考虑风光消纳与电动汽车灵活性的调度策略

doi:10.19595/j.cnki.1000-6753.tces.231603

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1471 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract As a clean means of transport, electric vehicles (EVs) can improve the peak-to-valley difference of grid load and promote renewable energy consumption while facilitating users' travelling, and the scale of their application is constantly expanding. However, the disorderly charging of large-scale EVs will increase the burden on the power grid, which will have a counterproductive effect in load regulation or renewable energy consumption. The orderly charging and discharging of EVs can be effectively guided by time-of-use (TOU) price. At present, with stochastic large and some extreme situations of the renewable energy, the wind curtailment, photovoltaic curtailment and thermal power unit regulation capacity under the existing TOU price under are insufficient to lead to the larger cost of load-shedding, which remains a more prominent phenomenon. Therefore, proposing a scheduling strategy considering wind and photovoltaic power consumption and the flexibility of EVs.
Firstly, based on the wind power and photovoltaic output values and load information of each time period predicted in the day-ahead, the equivalent load of wind power, photovoltaic and base load and their average values are derived, and the peak-valley difference of the equivalent load in this time period is calculated. Based on the average value of the equivalent load and the peak-valley difference, the peak-valley price is formulated; when the equivalent load is lower than the minimum output of the thermal power unit, it will increase the phenomenon of wind and photovoltaic curtailment, resulting in a waste of resources, which will be formulated as the sunken valley price; when the equivalent load is larger than the maximum output of the thermal power unit, or the neighbouring difference in the equivalent load exceeds the ramping capacity of the thermal power unit, it will be insufficient to supply electricity, and it will be formulated as the sharp peak price. At the same time, the price elasticity coefficient is introduced to quantitatively describe the response of EVs to the sunken valley and sharp peak price.
Secondly, an EV charging and discharging model is established based on the TOU price information to determine the electricity information for EV charging and discharging scheduling. Then, an optimal EV charging and discharging scheduling model is constructed with the goal of minimizing load fluctuation and wind and photovoltaic curtailment costs, minimizing the cost of electricity for users and maximizing the revenue for the aggregator, taking into account the interests of the power grid, the EV aggregator and the EV users.
Finally, conducting an analysis of a specific power system, compared to the TOU price strategy in the peak-to-valley period, the TOU price strategy in this paper can attract more EVs to participate in the charging scheduling. The phenomenon of wind and photovoltaic curtailment can be reduced through the sunken valley TOU price in the period of renewable energy surplus. In the period of insufficient regulation capacity of thermal power unit, more EVs are attracted to participate in discharge scheduling through sharp peak TOU price to provide load resource for the power grid and reduce load shedding. Meanwhile, the price strategy can improve the revenue of aggregators and reduce the electricity cost of users while reducing the load fluctuation of power grid.

Key words： Electric vehicle load shedding time-of-use electricity price scheduling strategy

Received: 06 October 2023

PACS:

TM73

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Han Li
	Chen Shuo
	Wang Shiqi
	Cheng Yingjie

Cite this article:

Han Li,Chen Shuo,Wang Shiqi等. Scheduling Strategy Considering Wind and Photovoltaic Power Consumption and the Flexibility of Electric Vehicles[J]. Transactions of China Electrotechnical Society, 2024, 39(21): 6793-6803.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.231603 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I21/6793

[1] 王海鑫, 袁佳慧, 陈哲, 等. 智慧城市车-站-网一体化运行关键技术研究综述及展望[J]. 电工技术学报, 2022, 37(1): 112-132.
Wang Haixin, Yuan Jiahui, Chen Zhe, et al.Review and prospect of key techniques for vehicle-station-network integrated operation in smart city[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 112-132.
[2] 毛玲, 张钟浩, 赵晋斌, 等. 车-桩-网交融技术研究现状及展望[J]. 电工技术学报, 2022, 37(24): 6357-6371.
Mao Ling, Zhang Zhonghao, Zhao Jinbin, et al.Research status and prospects of fusion technology of vehicle-charging pile-power grid[J]. Transactions of China Electrotechnical Society, 2022, 37(24): 6357-6371.
[3] 何晨可, 朱继忠, 刘云, 等. 计及碳减排的电动汽车充换储一体站与主动配电网协调规划[J]. 电工技术学报, 2022, 37(1): 92-111.
He Chenke, Zhu Jizhong, Liu Yun, et al.Coordinated planning of electric vehicle charging-swapping-storage integrated station and active distribution network considering carbon reduction[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 92-111.
[4] 房宇轩, 胡俊杰, 马文帅. 计及用户意愿的电动汽车聚合商主从博弈优化调度策略[J]. 电工技术学报, 2024, 39(16): 5091-5103.
Fang Yuxuan, Hu Junjie, Ma Wenshuai.Optimal dispatch strategy for electric vehicle aggregators based on stackelberg game theory considering user intention[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5091-5103.
[5] 胡俊杰, 周华嫣然, 李阳. 集群电动汽车平抑光伏波动实时调度策略[J]. 电网技术, 2019, 43(7): 2552-2560.
Hu Junjie, Zhou Huayanran, Li Yang.Real-time dispatching strategy for aggregated electric vehicles to smooth power fluctuation of photovoltaics[J]. Power System Technology, 2019, 43(7): 2552-2560.
[6] 陈中, 刘艺, 陈轩, 等. 考虑移动储能特性的电动汽车充放电调度策略[J]. 电力系统自动化, 2020, 44(2): 77-85.
Chen Zhong, Liu Yi, Chen Xuan, et al.Charging and discharging dispatching strategy for electric vehicles considering characteristics of mobile energy storage[J]. Automation of Electric Power Systems, 2020, 44(2): 77-85.
[7] 胡俊杰, 赖信辉, 郭伟, 等. 考虑电动汽车灵活性与风电消纳的区域电网多时间尺度调度[J]. 电力系统自动化, 2022, 46(16): 52-60.
Hu Junjie, Lai Xinhui, Guo Wei, et al.Multi-time-scale scheduling for regional power grid considering flexibility of electric vehicle and wind power accommodation[J]. Automation of Electric Power Systems, 2022, 46(16): 52-60.
[8] 葛晓琳, 郝广东, 夏澍, 等. 考虑规模化电动汽车与风电接入的随机解耦协同调度[J]. 电力系统自动化, 2020, 44(4): 54-62.
Ge Xiaolin, Hao Guangdong, Xia Shu, et al.Stochastic decoupling collaborative dispatch considering integration of large-scale electric vehicles and wind power[J]. Automation of Electric Power Systems, 2020, 44(4): 54-62.
[9] Bayani R, Manshadi S D, Liu Guangyi, et al.Autonomous charging of electric vehicle fleets to enhance renewable generation dispatchability[J]. CSEE Journal of Power and Energy Systems, 2022, 8(3): 669-681.
[10] Yin Wanjun, Jia Leilei, Ji Jianbo.Energy optimal scheduling strategy considering V2G characteristics of electric vehicle[J]. Energy, 2024, 294: 130967.
[11] 史文龙, 秦文萍, 王丽彬, 等. 计及电动汽车需求和分时电价差异的区域电网LSTM调度策略[J]. 中国电机工程学报, 2022, 42(10): 3573-3587.
Shi Wenlong, Qin Wenping, Wang Libin, et al.Regional power grid LSTM dispatch strategy considering the difference between electric vehicle demand and time-of-use electricity price[J]. Proceedings of the CSEE, 2022, 42(10): 3573-3587.
[12] 张良, 孙成龙, 蔡国伟, 等. 基于PSO算法的电动汽车有序充放电两阶段优化策略[J]. 中国电机工程学报, 2022, 42(5): 1837-1852.
Zhang Liang, Sun Chenglong, Cai Guowei, et al.Two-stage optimization strategy for coordinated charging and discharging of EVs based on PSO algorithm[J]. Proceedings of the CSEE, 2022, 42(5): 1837-1852.
[13] 石文超, 吕林, 高红均, 等. 考虑需求响应和电动汽车参与的主动配电网经济调度[J]. 电力系统自动化, 2020, 44(11): 41-51.
Shi Wenchao, Lü Lin, Gao Hongjun, et al.Economic dispatch of active distribution network with participation of demand response and electric vehicle[J]. Automation of Electric Power Systems, 2020, 44(11): 41-51.
[14] 杨镜司, 秦文萍, 史文龙, 等. 基于电动汽车参与调峰定价策略的区域电网两阶段优化调度[J]. 电工技术学报, 2022, 37(1): 58-71.
Yang Jingsi, Qin Wenping, Shi Wenlong, et al.Two-stage optimal dispatching of regional power grid based on electric vehicles' participation in peak-shaving pricing strategy[J]. Transactions of China Electro-technical Society, 2022, 37(1): 58-71.
[15] Yin Wanjun, Wen Tao, Zhang Chao.Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism[J]. Energy, 2023, 263: 125627.
[16] 朱磊, 黄河, 高松, 等. 计及风电消纳的电动汽车负荷优化配置研究[J]. 中国电机工程学报, 2021, 41(增刊1): 194-203.
Zhu Lei, Huang He, Gao Song, et al.Research on optimal load allocation of electric vehicle considering wind power consumption[J]. Proceedings of the CSEE, 2021, 41(S1): 194-203.
[17] 秦祯芳, 岳顺民, 余贻鑫, 等. 零售端电力市场中的电量电价弹性矩阵[J]. 电力系统自动化, 2004, 28(5): 16-19, 24.
Qin Zhenfang, Yue Shunmin, Yu Yixin, et al.Price elasticity matrix of demand in current retail power market[J]. Automation of Electric Power Systems, 2004, 28(5): 16-19, 24.
[18] 王守相, 王瀚樟, 赵倩宇, 等. 面向配电网光伏接纳能力提升的分时电价优化方法[J]. 电力系统自动化, 2023, 47(10): 38-46.
Wang Shouxiang, Wang Hanzhang, Zhao Qianyu, et al.Optimization method of time-of-use electricity price for improving photovoltaic hosting capacity of distribution network[J]. Automation of Electric Power Systems, 2023, 47(10): 38-46.
[19] 崔杨, 修志坚, 薄利明, 等. 考虑火-储深度调峰容量二次分配的含风电电力系统分层优化调度[J]. 电网技术, 2022, 46(7): 2520-2531.
Cui Yang, Xiu Zhijian, Bo Liming, et al.Hierarchical optimal scheduling of power systems with wind generation considering secondary allocation of thermal-storage deep peaking capacity[J]. Power System Technology, 2022, 46(7): 2520-2531.
[20] Hou Hui, Wang Yifan, Chen Yue, et al.Long-time scale vehicle-to-grid scheduling strategy considering psychological effect based on Weber-Fechner law[J]. International Journal of Electrical Power & Energy Systems, 2022, 136: 107709.
[21] 宋艺航, 谭忠富, 李欢欢, 等. 促进风电消纳的发电侧、储能及需求侧联合优化模型[J]. 电网技术, 2014, 38(3): 610-615.
Song Yihang, Tan Zhongfu, Li Huanhuan, et al.An optimization model combining generation side and energy storage system with demand side to promote accommodation of wind power[J]. Power System Technology, 2014, 38(3): 610-615.
[22] 常方宇, 黄梅, 张维戈. 分时充电价格下电动汽车有序充电引导策略[J]. 电网技术, 2016, 40(9): 2609-2615.
Chang Fangyu, Huang Mei, Zhang Weige.Research on coordinated charging of electric vehicles based on TOU charging price[J]. Power System Technology, 2016, 40(9): 2609-2615.