Optimal Scheduling of Integrated Energy System Considering Integration of Electric Vehicles and Load Aggregators

doi:10.16183/j.cnki.jsjtu.2022.029

Abstract

Abstract:

Fully tapping into the role of user side regulation helps reduce the energy cost of integrated energy system (IES). Demand response (DR) and electric vehicle (EV) as schedulable resources on the user side are important regulation means for optimal scheduling of IES. However, in the actual operation process, due to the influence of load aggregator (LA) economic incentives and EV travel, the economic impact of the uncertainty of user side DR on IES cannot be ignored. Based on this, this paper proposes an IES optimal operation model considering the robust stochastic optimization of EV and the participation of LA which considers the energy purchase cost of IES from the superior network and the economic loss cost of LA. First, the response rate model and EV uncertainty model based on economic incentive are constructed. Then, the robust optimization model of EV is built, and the load demand of EV travel uncertainty is analyzed. Finally, a simulation example is given to analyze the impact of user DR uncertainty and EV uncertainty on IES operation economy and power balance. The simulation results show that considering the uncertainty of DR and EV can optimize the economic operation of IES and reduce the economic loss of LA and the total cost, which verifies the effectiveness and economy of the proposed models.

Key words: integrated energy system (IES), load aggregator (LA), demand response (DR), electric vehicle (EV), uncertainty

CLC Number:

TM732

WANG Jing, XING Haijun, WANG Huaxin, PENG Sijia. Optimal Scheduling of Integrated Energy System Considering Integration of Electric Vehicles and Load Aggregators[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 814-823.

Figures/Tables 15

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Tab.1

Tab.2

Data of each equipment

装置	参数
CHP	$P C H P m a x$ =2 000 kW, $η e C H P$ =0.35, $η h C H P$ =0.4
GB	$P G B m a x$ =800 kW, $η h G B$ =0.85
EC	$P E C m a x$ =400 kW, COP_EC=4.0
AR	$P A R m a x$ =1 000 kW, COP_AR=0.8
HST	$E h m a x$ =500 kW·h, $E h m i n$ =50 kW·h
	$P H S T m a x$ =125 kW, η_h,ch=η_h,dis=0.96

Tab.2

Tab.3

Tab.4

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References 26

[1]	丁煜蓉, 陈红坤, 吴军, 等. 计及综合能效的电-气-热综合能源系统多目标优化调度[J]. 电力系统自动化, 2021, 45(2): 64-73.
	DING Yurong, CHEN Hongkun, WU Jun, et al. Multi-objective optimal dispatch of electricity-gas-heat integrated energy system considering comprehensive energy efficiency[J]. Automation of Electric Power Systems, 2021, 45(2): 64-73.
[2]	刘文霞, 李征洲, 杨粤, 等. 计及需求响应不确定性的综合能源系统协同优化配置[J]. 电力系统自动化, 2020, 44(10): 41-49.
	LIU Wenxia, LI Zhengzhou, YANG Yue, et al. Collaborative optimal configuration for integrated energy system considering uncertainties of demand response[J]. Automation of Electric Power System, 2020, 44(10): 41-49.
[3]	WANG Y L, MA Y Z, SONG F H, et al. Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response[J]. Energy, 2020, 205: 118022. doi: 10.1016/j.energy.2020.118022 URL
[4]	王旭. 考虑电动汽车接入的配电网优化运行研究[D]. 济南: 山东大学, 2020.
	WANG Xu. Research on optimal operation for distribution network with electric vehicle[D]. Jinan: Shandong University, 2020.
[5]	许刚, 张丙旭, 张广超. 电动汽车集群并网的分布式鲁棒优化调度模型[J]. 电工技术学报, 2021, 36(3): 565-578.
	XU Gang, ZHANG Bingxu, ZHANG Guangchao. Distributed and robust optimal scheduling model for large-scale electric vehicles connected to grid[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 565-578.
[6]	杨璐. 面向不确定性充电需求的电动出租车充电站选址模型研究[D]. 哈尔滨: 哈尔滨工业大学, 2020.
	YANG Lu. Research on location model of electric taxi charging station for uncertain charging demand[D]. Harbin: Harbin Institute of Technology, 2020.
[7]	YANG H M, ZHANG J, QIU J, et al. A practical pricing approach to smart grid demand response based on load classification[J]. IEEE Transactions on Smart Grid, 2018, 9(1): 179-190. doi: 10.1109/TSG.2016.2547883 URL
[8]	彭巧, 王秀丽, 邵成成, 等. 计及信息间隙决策理论的含电动汽车充电负荷的微电网多目标规划[J]. 电力自动化设备, 2021, 41(1): 128-134.
	PENG Qiao, WANG Xiuli, SHAO Chengcheng, et al. Multi-objective planning of microgrid with electric vehicle charging load based on information gap decision theory[J]. Electric Power Automation Equipment, 2021, 41(1): 128-134.
[9]	葛晓琳, 史亮, 刘亚, 等. 考虑需求响应不确定性的电动汽车负荷Sigmoid云模型预测[J]. 中国电机工程学报, 2020, 40(21): 6913-6925.
	GE Xiaolin, SHI Liang, LIU Ya, et al. Load forecasting of electric vehicles based on sigmoid cloud model considering the uncertainty of demand response[J]. Proceedings of the CSEE, 2020, 40(21): 6913-6925.
[10]	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, LYU 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.
[11]	贾雨龙, 米增强, 余洋, 等. 计及不确定性的柔性负荷聚合商随机-鲁棒投标决策模型[J]. 电工技术学报, 2019, 34(19): 4096-4107.
	JIA Yulong, MI Zengqiang, YU Yang, et al. Stochastic-robust decision-making model for flexible load aggregator considering uncertainties[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4096-4107.
[12]	PAN G S, GU W, WU Z, et al. Optimal design and operation of multi-energy system with load aggregator considering nodal energy prices[J]. Applied Energy, 2019, 239: 280-295. doi: 10.1016/j.apenergy.2019.01.217 URL
[13]	吴宛潞, 韩帅, 孙乐平, 等. 负荷聚合商多类型需求侧资源激励价格制定一般模型及应用[J]. 电力建设, 2021, 42(1): 1-9. doi: 10.12204/j.issn.1000-7229.2021.01.001
	WU Wanlu, HAN Shuai, SUN Leping, et al. A general incentive pricing model and its application for multi-type demand-side resources of load aggregators[J]. Electric Power Construction, 2021, 42(1): 1-9. doi: 10.12204/j.issn.1000-7229.2021.01.001
[14]	刘珮云, 丁涛, 贺元康, 等. 基于综合需求响应的负荷聚合商最优市场交易策略[J]. 电力自动化设备, 2019, 39(8): 224-231.
	LIU Peiyun, DING Tao, HE Yuankang, et al. Optimal trading strategy for load aggregator based on integrated demand response[J]. Electric Power Automation Equipment, 2019, 39(8): 224-231.
[15]	LI C Y, ZHAO R S, WANG D, et al. Optimal spatio-temporal scheduling for Electric Vehicles and Load Aggregators considering response reliability[J]. Electric Power Systems Research, 2018, 162: 183-193. doi: 10.1016/j.epsr.2018.05.007 URL
[16]	JIN H Y, TENG Y, ZHANG T Y, et al. A deep neural network coordination model for electric heating and cooling loads based on IoT data[J]. CSEE Journal of Power & Energy Systems, 2020, 6(1): 22-30.
[17]	郭亦宗, 冯斌, 岳铂雄, 等. 负荷聚合商模式下考虑需求响应的超短期负荷预测[J]. 电力系统自动化, 2021, 45(1): 79-87.
	GUO Yizong, FENG Bin, YUE Boxiong, et al. Ultra-short-term load forecasting considering demand response in load aggregator mode[J]. Automation of Electric Power Systems, 2021, 45(1): 79-87.
[18]	任惠, 陆海涛, 卢锦玲, 等. 考虑信息物理系统耦合和用户响应差异的负荷聚合商需求响应特性分析[J]. 电网技术, 2020, 44(10): 3927-3936.
	REN Hui, LU Haitao, LU Jinling, et al. Analysis of LA demand response characteristics considering cyber physical system coupling and user’s response difference[J]. Power System Technology, 2020, 44(10): 3927-3936.
[19]	余涛. 电动汽车有序充放电策略与鲁棒优化调度[D]. 长沙: 湖南大学, 2020.
	YU Tao. Orderly charging and discharging strategy and robust optimal scheduling of electric vehicles[D]. Changsha: Hunan University, 2020.
[20]	邵尹池, 穆云飞, 林佳颖, 等. “车-站-网”多元需求下的电动汽车快速充电引导策略[J]. 电力系统自动化, 2019, 43(18): 60-66.
	SHAO Yinchi, MU Yunfei, LIN Jiaying, et al. Fast charging guidance strategy for multiple demands of electric vehicle, fast charging station and distribution network[J]. Automation of Electric Power System, 2019, 43(18): 60-66.
[21]	孙宇军, 王岩, 王蓓蓓, 等. 考虑需求响应不确定性的多时间尺度源荷互动决策方法[J]. 电力系统自动化, 2018, 42(2): 106-113.
	SUN Yujun, WANG Yan, WANG Beibei, et al. Multi-time scale decision method for source-load interaction considering demand response uncertainty[J]. Automation of Electric Power System, 2018, 42(2): 106-113.
[22]	王瑾然, 卫志农, 张勇, 等. 计及不确定性的区域综合能源系统日前多目标优化调度[J]. 电网技术, 2018, 42(11): 3496-3506.
	WANG Jinran, WEI Zhinong, ZHANG Yong, et al. Multi-objective optional day-ahead dispatching for regional integrated energy system considering uncertainty[J]. Power System Technology, 2018, 42(11): 3496-3506.
[23]	胡枭, 尚策, 陈东文, 等. 考虑能量品质的区域综合能源系统多目标规划方法[J]. 电力系统自动化, 2019, 43(19): 22-31.
	HU Xiao, SHANG Ce, CHEN Dongwen, et al. Multi-objective planning method for regional integrated energy systems considering energy quality[J]. Automation of Electric Power System, 2019, 43(19): 22-31.
[24]	潘华, 梁作放, 肖雨涵, 等. 多场景下区域综合能源系统的优化运行[J]. 太阳能学报, 2021, 42(1): 484-492.
	PAN Hua, LIANG Zuofang, XIAO Yuhan, et al. Optimal operation of regional integrated energy system under multiple scenes[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 484-492.
[25]	许周, 孙永辉, 谢东亮, 等. 计及电/热柔性负荷的区域综合能源系统储能优化配置[J]. 电力系统自动化, 2020, 44(2): 53-59.
	XU Zhou, SUN Yonghui, XIE Dongliang, et al. Optimal configuration of energy storage for integrated region energy system considering power/thermal flexible load[J]. Automation of Electric Power Systems, 2020, 44(2): 53-59.
[26]	蒋文超, 严正, 曹佳, 等. 计及柔性负荷的能源枢纽多目标综合优化调度[J]. 电测与仪表, 2018, 55(13): 31-39.
	JIANG Wenchao, YAN Zheng, CAO Jia, et al. Multi-objective comprehensive optimal dispatch of energy hub considering flexible load[J]. Electrical Measurement & Instrumentation, 2018, 55(13): 31-39.

用电负荷	时段	价格/[元·(kW·h)^-1]
峰	8:00—11:00	1.2
	18:00—21:00	1.2
平	6:00—8:00	0.75
	12:00—14:00	0.75
谷	0:00—5:00	0.35
	15:00—17:00	0.35
	22:00—24:00	0.35

场景	DR不确定性	EV不确定性
1	√	×
2	×	√
3	×	×
4	√	√

场景	成本
场景	购能成本× 10^-4/元	LA经济损失成本×10^-4/元	EV充放电成本/元	总成本× 10^-4/元
1	117.14	5.41	1 169.58	122.66
2	116.55	5.85	1 115.60	122.52
3	119.04	4.84	1 210.76	124.01
4	116.25	4.13	1 097.32	121.38