailment through vehicle-to-grid[J]. Proceedings of the CSEE, 2015, 35(24): 6293-6303.
[6]	MÜLLER F L, SZABÓ J, SUNDSTRÖM O, et al. Aggregation and disaggregation of energetic flexibility from distributed energy resources[J]. IEEE Transactions on Smart Grid, 2019, 10(2): 1205-1214.
[7]	ZHANG N, HU Z G, DAI D H, et al. Unit commitment model in smart grid environment considering carbon emissions trading[J]. IEEE Transactions on Smart Grid, 2016, 7(1):420-427.
[8]	ZENG B, ZHANG J H, YANG X, et al. Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response[J]. IEEE Transactions on Power Systems, 2014, 29(3):1153-1165.
[9]	LEE W, XIANG L, SCHOBER R, et al. Electric vehicle charging stations with renewable power generators:a game theoretical analysis[J]. IEEE Transactions on Smart Grid, 2015, 6(2):608-617.
[10]	TAVAKOLI A, NEGNEVITSKY M, NGUYEN D T, et al. Energy exchange between electric vehicle load and wind generating utilities[J]. IEEE Transactions on Power Systems, 2016, 31(2):1248-1258.
[11]	VAYÁ M G, ANDERSSON G. Self scheduling of plug-in electric vehicle aggregator to provide balancing services for wind power[J]. IEEE Transactions on Sustainable Energy, 2016, 7(2):886-899.
[12]	SIKSNYS L, VALSOMATZIS E, HOSE K, et al. Aggregating and disaggregating flexibility objects[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(11): 2893-2906.
[13]	VAGROPOULOS S, KYRIAZIDIS D, BAKIRTZIS A. Real-time charging management framework for electric vehicle aggregators in a market environment[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 948-957.
[14]	宋磊. 电动汽车充电与大规模风电并网的协同调度研究[D]. 济南: 山东大学, 2018.
	SONG Lei. Research on collaborative scheduling of electric vehicle charging and large-scale wind power grid connection[D]. Jinan: Shandong University, 2018.
[15]	孙波, 孙佳佳, 董浩. 基于分时充电电价的电动汽车消纳风电的机组调度优化模型[J]. 可再生能源, 2017, 35(1): 110-118.
	SUN Bo, SUN Jiajia, DONG Hao. Unit dispatch optimization model of electrical vehicle to accommodate the wind power based on time-of-use charging price[J]. Renewable Energy Resources, 2017, 35(1): 110-118.
[16]	XU Z, HU Z, SONG Y, et al. Risk-averse optimal bidding strategy for demand-side resource aggregators in day-ahead electricity markets under uncertainty[J]. IEEE Transactions on Smart Grid, 2015, 8(1): 96-105.
[17]	王静雯, 李华强, 李旭翔, 等. 综合能源服务效用模型及用户需求评估[J]. 中国电机工程学报, 2020, 40(2): 411-425.
	WANG Jingwen, LI Huaqiang, LI Xuxiang, et al. Utility model and demand assessment method of integrated energy service[J]. Proceedings of the CSEE, 2020, 40(2): 411-425.
[18]	石玉东, 蒋卓臻, 高红均, 等. 促进风电消纳的配电网分布式电源与电动汽车充电站联合鲁棒规划[J]. 可再生能源, 2018, 36(11): 1638-1644.
	SHI Yudong, JIANG Zhuozhen, GAO Hongjun, et al. A joint robust planning of distributed generation and electric vehicle charging stations in distribution network to promote accommodation of wind power[J]. Renewable Energy Resources, 2018, 36(11): 1638-1644.
[19]	QIAO B H, LIU J. Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm[J]. Renewable Energy, 2020, 154:316-336.
[20]	吴巨爱, 薛禹胜, 谢东亮, 等. 电动汽车参与电量市场与备用市场的联合风险调度[J]. 电工技术学报, 2023, 38(23): 6407-6418.
	WU Juai, XUE Yusheng, XIE Dongliang, et al. The joint risk dispatch of electric vehicle in day-ahead electricity energy market and reserve market[J]. Transactions of China Electrotechnical Society, 2023, 38(23):6407-6418.
[21]	张巍, 王丹. 基于云边协同的电动汽车实时需求响应调度策略[J]. 电网技术, 2022, 46(4): 1447-1458.
	ZHANG Wei, WANG Dan. Real-time demand response scheduling strategy for electric vehicles based on cloud edge collaboration[J]. Power System Technology, 2022, 46(4): 1447-1458.
[22]	郁海彬, 卢闻州, 唐亮, 等. 考虑风险偏好的多主体虚拟电厂经济调度与收益分配策略[J]. 综合智慧能源, 2024, 46(6): 66-77. doi: 10.3969/j.issn.2097-0706.2024.06.008
	YU Haibin, LU Wenzhou, TANG Liang, et al. Economic dispatch and profit distribution strategy for multi-agent virtual power plants considering risk preferences[J]. Integrated Intelligent Energy, 2024, 46(6): 66-77. doi: 10.3969/j.issn.2097-0706.2024.06.008
[23]	杨天宇, 郭庆来, 盛裕杰, 等. 系统互联视角下的城域电力-交通融合网络协同[J]. 电力系统自动化, 2020, 44(11): 1-9.
	YANG Tianyu, GUO Qinglai, SHENG Yujie, et al. Coordination of urban integrated electric power and traffic network from perspective of system interconnection[J]. Automation of Electric Power Systems, 2020, 44(11): 1-9.
[24]	SONG J, HE G N, WANG J X, et al. Shaping future low carbon energy and transportation systems:digital technologies and applications[J]. iEnergy, 2022, 1(3):285-305.
[25]	胡泽春, 邵成成, 何方, 等. 电网与交通网耦合的设施规划与运行优化研究综述及展望[J]. 电力系统自动化, 2022, 46(12): 3-19.
	HU Zechun, SHAO Chengcheng, HE Fang, et al. Review and prospect of research on facility planning and optimal operation for coupled power and transportation networks[J]. Automation of Electric Power Systems, 2022, 46(12): 3-19.
[26]	HAN K, FRIESZ T L, YAO T. A partial differential equation formulation of Vickrey's bottleneck model,part II:Numerical analysis and computation[J]. Transportation Research Part B: Methodological, 2013, 49:75-93.
[27]	SZETO W Y. Enhanced lagged cell-transmission model for dynamic traffic assignment[J]. Transportation Research Record, 2008, 2085:76-85.
[28]	GENTILE G. Using the general link transmission model in a dynamic traffic assignment to simulate congestion on urban networks[J]. Transportation Research Procedia, 2015, 5:66-81.

聚类算法	DBI	计算时间/s
改进的SOM算法	0.721 5	80.57
k-means算法	1.837 4	20.34
SOM算法	0.844 5	124.51