计及电动汽车和温控负荷集群的负荷控制用户组合优化方法

doi:10.11930/j.issn.1004-9649.202402070

中国电力 ›› 2025, Vol. 58 ›› Issue (3): 86-97.DOI: 10.11930/j.issn.1004-9649.202402070

计及电动汽车和温控负荷集群的负荷控制用户组合优化方法

李思维¹^,²(), 许中平²(), 于龙², 杜立石², 岳靓², 张喜润²(), 王小明³

1. 智能电网教育部重点实验室（天津大学），天津　300072
2. 北京中电飞华通信有限公司，北京　100071
3. 国网安徽省电力有限公司电力科学研究院，安徽合肥　230061

收稿日期:2024-02-28 出版日期:2025-03-28 发布日期:2025-03-26
作者简介:
李思维（1988），男，高级工程师，从事电力系统需求响应和优化运行与控制研究，E-mail：lisiwei@sgitg.sgcc.com.cn
许中平（1973），男，高级工程师，从事电力系统信息通信技术研究，E-mail：xuzhongping@sgitg.sgcc.com.cn
张喜润（1981），男，通信作者，中级工程师，从事电力信息通信和电力负荷管理及优化调控研究，E-mail：xirunzhang@163.com
基金资助:
国家电网有限公司科技项目（面向多主体参与的负荷管理云关键技术及运行机制研究，5400-202320223A-1-1-ZN）。

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

Siwei LI¹^,²(), Zhongping XU²(), Long YU², Lishi DU², Liang YUE², Xirun ZHANG²(), Xiaoming WANG³

1. Key Laboratory of Smart Grid of Ministry of Education (Tianjin University), Tianjin 300072, China
2. Beijing Fibrlink Communications Co., Ltd., Beijing 100071, China
3. Electric Power Research Institute of State Grid Anhui Electric Power Company, Hefei 230061, China

Received:2024-02-28 Online:2025-03-28 Published:2025-03-26
Supported by:
This work is supported by Science and Technology Project of SGCC (Research on Key Technology and Operation Mechanism of Load Management Cloud for Multi-agent Participation, No.5400-202320223A-1-1-ZN).

摘要/Abstract

摘要：

随着新型电力系统建设不断深入，电力系统面临峰谷差大、波动性强的问题，利用用户侧资源参与负荷控制是解决该问题的重要举措之一。提出一种计及电动汽车和温控负荷集群的负荷控制用户组合优化方法。首先，采用分级控制的方式聚合各单体电动汽车与温控负荷集群，并根据其参与负荷控制类型的意愿分为错峰和避峰2类，分别建立错峰和避峰用户负荷调节量模型。其次，针对避峰用户参与负荷控制后出现的负荷反弹现象，构建三阶段反弹负荷模型；在此基础上考虑用户参与负荷控制的影响程度，建立负荷控制影响函数。最后，以负荷控制影响最小、电网损失最小以及负荷波动最小为多目标，优化参与错峰和避峰的用户构成以及用户负荷调节量，在满足负荷控制调控需求的同时，有效抑制参与控制后负荷反弹造成的新负荷高峰，实现分散式负荷资源与电网的良好供需互动。

关键词: 分散式负荷资源, 温控负荷集群, 负荷反弹, 负荷控制, 组合优化

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

李思维, 许中平, 于龙, 杜立石, 岳靓, 张喜润, 王小明. 计及电动汽车和温控负荷集群的负荷控制用户组合优化方法[J]. 中国电力, 2025, 58(3): 86-97.

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.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202402070

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

图/表 16

图 1 电动汽车参与负荷控制的响应特性

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

图 2 温控负荷动态特性

Fig.2 Dynamic characteristics of temperature-controlled load

图 3 单个温控负荷响应特性

Fig.3 Individual temperature-controlled load response characteristics

图 4 错峰和避峰用户参与负荷控制示意

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

图 5 负荷控制影响函数

Fig.5 Load control influence function

表 1 车辆交通用途分类

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

图 6 车辆出行时间概率分布规律

Fig.6 Probability distribution of vehicle travel time

图 7 室外温度和光照强度变化

Fig.7 Outdoor temperature and irradiation density variation

表 2 负荷控制影响函数与负荷反弹函数参数

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

图 8 负荷控制前后负荷曲线对比

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

图 9 19:00及20:00的响应容量和实际响应功率比较

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

图 10 考虑负荷反弹现象前后的负荷曲线对比

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

图 11 不考虑负荷反弹时19:00及20:00的响应容量和实际响应功率比较

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

图 12 电动汽车集群参与负荷控制前后功率对比

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

图 13 集群6参与负荷控制前后功率对比

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

表 3 不同负荷集群负荷控制结果对比

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

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计及电动汽车和温控负荷集群的负荷控制用户组合优化方法

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

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

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