A Load Control User Combinatorial Optimization Method Considering Electric Vehicle and Temperature-Controlled Load Clusters

doi:10.11930/j.issn.1004-9649.202402070

Abstract

Abstract:

As the construction of the new power system continues to deepen, the power system faces such problems as large peak-to-valley difference and high volatility, and the use of user-side resources to participate in load control is one of the important initiatives to solve the above-said problems. In this paper, a load control user combinatorial optimization method considering electric vehicle (EV) and temperature-controlled load clusters is proposed. Firstly, a hierarchical control method is used to aggregate individual EVs and temperature-controlled load clusters, and the aggregated clusters are divided into peak load shifting type and peak load shedding type according to their willingness to participate in load control types, and their respective user load control models are established. Secondly, a three-stage rebound load model is constructed to solve the load rebound problem after peak load shifting users participate in load control. And then, a load control influence function is established with consideration of the influence degree of users participating in load control. Finally, the composition of user groups participating in peak load shifting and shedding and the adjustment amount of user load are optimized with the minimum load control influence, minimum network loss and minimum load fluctuation as multi-objectives. While meeting the demand of load control, the proposed method can effectively inhibit the new peak load caused by the rebound of load after users participating in load control, as a result, realizing the good interaction of supply and demand between distributed load resources and the power system.

Key words: distributed load resources, temperature control load cluster, load rebound, load control, combinatorial optimization

Siwei LI, Zhongping XU, Long YU, Lishi DU, Liang YUE, Xirun ZHANG, Xiaoming WANG. A Load Control User Combinatorial Optimization Method Considering Electric Vehicle and Temperature-Controlled Load Clusters[J]. Electric Power, 2025, 58(3): 86-97.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202402070

https://www.electricpower.com.cn/EN/Y2025/V58/I3/86

Figures/Tables 16

Fig.1 Response characteristics of electric vehicles participating in load control

Fig.2 Dynamic characteristics of temperature-controlled load

Fig.3 Individual temperature-controlled load response characteristics

Fig.4 Load control of users participating in peak load shifting and shedding

Fig.5 Load control influence function

Table 1 Classification of vehicle traffic uses

车辆类型		交通用途
HBW(Home-Based-Work)		私家车，主要用于往返交通
HBO(Home-Based-Other)		私家车，主要为退休和待业人员使用
NHB(Non-Home-Based)		非家用车辆，如公司车辆、公共交通等

Fig.6 Probability distribution of vehicle travel time

Fig.7 Outdoor temperature and irradiation density variation

Table 2 Parameters of load control influence function and load rebound function

参数		取值
${a_1}$/(元·kW^–1)		200
${a_2}$/(元·kW^–1)		10
${a_3}$/(元·kW^–1)		500
${a_4}$/(元·kW^–1)		100
${a_5}$/(元·kW^–1)		1000
$ \alpha $		0.6
$ \beta $		0.3
$ \chi $		0.1
$ {\varphi _1} $		0.2
$ {\varphi _2} $		0.3
$ {\varphi _3} $		0.5

Fig.8 Comparison of load curves before and after load control

Fig.9 Comparison of response capacity and actual response power at 19:00 and 20:00

Fig.10 Comparison of load curves before and after considering load rebound phenomenon

Fig.11 Comparison of response capacity and actual response power at 19:00 and 20:00 without considering load rebound

Fig.12 Power comparison of electric vehicle clusters before and after participating in load control

Fig.13 Power comparison of load clusters 6 before and after participating in load control

Table 3 Comparison of load control results for different load clusters 单位：元

类型	用户损失	电网损失	负荷反弹损失	成本总计
10个电动汽车集群	42753	3175	51019	35012
10个温控负荷集群	74334	7590	86550	60418
5个温控负荷集群+ 5个电动汽车集群	56276	5487	43106	34454

References 25

1	徐韵, 徐耀杰, 杨嘉禹, 等. 基于阶梯碳交易的电转气虚拟电厂低碳经济调度[J]. 电力系统及其自动化学报, 2023, 35 (7): 118- 128.
	XU Yun, XU Yaojie, YANG Jiayu, et al. Low carbon economic dispatching for power-to-gas virtual power plant based on stepped carbon trading[J]. Proceedings of the CSU-EPSA, 2023, 35 (7): 118- 128.
2	舒印彪, 赵勇, 赵良, 等. “双碳” 目标下我国能源电力低碳转型路径[J]. 中国电机工程学报, 2023, 43 (5): 1663- 1672.
	SHU Yinbiao, ZHAO Yong, ZHAO Liang, et al. Study on low carbon energy transition path toward carbon peak and carbon neutrality[J]. Proceedings of the CSEE, 2023, 43 (5): 1663- 1672.
3	原希尧, 王关涛, 朱若源, 等. 碳-绿色证书交易机制下考虑回收P2G余热和需求响应的PIES优化调度[J]. 电力建设, 2023, 44 (3): 25- 35.
	YUAN Xiyao, WANG Guantao, ZHU Ruoyuan, et al. Optimal scheduling of park integrated energy system with P2G waste heat recovery and demand response under carbon-green certificate trading mechanism[J]. Electric Power Construction, 2023, 44 (3): 25- 35.
4	章雷其, 谭彩霞, 赵波, 等. 考虑子系统特性的分布式电氢耦合系统多时间尺度优化[J]. 电力建设, 2023, 44 (9): 118- 128.
	ZHANG Leiqi, TAN Caixia, ZHAO Bo, et al. Multi-time-scale operational optimization of a distributed electro-hydrogen coupling system considering subsystem characteristics[J]. Electric Power Construction, 2023, 44 (9): 118- 128.
5	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42 (8): 2806- 2819.
	ZHANG Zhigang, KANG Chongqing. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42 (8): 2806- 2819.
6	胡长斌, 蔡晓钦, 赵鑫宇, 等. 含光热电站及碳交易机制下的电气热多能流耦合系统分层优化运行[J]. 电力建设, 2024, 45 (3): 27- 38.
	HU Changbin, CAI Xiaoqin, ZHAO Xinyu, et al. Optimized operation of electric thermal multi-energy flow coupling system under concentrating solar power plant and carbon trading mechanism[J]. Electric Power Construction, 2024, 45 (3): 27- 38.
7	赵杰, 王聪, 李冠冠, 等. 考虑需求响应的多微网P2P能源交易低碳运行策略[J]. 电力建设, 2023, 44 (12): 54- 65.
	ZHAO Jie, WANG Cong, LI Guanguan, et al. Low-carbon operation strategy for P2P energy trading among multiple microgrids considering demand response[J]. Electric Power Construction, 2023, 44 (12): 54- 65.
8	宋大为, 尹硕, 何洋, 等. 基于虚拟电厂的多元小微主体参与现货市场的竞价策略[J]. 南方电网技术, 2021, 15 (9): 75- 84.
	SONG Dawei, YIN Shuo, HE Yang, et al. Bidding strategy of multiple small and micro entities participating in the spot market based on virtual power plant[J]. Southern Power System Technology, 2021, 15 (9): 75- 84.
9	陆婷婷, 高赐威, 苏卫华, 等. 有序用电避峰预案优化编制方法研究[J]. 电网技术, 2014, 38 (9): 2315- 2321.
	LU Tingting, GAO Ciwei, SU Weihua, et al. Research on drafting of optimal peak averting planning for ordered power utilization[J]. Power System Technology, 2014, 38 (9): 2315- 2321.
10	张粒子, 杨阳. 基于能效标尺竞争与OaR的工业负荷管理策略[J]. 电力自动化设备, 2013, 33 (3): 9- 14. DOI
	ZHANG Lizi, YANG Yang. Industrial load management based on energy efficiency yardstick competition and OaR[J]. Electric Power Automation Equipment, 2013, 33 (3): 9- 14. DOI
11	颜庆国, 童星, 张宁, 等. 考虑用户节电损失的有序用电动态多级时空协调方法[J]. 电网技术, 2016, 40 (2): 425- 432.
	YAN Qingguo, TONG Xing, ZHANG Ning, et al. A dynamic multi-level temporal and spatial coordination method for orderly power utilization based on customer load curtailment cost characteristics[J]. Power System Technology, 2016, 40 (2): 425- 432.
12	徐青山, 丁一帆, 颜庆国, 等. 大用户负荷调控潜力及价值评估研究[J]. 中国电机工程学报, 2017, 37 (23): 6791- 6800, 7070.
	XU Qingshan, DING Yifan, YAN Qingguo, et al. Research on evaluation of scheduling potentials and values on large consumers[J]. Proceedings of the CSEE, 2017, 37 (23): 6791- 6800, 7070.
13	高岩. 基于需求侧管理实时电价优化方法综述[J]. 上海理工大学学报, 2022, 44 (2): 103- 111,121.
	GAO Yan. A review on real-time electricity price optimization methods based on demand side management[J]. Journal of University of Shanghai for Science and Technology, 2022, 44 (2): 103- 111,121.
14	郑若楠, 李志浩, 唐雅洁, 等. 考虑居民用户参与度不确定性的激励型需求响应模型与评估[J]. 电力系统自动化, 2022, 46 (8): 154- 162. DOI
	ZHENG Ruonan, LI Zhihao, TANG Yajie, et al. Incentive demand response model and evaluation considering uncertainty of residential customer participation degree[J]. Automation of Electric Power Systems, 2022, 46 (8): 154- 162. DOI
15	甘海庆, 朱竞, 马琎劼, 等. 考虑产业链的有序用电轮休方案制定策略[J]. 电力需求侧管理, 2023, 25 (4): 93- 98. DOI
	GAN Haiqing, ZHU Jing, MA Jinjie, et al. Formulating strategy for orderly electricity rotation scheme considering industrial chain[J]. Power Demand Side Management, 2023, 25 (4): 93- 98. DOI
16	王伊宁, 丁坚勇, 田世明, 等. 基于智能多代理属地管理系统的有序用电模式[J]. 电网技术, 2019, 43 (5): 1802- 1814.
	WANG Yining, DING Jianyong, TIAN Shiming, et al. An orderly power utilization mode based on intelligent multi-agent apanage management system[J]. Power System Technology, 2019, 43 (5): 1802- 1814.
17	HSU Y Y, SU C C. Dispatch of direct load control using dynamic programming[J]. IEEE Transactions on Power Systems, 1991, 6 (3): 1056- 1061.
18	陈艺灵, 吕志鹏, 周珊. 基于云边端协同的电动汽车多目标优化调度[J]. 供用电, 2022, 39 (4): 17- 24.
	CHEN Yiling, LV Zhipeng, ZHOU Shan. Multi-objective optimal scheduling of electric vehicles based on cloud edge end cooperation[J]. Distribution & Utilization, 2022, 39 (4): 17- 24.
19	陈文哲, 孙海顺, 徐瑞林, 等. 电动汽车参与调频服务的云边融合分层调控技术研究[J]. 中国电机工程学报, 2023, 43 (3): 914- 927.
	CHEN Wenzhe, SUN Haishun, XU Ruilin, et al. Cloud-edge collaboration based hierarchical dispatch technology for EV participating in frequency regulation service[J]. proceedings of the CSEE, 2023, 43 (3): 914- 927.
20	姜婷玉, 李亚平, 江叶峰, 等. 温控负荷提供电力系统辅助服务的关键技术综述[J]. 电力系统自动化, 2022, 46 (11): 191- 207. DOI
	JIANG Tingyu, LI Yaping, JIANG Yefeng, et al. Review on key technologies for providing auxiliary services to power system by thermostatically controlled loads[J]. Automation of Electric Power Systems, 2022, 46 (11): 191- 207. DOI
21	李力, 董密, 宋冬然, 等. 分布式的温控负荷集群负荷跟随控制[J]. 中国电机工程学报, 2023, 43 (21): 8270- 8282.
	LI Li, DONG Mi, SONG Dongran, et al. Distributed load-following control for thermostatically controlled loads clusters[J]. Proceedings of the CSEE, 2023, 43 (21): 8270- 8282.
22	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. DOI
	SHI Wenchao, LV 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. DOI
23	徐俊俊, 程奕凌, 张腾飞, 等. 计及充电行为特征与可调性的电动汽车集群优化调度[J]. 电力系统自动化, 2023, 47 (23): 23- 32. DOI
	XU Junjun, CHENG Yiling, ZHANG Tengfei, et al. Optimal scheduling of electric vehicle clusters considering characteristics and adjustability of charging behavior[J]. Automation of Electric Power Systems, 2023, 47 (23): 23- 32. DOI
24	Chen L, Zhu X, Cai J, et al. Multi-time scale coordinated optimal dispatch of microgrid cluster based on MAS[J]. Electric Power Systems Research, 2019, 177, 105976. DOI
25	ZHANG J R, MU Y F, WU Z Q, et al. Optimal scheduling method of regenerative electric heating for emergency residential building heating: an affine arithmetic-based model predictive control approach[J]. IET Energy Systems Integration, 2023, 5 (1): 40- 53. DOI

[1]	WU Tong, HUI Hongxun, ZHANG Hongcai. Review of Commercial Air Conditioners for Participating in Urban Grid Regulation [J]. Electric Power, 2023, 56(7): 1-11.
[2]	YANG Lei, WANG Zhichao, ZHOU Xin, LI Shengnan, HE Peng, XIANG Chuan, ZHANG Jie, WANG Delin. Frequency Stability Control Strategy for Large-scale Grid Connections with DFIG Units [J]. Electric Power, 2021, 54(5): 186-194.
[3]	WANG Yan, JIANG Jing, HE Hengjing, GAO Ciwei, XIAO Yong. Multi-scenario Load Combinatorial Optimization Based on Improved Greedy Algorithm [J]. Electric Power, 2020, 53(5): 1-9.
[4]	JIANG Huilan, QIAN Guangchao, FAN Zhonglin, CHEN Juan. A Service Restoration Strategy Considering Influence of Load Management for Distribution System with DG [J]. Electric Power, 2017, 50(3): 101-106.
[5]	AI Xin, ZHAO Yuequn, ZHOU Shupeng. Study on Aggregate Electric Vehicle Charging Load Model and Simulation for Clean Energy Accommodation in Distribution Network [J]. Electric Power, 2016, 49(6): 170-175.
[6]	NIU Honghai, LI Bing, CHEN Jun. R&D and Application of Plant-Wide Load Control System for Thermal Power Plants [J]. Electric Power, 2016, 49(12): 96-100.
[7]	YANG Zhen-li. Solutions to Generation Output Constrained by Power Line Transmission Capabilities [J]. Electric Power, 2014, 47(9): 18-24.