Distribution Network Planning Considering the Flexibility of EV Virtual Power Plants and High Penetration of PV

doi:10.12204/j.issn.1000-7229.2022.11.002

Abstract

Abstract:

This paper studies the distribution network planning problem which considers high proportion of photovoltaic power and large-scale electric vehicles (EVs). Firstly, the paper quantifies the flexibility of the EV virtual power plant, and then establishes a distribution network planning model that comprehensively considers the flexibility of the EV virtual power plant and the high proportion of photovoltaic access. The model aims to minimize the annual investment cost of distribution network lines, while taking into account renewable energy consumption, investment costs of energy storage system and flexibility compensation costs of EV virtual power plant, in order to improve the economy of distribution network planning and realize peak shaving and valley filling of power system and improve the photovoltaic output consumption rate. Finally, the IEEE 24-node distribution network system is taken as an example for simulation verification. The example shows that the planning model proposed in this paper uses the flexibility of the energy storage system and electric vehicles to reduce the planning and operation cost of the system and improves the consumption rate of the photovoltaic power station in the distribution network. The model can provide reference for future power system planning including high penetration of renewable energy and virtual power plant flexibility resources.

Key words: distribution network planning, virtual power plant, electric vehicle, new energy consumption

CLC Number:

TM715

YAN Huan, HU Junjie, HUANG Danli, ZHANG Zhanyu, YUE Yuanyuan. Distribution Network Planning Considering the Flexibility of EV Virtual Power Plants and High Penetration of PV[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(11): 14-23.

Figures/Tables 13

Fig.1 Framework for distribution network planning

Fig.2 Electrical scope of unit EV

Fig.3 Backup capacity of EV

Fig.4 Timing load power and adjustable power curves of 10 000 electric vehicles

Fig.5 Sequential active power curves of PV

Fig.6 Sequential load rate curves of load

Table 1 The cost parameters of the energy storage system

Table 2 Compensation electricity price of EV virtual power plants’ flexible resources

Table 3 Route planning results

Table 4

Fig.7 Comparison of planning costs for distribution network

Fig.8 PV power output at Node 3 comparison between the 1st and 2nd typical day

Fig.9 PV power output at Node 3 comparison between the 3rd and 4th typical day

References 20

[1]	杨洪朝, 杨迪, 孟科. 高比例可再生能源渗透下多虚拟电厂多时间尺度协调优化调度[J]. 智慧电力, 2021, 49(2): 60-68.
	YANG Hongzhao, YANG Di, MENG Ke. Multi-time scale coordination optimal scheduling of multiple virtual power plants with high-penetration renewable energy integration[J]. Smart Power, 2021, 49(2): 60-68.
[2]	翁菖宏, 胡志坚, 刘妍, 等. 计及互联调控的新能源汽车一体化供能站规划[J]. 智慧电力, 2021, 49(9): 24-31, 94.
	WENG Changhong, HU Zhijian, LIU Yan, et al. Integrated power supply station planning of new energy vehicles with interconnection control[J]. Smart Power, 2021, 49(9): 24-31, 94.
[3]	宣文博, 李慧, 刘忠义, 等. 一种基于虚拟电厂技术的城市可再生能源消纳能力提升方法[J]. 发电技术, 2021, 42(3): 289-297. doi: 10.12096/j.2096-4528.pgt.20104
	XUAN Wenbo, LI Hui, LIU Zhongyi, et al. A method for improving the accommodating capability of urban renewable energy based on virtual power plant technology[J]. Power Generation Technology, 2021, 42(3): 289-297. doi: 10.12096/j.2096-4528.pgt.20104
[4]	吴界辰, 艾欣, 胡俊杰. 需求侧资源灵活性刻画及其在日前优化调度中的应用[J]. 电工技术学报, 2020, 35(9): 1973-1984.
	WU Jiechen, AI Xin, HU Junjie. Methods for characterizing flexibilities from demand-side resources and their applications in the day-ahead optimal scheduling[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 1973-1984.
[5]	周仁和. 高比例新能源电力系统灵活性资源价值评价模型及应用[D]. 北京: 华北电力大学, 2021.
	ZHOU Renhe. Flexible resources value evaluation model and application of high proportion new energy power system[D]. Beijing: North China Electric Power University, 2021.
[6]	吴洲洋, 艾欣, 胡俊杰. 需求侧灵活性资源参与调频辅助服务的备用优化与实时调度[J]. 电力系统自动化, 2021, 45(6): 148-157.
	WU Zhouyang, AI Xin, HU Junjie. Reserve optimization and real-time scheduling of frequency regulation ancillary service with participation of flexible resource on demand side[J]. Automation of Electric Power Systems, 2021, 45(6): 148-157.
[7]	李秀磊, 耿光飞, 季玉琦, 等. 主动配电网中储能和需求侧响应的联合优化规划[J]. 电网技术, 2016, 40(12): 3803-3810.
	LI Xiulei, GENG Guangfei, JI Yuqi, et al. Integrated optimal planning of energy storage and demand side response in active power distribution network[J]. Power System Technology, 2016, 40(12): 3803-3810.
[8]	葛少云, 张有为, 刘洪, 等. 考虑网架动态重构的主动配电网双层扩展规划[J]. 电网技术, 2018, 42(5): 1526-1536.
	GE Shaoyun, ZHANG Youwei, LIU Hong, et al. Bi-layer expansion programming method for active distribution network considering dynamic grid reconfiguration[J]. Power System Technology, 2018, 42(5): 1526-1536.
[9]	吴志, 刘亚斐, 顾伟, 等. 基于改进Benders分解的储能、分布式电源与配电网多阶段规划[J]. 中国电机工程学报, 2019, 39(16): 4705-4715, 4973.
	WU Zhi, LIU Yafei, GU Wei, et al. A modified decomposition method for multistage planning of energy storage, distributed generation and distribution network[J]. Proceedings of the CSEE, 2019, 39(16): 4705-4715, 4973.
[10]	贾龙, 胡泽春, 宋永华, 等. 储能和电动汽车充电站与配电网的联合规划研究[J]. 中国电机工程学报, 2017, 37(1): 73-84.
	JIA Long, HU Zechun, SONG Yonghua, et al. Joint planning of distribution networks with distributed energy storage systems and electric vehicle charging stations[J]. Proceedings of the CSEE, 2017, 37(1): 73-84.
[11]	钟清, 孙闻, 余南华, 等. 主动配电网规划中的负荷预测与发电预测[J]. 中国电机工程学报, 2014, 34(19): 3050-3056.
	ZHONG Qing, SUN Wen, YU Nanhua, et al. Load and power forecasting in active distribution network planning[J]. Proceedings of the CSEE, 2014, 34(19): 3050-3056.
[12]	TÓMASSON E, SÖDER L. Improved importance sampling for reliability evaluation of composite power systems[J]. IEEE Transactions on Power Systems, 2017, 32(3): 2426-2434. doi: 10.1109/TPWRS.2016.2614831 URL
[13]	黄家祺, 张宇威, 贺继锋, 等. 一种考虑极限场景的配电网鲁棒扩展规划方法[J]. 电力建设, 2020, 41(7): 67-74. doi: 10.12204/j.issn.1000-7229.2020.07.009
	HUANG Jiaqi, ZHANG Yuwei, HE Jifeng, et al. A robust expansion planning method for distribution networks considering extreme scenarios[J]. Electric Power Construction, 2020, 41(7): 67-74. doi: 10.12204/j.issn.1000-7229.2020.07.009
[14]	张翔. 含分布式电源的主动配电网规划研究[D]. 上海: 上海交通大学, 2014.
	ZHANG Xiang. Active distribution network planning with distributed generation[D]. Shanghai: Shanghai Jiao Tong University, 2014.
[15]	罗雯清. 计及风光储发电和电动汽车的配电网规划[D]. 上海: 上海交通大学, 2013.
	LUO Wenqing. Distribution network planning with wind turbine, photovoltaic system, storage system and electric vehicles[D]. Shanghai: Shanghai Jiao Tong University, 2013.
[16]	肖白, 郭蓓, 姜卓, 等. 基于负荷点聚类分区的配电网网架规划方法[J]. 电力建设, 2018, 39(11): 85-95. doi: 10.3969/j.issn.1000-7229.2018.11.011
	XIAO Bai, GUO Bei, JIANG Zhuo, et al. Distribution network planning method based on clustering blocks of load nodes[J]. Electric Power Construction, 2018, 39(11): 85-95. doi: 10.3969/j.issn.1000-7229.2018.11.011
[17]	马丽, 王宗礼, 刘伟, 等. 考虑运行灵活性的智能配电网多层优化规划[J]. 电力建设, 2018, 39(6): 71-79. doi: 10.3969/j.issn.1000-7229.2018.06.010
	MA Li, WANG Zongli, LIU Wei, et al. Research on multi-layer optimization planning method for intelligent distribution network considering operational flexibility[J]. Electric Power Construction, 2018, 39(6): 71-79. doi: 10.3969/j.issn.1000-7229.2018.06.010
[18]	KOUTSOUKIS N C, GEORGILAKIS P S. A multistage distribution network planning method considering distributed generation active management and demand response[J]. IET Renewable Power Generation, 2022, 16(1): 65-76. doi: 10.1049/rpg2.12325 URL
[19]	GAN L W, LI N, TOPCU U, et al. Exact convex relaxation of optimal power flow in radial networks[J]. IEEE Transactions on Automatic Control, 2015, 60(1): 72-87. doi: 10.1109/TAC.2014.2332712 URL
[20]	张忠会, 雷大勇, 蒋昌辉, 等. 基于二阶锥规划和NNC法的交直流混合配电网双层规划模型及其求解方法[J/OL]. 中国电机工程学报, 2022: 1-16 (2022-01-17)[2022-04-28]. http://kns.cnki.net/kcms/detail/11.2107.TM.20220114.1754.009.html.
	ZHANG Zhonghui, LEI Dayong, JIANG Changhui, et al. A bi-level planning model and its solution method of AC/DC hybrid distribution network based on second-order cone programming and NNC method[J/OL]. Proceedings of the CSEE, 2022: 1-16 (2022-01-17) [2022-04-28]. http://kns.cnki.net/kcms/detail/11.2107.TM.20220114.1754.009.html.