Bi-level Coordinated Day-ahead Optimal Dispatch of Regional Integrated Energy System Considering the Integrations of Electric Vehicles

doi:10.12204/j.issn.1000-7229.2023.05.003

Abstract

Abstract:

Access to large-scale electric vehicles poses new challenges for the optimal dispatching of a regional integrated energy system (RIES). This study proposes a bi-level day-ahead scheduling strategy for a RIES while considering the integration of electric vehicles. First, the Monte Carlo method was used to simulate the disordered charging of electric vehicles. On this basis, the time-of-use price was used to guide the orderly charging of electric vehicles on the load side. Next, based on the electricity and carbon trading markets and fully considering the uncertainty factors in the process of wind power output on the source side and the demand-side response on the load side, the system was optimized and scheduled to minimize the daily economic and carbon trading costs. An improved particle swarm optimization algorithm was used to solve the problem. Finally, through the analysis of simulated examples, it was concluded that the bi-level optimal scheduling strategy proposed in this study can effectively reduce the peak-valley difference of the system load, improve the electricity consumption satisfaction of users, reduce carbon emissions, and increase the economic benefits of the system, while realizing the orderly charging of electric vehicles.

Key words: electric vehicles, orderly charging, time-of-use electricity price, demand-side response, bi-level day-ahead scheduling strategy

CLC Number:

TM73

LI Ling, CAO Jinye, Nikita TOMIN, YANG Dechang, ZHENG Yingying. Bi-level Coordinated Day-ahead Optimal Dispatch of Regional Integrated Energy System Considering the Integrations of Electric Vehicles[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(5): 23-33.

Figures/Tables 13

Fig.1 The basic structure of RIES

Fig.2 The uncertainty of demand-side response

Table 1 Meaning of inflection point A, B, and C

Fig.3 EV disordered charging simulation process based on Monte Carlo

Fig.4 The bi-level optimization scheduling model

Table 2 Price elasticity matrix of demand

Table 3 Rated battery parameters for EVs

Fig.5 The charging load for EVs in three scenarios

Table 4 The consumer satisfaction degree in three scenarios

Fig.6 The scheduling results in three scenarios

Table 5 The Scheduling cost in three scenarios元

Fig.7 The electric power optimization in three scenarios

Table 6 Carbon trading cost and economic benefits元

References 31

[1]	魏阳喆. 电动汽车充电对电网影响综述[J]. 时代汽车, 2022(2): 108-109, 120.
	WEI Yangzhe. Overview of the impact of electric vehicle charging on the power grid[J]. Auto Time, 2022(2): 108-109, 120.
[2]	胡福年, 徐伟成, 陈军. 计及电动汽车充电负荷的风电-光伏-光热联合系统协调调度[J]. 电力系统保护与控制, 2021, 49(13):10-20.
	HU Funian, XU Weicheng, CHEN Jun. Coordinated scheduling of wind power photovoltaic solar thermal combined system considering electric vehicle charging load[J]. Power System Protection and Control, 2021, 49(13):10-20.
[3]	蔡黎, 张权文, 代妮娜, 等. 规模化电动汽车接入主动配电网研究进展综述[J]. 智慧电力, 2021, 49(6):75-82.
	CAI Li, ZHANG Quanwen, DAI Nina, et al. Review on research progress of large-scale electric vehicle access to active distribution network[J]. Smart Power, 2021, 49(6):75-82.
[4]	陈小波, 杨秀媛. 泛在电力物联网中考虑风电消纳的电动汽车充放电控制策略研究[J]. 发电技术, 2021, 42(5):561-567. doi: 10.12096/j.2096-4528.pgt.20008
	CHEN Xiaobo, YANG Xiuyuan. Research on charging-discharging control strategy of electric vehicles considering wind power consumption in ubiquitous power internet of things[J]. Power Generation Technology, 2021, 42(5):561-567. doi: 10.12096/j.2096-4528.pgt.20008
[5]	郭豪杰, 崔双喜. 基于分时电价的电动汽车有序充电策略研究[J]. 机电工程技术, 2021, 50(10): 20-24.
	GUO Haojie, CUI Shuangxi. Study on orderly charging strategy of electric vehicles based on time-of-use electricity price[J]. Mechanical & Electrical Engineering Technology, 2021, 50(10): 20-24.
[6]	程杉, 汪业乔, 廖玮霖, 等. 含电动汽车的新能源微电网多目标分层优化调度[J]. 电力系统保护与控制, 2022, 50(12):63-71.
	CHENG Shan, WANG Yeqiao, LIAO Weilin, et al. Bi-level multi-objective optimization of a new energy microgrid with electric vehicles[J]. Power System Protection and Control, 2022, 50(12):63-71.
[7]	HAI T, ALAZZAWI A K, MOHAMAD ZAIN J, et al. A stochastic optimal scheduling of distributed energy resources with electric vehicles based on microgrid considering electricity price[J]. Sustainable Energy Technologies and Assessments, 2023, 55: 102879. doi: 10.1016/j.seta.2022.102879 URL
[8]	LIANG S, ZHU B, HE J, ET AL. A pricing strategy for electric vehicle charging in residential areas considering the uncertainty of charging time and demand[J]. Computer Communications, 2023, 199: 153-167. doi: 10.1016/j.comcom.2022.12.018 URL
[9]	YIN W, WEN T, ZHANG C. Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism[J]. Energy, 2023, 263: 125627. doi: 10.1016/j.energy.2022.125627 URL
[10]	WANG K, WANG H, YANG J, et al. Electric vehicle clusters scheduling strategy considering real-time electricity prices based on deep reinforcement learning[J]. Energy Reports, 2022, 8: 695-703. doi: 10.1016/j.egyr.2022.01.233 URL
[11]	余桂华, 姚海燕, 崔金栋, 等. 基于动态分时电价需求响应下电动汽车有序充电方法[J]. 农村电气化, 2021(8): 57-60.
	YU Guihua, YAO Haiyan, CUI Jindong, et al. Order charging method research of electrical vehicle based on dynamic distributed time electricity cost need answer[J]. Rural Electrification, 2021(8): 57-60.
[12]	SUBRAMANIAN V, DAS T K. A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes[J]. Energy, 2019, 167: 1266-1277. doi: 10.1016/j.energy.2018.10.171 URL
[13]	ZHANG X, GOU H X, XIE Y Q. Research on orderly charging of electric vehicles considering random fuzzy under smart grid[C]// 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering. March 18-20, 2022. Xishuangbanna, China. SPIE, 2022.
[14]	ROCHA S P D, SILVA S M D, EKEL P I. Fuzzy set-based approach for grid integration and operation of ultra-fast charging electric buses[J]. International Journal of Electrical Power & Energy Systems, 2022, 138: 107919. doi: 10.1016/j.ijepes.2021.107919 URL
[15]	宋晓通, 吕倩楠, 孙艺, 等. 基于电价引导的电动汽车与综合能源系统交互策略[J]. 高电压技术, 2021, 47(10): 3744-3756.
	SONG Xiaotong, LÜ Qiannan, SUN Yi, et al. Interactive strategy of electric vehicles and integrated energy system based on electricity price guidance[J]. High Voltage Engineering, 2021, 47(10): 3744-3756.
[16]	王扬, 雷小林, 唐红艳. 考虑用户响应度的电动汽车有序充放电策略[J]. 东北电力技术, 2019, 40(9): 1-6.
	WANG Yang, LEI Xiaolin, TANG Hongyan. Orderly charge and discharge strategy for electric vehicle considering demand response of users[J]. Northeast Electric Power Technology, 2019, 40(9): 1-6.
[17]	王杰, 唐菁敏, 刘思淼. 基于用户响应度的电动汽车有序充放电策略[J]. 电子测量技术, 2021, 44(1): 31-36.
	WANG Jie, TANG Jingmin, LIU Simiao. Orderly charging and discharging strategy of electric vehicles based on user responsiveness[J]. Electronic Measurement Technology, 2021, 44(1): 31-36.
[18]	YIN Wanjun, MING Zhengfeng, WEN Tao. Scheduling strategy of electric vehicle charging considering different requirements of grid and users[J]. Energy, 2021, 232: 121118. doi: 10.1016/j.energy.2021.121118 URL
[19]	粟世玮, 杨玄, 曹申, 等. 考虑新能源消纳的电动汽车有序充电研究[J]. 三峡大学学报(自然科学版), 2019, 41(5): 84-89.
	SU Shiwei, YANG Xuan, CAO Shen, et al. Research on coordinated charging for electric vehicles considering new energy accommodation[J]. Journal of China Three Gorges University (Natural Sciences), 2019, 41(5): 84-89.
[20]	段俊东, 李高尚, 李一石, 等. 考虑风电消纳的电动汽车充电站有序充电控制[J]. 储能科学与技术, 2021, 10(2): 630-637.
	DUAN Jundong, LI Gaoshang, LI Yishi, et al. Coordinated charging control for EV charging stations considering wind power accommodation[J]. Energy Storage Science and Technology, 2021, 10(2): 630-637.
[21]	王毅, 谭仕奇. 考虑光伏出力不确定性的电动汽车充放电策略[J]. 重庆理工大学学报(自然科学), 2021, 35(9): 50-57.
	WANG Yi, TAN Shiqi. A charging and discharging strategy for electric vehicles with photovoltaic output uncertainty being considered[J]. Journal of Chongqing University of Technology (Natural Science), 2021, 35(9): 50-57.
[22]	FENG J W, YANG J Y, WANG H X, et al. Flexible optimal scheduling of power system based on renewable energy and electric vehicles[J]. Energy Reports, 2022, 8: 1414-1422. doi: 10.1016/j.egyr.2021.11.065 URL
[23]	欧名勇, 陈仲伟, 谭玉东, 等. 基于峰谷分时电价引导下的电动汽车充电负荷优化[J]. 电力科学与技术学报, 2020, 35(5): 54-59.
	OU Mingyong, CHEN Zhongwei, TAN Yudong, et al. Optimization of electric vehicle charging load based on peak-to-valley time-of-use electricity price[J]. Journal of Electric Power Science and Technology, 2020, 35(5): 54-59.
[24]	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.
[25]	史亮, 葛晓琳, 顾闻, 等. 考虑需求响应的电动汽车充电负荷研究[J]. 电测与仪表, 2022, 59(7): 42-47.
	SHI Liang, GE Xiaolin, GU Wen, et al. Research on charging loads of electric vehicles considering demand response[J]. Electrical Measurement & Instrumentation, 2022, 59(7): 42-47.
[26]	王泽森, 石岩, 唐艳梅, 等. 考虑LCA能源链与碳交易机制的综合能源系统低碳经济运行及能效分析[J]. 中国电机工程学报, 2019, 39(6): 1614-1626, 1858.
	WANG Zesen, SHI Yan, TANG Yanmei, et al. Low carbon economy operation and energy efficiency analysis of integrated energy systems considering LCA energy chain and carbon trading mechanism[J]. Proceedings of the CSEE, 2019, 39(6): 1614-1626, 1858.
[27]	张晓辉, 刘小琰, 钟嘉庆. 考虑奖惩阶梯型碳交易和电-热转移负荷不确定性的综合能源系统规划[J]. 中国电机工程学报, 2020, 40(19): 6132-6142.
	ZHANG Xiaohui, LIU Xiaoyan, ZHONG Jiaqing. Integrated energy system planning considering a reward and punishment ladder-type carbon trading and electric-thermal transfer load uncertainty[J]. Proceedings of the CSEE, 2020, 40(19): 6132-6142.
[28]	吴永红, 曾志高, 邓彬. 基于惯性权重和学习因子动态调整的粒子群算法[J]. 湖南工业大学学报, 2021, 35(1): 91-96.
	WU Yonghong, ZENG Zhigao, DENG Bin. Particle swarm optimization algorithm based on dynamic adjustment of inertial weight and learning factors[J]. Journal of Hunan University of Technology, 2021, 35(1): 91-96.
[29]	董飞飞, 俞登科. 考虑需求侧响应的微电网经济优化调度[J]. 电网与清洁能源, 2020, 36(8): 55-59.
	DONG Feifei, YU Dengke. Economic dispatch of microgrid considering demand side response[J]. Power System and Clean Energy, 2020, 36(8): 55-59.
[30]	赵贺, 李子矝, 闫浩然, 等. 基于模型预测控制的微电网经济调度[J]. 电网与清洁能源, 2017, 33(9): 119-123.
	ZHAO He, LI Ziqin, YAN Haoran, et al. Economical dispatch of microgrid based on model predictive control[J]. Power System and Clean Energy, 2017, 33(9): 119-123.
[31]	杨柳, 张超, 蒋勃, 等. 考虑用户满意度的虚拟电厂热电联合低碳经济调度模型[J]. 热力发电, 2019, 48(9): 40-45.
	YANG Liu, ZHANG Chao, JIANG Bo, et al. A combined low-carbon economic dispatching model for virtual power plant considering customer satisfaction index[J]. Thermal Power Generation, 2019, 48(9): 40-45.