动态电价下计及能量回收的电动汽车负荷时空引导策略

doi:10.11930/j.issn.1004-9649.202501053

中国电力 ›› 2025, Vol. 58 ›› Issue (11): 122-134.DOI: 10.11930/j.issn.1004-9649.202501053

• 电-碳协同下分布式能源系统运营关键技术 • 上一篇下一篇

动态电价下计及能量回收的电动汽车负荷时空引导策略

杨新桥¹(), 加鹤萍¹(), 李培军², 李顺³, 龙羿³, 刘敦楠¹, 黄辉¹

1. 华北电力大学经济与管理学院，北京　102206
2. 国网智慧车联网技术有限公司，北京　100052
3. 国网重庆市电力公司营销服务中心，重庆　400014

收稿日期:2025-01-20 修回日期:2025-04-23 发布日期:2025-12-01 出版日期:2025-11-28
作者简介:
杨新桥（2000），男，硕士研究生，从事车网互动研究，E-mail：fengba80418@163.com
加鹤萍（1992），女，通信作者，副教授，从事虚拟电厂、车网互动及电力市场研究，E-mail：jiaheping@ncepu.edu.cn
基金资助:
国家电网公司科技项目（5400-202427221A-1-1-ZN）。

Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing

YANG Xinqiao¹(), JIA Heping¹(), LI Peijun², LI Shun³, LONG Yi³, LIU Dunnan¹, HUANG Hui¹

1. School of Economics and Management, North China Electric Power University, Beijing 102206, China
2. State Grid Smart Internet of Vehicles Co., Ltd., Beijing 100052, China
3. Marketing Service Center, State Grid Chongqing Electric Power Company, Chongqing 400014, China

Received:2025-01-20 Revised:2025-04-23 Online:2025-12-01 Published:2025-11-28
Supported by:
This work is supported by Science and Technology Project of State Grid Corporation of China (No.5400-202427221A-1-1-ZN).

摘要/Abstract

摘要：

随着电动汽车的规模化发展，高不确定性的电动汽车无序接入电网会加剧电网峰谷差，有必要通过动态电价的引导，提高电动汽车灵活调节能力。此外，广泛应用于电动汽车的能量回收技术有助于减少能量损耗，降低碳排放。为此，提出动态电价下考虑能量回收的电动汽车负荷时空引导策略。首先，基于城市交通网络拓扑图，建立考虑城市功能区的电动汽车出行链模型。然后，考虑电动汽车制动能量回收，基于蒙特卡洛模拟方法构建电动汽车负荷时空分布模型，并引入考虑用户连续充电行为的充电效用函数，建立考虑配电网负荷波动最小和电动汽车用户充电成本最小的主从博弈模型，实现动态电价机制下的计及能量回收的电动汽车负荷时空引导。最后，通过算例验证该方法对电动汽车负荷时空分布引导的有效性。

关键词: 电动汽车, 时空分布, 动态电价, 能量回收, 主从博弈模型

Abstract:

With the large-scale development of electric vehicles, the disorderly access of high-uncertainty electric vehicles to the grid will exacerbate the peak-valley difference of the grid, and it is necessary to improve the flexible adjustment ability of electric vehicles through the guidance of dynamic pricing. In addition, the energy recovery technology widely used in electric vehicles helps to reduce energy loss and carbon emission. This paper proposes a spatio-temporal guidance strategy for electric vehicle loads considering energy recovery under dynamic pricing. Firstly, based on the topological map of urban transportation network, an electric vehicle travel chain model considering urban functional areas is established. Secondly, based on Monte Carlo simulation method, a spatio-temporal distribution model of electric vehicle load considering electric vehicle braking energy recovery is constructed; and by introducing a charging utility function that considers users' sequential charging behavior, a Stackelberg game model is established to minimize distribution grid load fluctuations and electric vehicle user charging costs, which achieves the spatio-temporal guidance for electric vehicle loads considering energy recovery under a dynamic pricing mechanism. Finally, a case study is used to verify the effectiveness of the proposed method in guiding the spatio-temporal distribution of electric vehicle loads.

Key words: electric vehicles, spatio-temporal distribution, dynamic electricity pricing, energy recovery, stackelberg game model

杨新桥, 加鹤萍, 李培军, 李顺, 龙羿, 刘敦楠, 黄辉. 动态电价下计及能量回收的电动汽车负荷时空引导策略[J]. 中国电力, 2025, 58(11): 122-134.

YANG Xinqiao, JIA Heping, LI Peijun, LI Shun, LONG Yi, LIU Dunnan, HUANG Hui. Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing[J]. Electric Power, 2025, 58(11): 122-134.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202501053

https://www.electricpower.com.cn/CN/Y2025/V58/I11/122

图/表 17

表 1 出行链类型占比

Table 1 Percentage of travel chain types

出行链类型		占比
居住区-工作区-居住区		0.47
居住区-其他区-居住区		0.15
居住区-工作区-其他区-居住区		0.12
居住区-其他区-工作区-居住区		0.13
其他区-居住区/工作区-其他区		0.03
工作区-居住区/其他区-工作区		0.03

图 1 蒙特卡洛仿真模拟流程

Fig.1 Monte Carlo simulation flow

图 2 电价引导下用户充电需求分析

Fig.2 Analysis of user demand for charging under tariff guide

图 3 主从博弈模型框架

Fig.3 Stackelberg game modeling framework

图 4 主从博弈模型求解流程

Fig.4 Stackelberg game model solution process

表 2 粒子群算法参数

Table 2 PSO parameters

参数		取值
种群数量/个		10
迭代次数		200
学习因子		2.0
例子速度限制		[–2, 2]
惯性权重		0.8

图 5 城市网络结构及功能区划分

Fig.5 City network structure and functional area delineation

图 6 电动汽车负荷时空引导模型的计算流程

Fig.6 Computational flow of spatio-temporal guidance modeling of electric vehicle loads

图 7 3种场景下总用电负荷24 h曲线

Fig.7 24-hour curves of total electricity loads under three scenarios

表 3 3种场景负荷峰谷差及充电总成本

Table 3 Peak-valley differences and total charging cost under three scenarios

场景	配电网负荷峰谷差/kW	用户充电总成本/元
1	19638.25	65907.52
2	9272.95	50029.13
3	7714.96	45315.26

图 8 不同功能区动态电价优化结果

Fig.8 Dynamic pricing optimization results for different functional areas

图 9 场景3中充电负荷时空分布

Fig.9 Spatial-temporal distribution of charging loads under scenario 3

表 4 某典型节点考虑能量回收前后充电负荷变化

Table 4 Change in charging load with and without the energy recovery at a typical node

	峰谷差/kW	平均负荷值/kW
未考虑能量回收	110.37	48.88
考虑能量回收	98.84	41.68

图 10 动态电价引导后场景1充电负荷时空转移结果

Fig.10 Spatial-temporal shift of charging loads in scenario 1 after dynamic pricing guidance

图 11 动态电价引导后场景2充电负荷时空转移结果

Fig.11 Spatial-temporal shift of charging loads in scenario 2 after dynamic pricing guidance

图 12 考虑充电效用函数前后对比

Fig.12 Comparison with and without the charging utility function

图 13 考虑制动能量回收前后对比

Fig.13 Comparison with and without the brake energy recovery

参考文献 46

1	中国人民共和国公安部. 2024年上半年全国机动车达4.4亿辆驾驶人达5.32亿人新能源汽车保有量达2472万辆[EB/OL]. (2024-07-09) [2025-07-17]. https://www.mps.gov.cn/n2254314/n6409334/n9194010/c9648257/content.html.
2	张晓虎, 张熊, 张克良, 等. 储能技术在轨道交通再生制动能量回收的应用[J]. 电气工程学报, 2023, 18 (2): 210- 220. DOI
	ZHANG Xiaohu, ZHANG Xiong, ZHANG Keliang, et al. Application of energy storage technology in regenerative braking energy recovery of rail transit[J]. Journal of Electrical Engineering, 2023, 18 (2): 210- 220. DOI
3	宁达文. 混合动力汽车再生制动控制策略仿真研究[D]. 北京: 北京交通大学, 2021.
	NING Dawen. Research on regenerative braking control strategy simulation of hybrid electric vehicle[D]. Beijing: Beijing Jiaotong University, 2021.
4	朱法华, 徐静馨. 双碳背景下中国与主要发达国家电力低碳转型比较[J]. 电力科技与环保, 2024, 40 (6): 561- 571.
	ZHU Fahua, XU Jingxin. Comparison of low-carbon transformation in electricity between China and major developed countries under the background of carbon peaking and carbon neutrality[J]. Electric Power Technology and Environmental Protection, 2024, 40 (6): 561- 571.
5	许翔泰, 张仰飞, 陈光宇, 等. 计及光伏发电的电动汽车充电优化调度研究[J]. 智慧电力, 2019, 47 (10): 44- 50. DOI
	XU Xiangtai, ZHANG Yangfei, CHEN Guangyu, et al. Optimal scheduling of charging for electric vehicle considering photovoltaic power generation[J]. Smart Power, 2019, 47 (10): 44- 50. DOI
6	喻恒凝, 黄力, 张思东, 等. 分布式光伏和电动汽车接入对配电网网损和电压偏移影响的分析研究[J]. 智慧电力, 2020, 48 (1): 28- 34. DOI
	YU Hengning, HUANG Li, ZHANG Sidong, et al. Effects of distributed photovoltaic generation and electric vehicle integrating into distribution network on line loss and voltage drift[J]. Smart Power, 2020, 48 (1): 28- 34. DOI
7	胡继匀, 杨镜非, 傅长熠. 基于两阶段优化模型的电动汽车充电站放电策略[J]. 智慧电力, 2021, 49 (1): 83- 89. DOI
	HU Jiyun, YANG Jingfei, FU Changyi. Two-stage optimization strategy for electric vehicle charging station based on Nash negotiation method and reinforcement learning algorithm[J]. Smart Power, 2021, 49 (1): 83- 89. DOI
8	姚明, 毛文杰, 曹淑超, 等. 基于多源数据的电动汽车充电设施布局优化方法研究[J]. 智慧电力, 2023, 51 (9): 31- 37. DOI
	YAO Ming, MAO Wenjie, CAO Shuchao, et al. Optimization method of electric vehicle charging facility layout based on multi-source data[J]. Smart Power, 2023, 51 (9): 31- 37. DOI
9	HAN S, HAN S, SEZAKI K. Development of an optimal vehicle-to-grid aggregator for frequency regulation[J]. IEEE Transactions on Smart Grid, 2010, 1 (1): 65- 72. DOI
10	WANG M S, MU Y F, SHI Q X, et al. Electric vehicle aggregator modeling and control for frequency regulation considering progressive state recovery[J]. IEEE Transactions on Smart Grid, 2020, 11 (5): 4176- 4189. DOI
11	周原冰, 龚乃玮, 王皓界, 等. 中国电动汽车发展及车网互动对新型储能配置的影响[J]. 中国电力, 2024, 57 (10): 1- 11. DOI
	ZHOU Yuanbing, GONG Naiwei, WANG Haojie, et al. Study on the influence of electric vehicle development and the vehicle-grid interaction on new energy storage configuration in China[J]. Electric Power, 2024, 57 (10): 1- 11. DOI
12	赵梓潼, 顾兵. 需求响应下基于电动汽车负荷聚合商的充放电电价与时段研究[J]. 东北电力大学学报, 2023, 43 (6): 79- 86.
	ZHAO Zitong, GU Bing. Research on charging and discharging price and time period basedon electric vehicle load aggregator under demand response[J]. Journal of Northeast Electric Power University, 2023, 43 (6): 79- 86.
13	于雷, 饶磊, 向官腾, 等. 小容量光伏发电系统接入配电网的电压特性分析[J]. 内蒙古电力技术, 2024, 42 (6): 26- 30.
	YU Lei, RAO Lei, XIANG Guanteng, et al. Analysis of voltage characteristics of small capacity photovoltaic power generation system connected to distribution network[J]. Inner Mongolia Electric Power, 2024, 42 (6): 26- 30.
14	沈筱琦, 方鑫, 谭林林, 等. 基于居民出行模拟的电动汽车负荷时空分布预测[J]. 电力工程技术, 2024, 43 (3): 130- 139. DOI
	SHEN Xiaoqi, FANG Xin, TAN Linlin, et al. Prediction of spatio-temporal distribution of electric vehicle load based on residential travel simulation[J]. Electric Power Engineering Technology, 2024, 43 (3): 130- 139. DOI
15	屈克庆, 赵登辉, 毛玲, 等. 考虑城市空间结构和用户有限理性的电动汽车快充负荷预测[J]. 南方电网技术, 2024, 18 (10): 151- 160.
	QU Keqing, ZHAO Denghui, MAO Ling, et al. Fast charging load forecasting of electric vehicles considering urban spatial structure and user's bounded rationality[J]. Southern Power System Technology, 2024, 18 (10): 151- 160.
16	丁乐言, 柯松, 张帆, 等. 考虑出行需求和引导策略的电动汽车充电负荷预测[J]. 电力建设, 2024, 45 (6): 10- 26. DOI
	DING Leyan, KE Song, ZHANG Fan, et al. Forecasting of electric-vehicle charging load considering travel demand and guidance strategy[J]. Electric Power Construction, 2024, 45 (6): 10- 26. DOI
17	韩林阳, 叶承晋, 朱超, 等. 高温天气下计及用户意愿的城市充电负荷空间引导策略[J]. 电力系统自动化, 2024, 48 (10): 139- 150. DOI
	HAN Linyang, YE Chengjin, ZHU Chao, et al. Spatial guidance strategy for urban charging loads in high-temperature weather considering user willingness[J]. Automation of Electric Power Systems, 2024, 48 (10): 139- 150. DOI
18	程杉, 钟仕凌, 尚冬冬, 等. 考虑电动汽车时空负荷分布特性的主动配电网动态重构[J]. 电力系统保护与控制, 2022, 50 (17): 1- 13.
	CHENG Shan, ZHONG Shiling, SHANG Dongdong, et al. Dynamic reconfiguration of an active distribution network considering temporal and spatial load distribution characteristics of electric vehicles[J]. Power System Protection and Control, 2022, 50 (17): 1- 13.
19	MA S C, YI B W, FAN Y. Research on the valley-filling pricing for EV charging considering renewable power generation[J]. Energy Economics, 2022, 106, 105781. DOI
20	邓衍辉, 李剑, 卢国强, 等. 考虑分区域动态电价机制引导的电动汽车充电优化策略[J]. 电力系统保护与控制, 2024, 52 (7): 33- 44.
	DENG Yanhui, LI Jian, LU Guoqiang, et al. Charging optimization strategy of electric vehicles guided by the dynamic tariff mechanism of a subregion[J]. Power System Protection and Control, 2024, 52 (7): 33- 44.
21	肖白, 吕会森, 杨士伟, 等. 考虑电动汽车用户有限理性的定制化电价套餐设计[J]. 电气应用, 2024, 43 (2): 91- 100. DOI
	XIAO Bai, LV Huisen, YANG Shiwei, et al. The design of customized electricity price package considering the limited rationality of electric vehicle users[J]. Electrotechnical Application, 2024, 43 (2): 91- 100. DOI
22	杨帅, 戴朝华, 郭爱, 等. 考虑负荷空间均衡和充电站合作博弈的快速充电定价机制[J]. 电力系统自动化, 2024, 48 (7): 190- 200. DOI
	YANG Shuai, DAI Chaohua, GUO Ai, et al. Fast charging pricing mechanism considering load space balance and cooperative game of charging stations[J]. Automation of Electric Power Systems, 2024, 48 (7): 190- 200. DOI
23	姜晓锋, 魏巍, 王永灿, 等. “车—路—网”协同优化下的电动汽车有序充电引导策略[J]. 电力科学与技术学报, 2023, 38 (5): 44- 56.
	JIANG Xiaofeng, WEI Wei, WANG Yongcan, et al. Orderly charging guidance strategies for electric vehicles under EVs-Traffic-Distribution collaborative optimization[J]. Journal of Electric Power Science and Technology, 2023, 38 (5): 44- 56.
24	叶文浩, 陈耀红, 颜勤, 等. 基于动态分时电价引导的电动汽车需求侧响应[J]. 电力科学与技术学报, 2024, 39 (4): 138- 145.
	YE Wenhao, CHEN Yaohong, YAN Qin, et al. Demand response of electric vehicle based on dynamic time-to-use electricity price[J]. Journal of Electric Power Science and Technology, 2024, 39 (4): 138- 145.
25	潘廷哲, 靳丰源, 陆泳昊, 等. 智能小区电动汽车充电动态电价策略设计[J/OL]. 中国电力, 2025: 1–11. (2025-07-17). https://kns.cnki.net/KCMS/detail/detail.aspx?filename=ZGDL20250716002&dbname=CJFD&dbcode=CJFQ.
	PAN Tingzhe, JIN Fengyuan, LU Yonghao, et al. Design of dynamic prices strategy for electric vehicles charging in smart communities[J/OL]. Electric Power, 2025: 1–11. (2025-07-17). https://kns.cnki.net/KCMS/detail/detail.aspx?filename=ZGDL20250716002&dbname=CJFD&dbcode=CJFQ.
26	张美霞, 吴子敬, 杨秀. 基于动态能耗模型与用户心理的电动汽车充电负荷预测[J]. 现代电力, 2022, 39 (6): 710- 719.
27	侯慧, 王逸凡, 吴细秀, 等. 长时间尺度下计及里程焦虑心理效应的电动汽车充放电调度策略[J]. 高电压技术, 2023, 49 (1): 85- 93.
	HOU Hui, WANG Yifan, WU Xixiu, et al. Electric vehicle charging and discharging scheduling strategy considering psychological effect of mileage anxiety on a long-time scale[J]. High Voltage Engineering, 2023, 49 (1): 85- 93.
28	孙磊, 冯智伟, 韩美灵, 等. 面向成本优化的电动汽车弹性充电策略[J]. 小型微型计算机系统, 2024, 45 (1): 132- 138.
	SUN Lei, FENG Zhiwei, HAN Meiling, et al. Cost-optimized elastic charging strategy for electric vehicles[J]. Journal of Chinese Computer Systems, 2024, 45 (1): 132- 138.
29	李宏伟, 宋玉峰, 李帅兵, 等. 基于ArcGIS路网结构与交通拥挤度分析的电动汽车充电负荷预测方法[J]. 电网技术, 2025, 49 (5): 1920- 1930.
	LI Hongwei, SONG Yufeng, LI Shuaibing, et al. Electric vehicle charging load prediction based on ArcGIS road network structure and traffic congestion analysis[J]. Power System Technology, 2025, 49 (5): 1920- 1930.
30	李晓涵, 曹伟. 弹性充电需求下电动汽车调频激励机制及控制策略[J]. 中国电力, 2025, 58 (4): 148- 158. DOI
	LI Xiaohan, CAO Wei. Frequency regulation incentive mechanism and control strategy for electric vehicles under elastic charging demand[J]. Electric Power, 2025, 58 (4): 148- 158. DOI
31	贾世成, 廖凯, 杨健维, 等. 考虑交通负荷时空需求及区域划分的配电网分布式经济调度策略[J]. 电力系统及其自动化学报, 2024, 36 (11): 30- 42.
	JIA Shicheng, LIAO Kai, YANG Jianwei, et al. Distributed economic dispatching strategy for distribution network considering temporal and spatial demand of traffic load and area division[J]. Proceedings of the CSU-EPSA, 2024, 36 (11): 30- 42.
32	陈丽丹, 张尧, Antonio Figueiredo. 融合多源信息的电动汽车充电负荷预测及其对配电网的影响[J]. 电力自动化设备, 2018, 38 (12): 1- 10.
	CHEN Lidan, ZHANG Yao, FIGUEIREDO A. Charging load forecasting of electric vehicles based on multi-source information fusion and its influence on distribution network[J]. Electric Power Automation Equipment, 2018, 38 (12): 1- 10.
33	Federal Highway Administration. 2017 National Household Travel Survey, U. S. Department of Transportation, Washington, DC[EB/OL]. (2021-02-21) [2024-10-30]. https://nhts.ornl.gov.
34	ZHANG W, DOU Y F. Coping with spatial mismatch: subsidy design for electric vehicle and charging markets[J]. Manufacturing & Service Operations Management, 2021, 24 (3): 1595- 1610.
35	GUILLET M, SCHIFFER M. Coordinated charging station search in stochastic environments: a multiagent approach[J]. Production and Operations Management, 2023, 32 (8): 2596- 2618. DOI
36	HE C, OZTURK O C, GU C, et al. Consumer tax credits for EVs: some quasi-experimental evidence on consumer demand, product substitution, and carbon emissions[J]. Management Science, 2023, 69 (12): 7759- 7783. DOI
37	YANG C, SUN T L, WANG W D, et al. Regenerative braking system development and perspectives for electric vehicles: an overview[J]. Renewable and Sustainable Energy Reviews, 2024, 198, 114389. DOI
38	《中国公路学报》编辑部. 中国汽车工程学术研究综述·2023[J]. 中国公路学报, 2023, 36 (11): 1- 192.
39	黄伟, 张桂连, 周登辉, 等. 基于能量流分析的纯电动汽车电耗优化研究[J]. 汽车工程, 2021, 43 (2): 171- 180.
	HUANG Wei, ZHANG Guilian, ZHOU Denghui, et al. Research on optimization of power consumption of pure electric vehicle based on energy flow analysis[J]. Automotive Engineering, 2021, 43 (2): 171- 180.
40	赵敬玉, 徐铖, 李晓宇. 基于实际行驶数据的电动汽车能耗分析与预测[J]. 机械工程学报, 2023, 59 (10): 263- 274. DOI
	ZHAO Jingyu, XU Cheng, LI Xiaoyu. Electric vehicle energy consumption analysis and prediction based on real-world driving data[J]. Journal of Mechanical Engineering, 2023, 59 (10): 263- 274. DOI
41	张树培, 黄璇, 张玮. 电动汽车道路行驶制动能量回收特性研究[J]. 汽车技术, 2014 (12): 49- 53. DOI
	ZHANG Shupei, HUANG Xuan, ZHANG Wei. Research on braking energy recovery characteristics of electric vehicle in road driving[J]. Automobile Technology, 2014 (12): 49- 53. DOI
42	齐彩娟, 陈宝生, 韦冬妮, 等. 考虑主从博弈定价模式的共享储能分布鲁棒优化配置方法研究[J]. 中国电力, 2024, 57 (7): 40- 53. DOI
	QI Caijuan, CHEN Baosheng, WEI Dongni, et al. Distributionally robust optimal configuration for shared energy storage based on Stackelberg game pricing model[J]. Electric Power, 2024, 57 (7): 40- 53. DOI
43	王一飞, 王秀丽, 黄晶, 等. 动态电价机制下电动汽车竞争性充电的阻塞管理模型[J]. 电力系统自动化, 2023, 47 (12): 103- 110. DOI
	WANG Yifei, WANG Xiuli, HUANG Jing, et al. Congestion management model for competitive charging of electric vehicles under dynamic electricity price mechanism[J]. Automation of Electric Power Systems, 2023, 47 (12): 103- 110. DOI
44	夏鑫, 钟浩, 张磊, 等. 计及动态电价的电动汽车参与微电网调度双层优化策略[J]. 电力工程技术, 2024, 43 (3): 140- 150. DOI
	XIA Xin, ZHONG Hao, ZHANG Lei, et al. A two-layer optimization strategy for electric vehicles participating in microgrid scheduling considering dynamic electricity prices[J]. Electric Power Engineering Technology, 2024, 43 (3): 140- 150. DOI
45	段梅梅, 程含渺, 方凯杰, 等. 基于分时电碳耦合定价的虚拟电厂主从博弈策略[J]. 电测与仪表, 2024, 61 (11): 1- 11.
	DUAN Meimei, CHENG Hanmiao, FANG Kaijie, et al. Research on Stackelberg game strategy for virtual power plant considering time-of-use electricity and carbon coupled price[J]. Electrical Measurement & Instrumentation, 2024, 61 (11): 1- 11.
46	余梦龙, 张进朝, 文豪, 等. 改进PSO-BPNN供水泵站变频恒压PID控制[J]. 电子测量技术, 2023, 46 (17): 64- 70.
	YU Menglong, ZHANG Jinchao, WEN Hao, et al. Variable frequency and constant pressure PID control of water supply pump station based on improved PSO-BPNN algorithm[J]. Electronic Measurement Technology, 2023, 46 (17): 64- 70.

[1]	郑佳俊, 段小宇, 胡泽春, 胡晓锐, 朱彬. 组件式移动充电设施布局优化方法与投放策略[J]. 中国电力, 2025, 58(4): 107-118.
[2]	苏大威, 范旖晖, 赵天辉, 潘虹锦, 黄佑会, 王岗, 贾勇勇. 基于价格激励的城市公共充电站填谷潜力评估[J]. 中国电力, 2025, 58(4): 131-139.
[3]	颜俊, 罗宇杰, 颜安, 贺伟, 韩涛, 杨军. 计及用户响应特性的电动汽车充电站设备优化配置方法[J]. 中国电力, 2025, 58(4): 140-147.
[4]	李晓涵, 曹伟. 弹性充电需求下电动汽车调频激励机制及控制策略[J]. 中国电力, 2025, 58(4): 148-158.
[5]	潘廷哲, 靳丰源, 陆泳昊, 曹望璋, 阳浩, 于鹤洋, 赵勃扬. 智能小区电动汽车充电动态电价策略设计[J]. 中国电力, 2025, 58(11): 14-24, 37.
[6]	王锦坤, 薛娴, 吴慧敏, 秦科源. 考虑EV储能特性的分布式多能互补系统设计与运行优化分析[J]. 中国电力, 2025, 58(11): 164-174.
[7]	廖建, 张耀, 张贝西, 董浩淼, 李嘉兴, 孙乾皓. 考虑电动汽车需求响应的交直流混合配电网智能软开关与储能装置鲁棒联合规划方法[J]. 中国电力, 2025, 58(10): 82-96.
[8]	丁屹峰, 曾爽, 张宝群, 王立永, 刘畅, 付智, 张吉. 光伏-直流智能充电桩有序充电策略与应用效果[J]. 中国电力, 2024, 57(5): 70-77.
[9]	刘韵艺, 汤渊, 苏盛, 吴裕宙, 王晓倩. 基于STL-Bayesian时空模型的分布式光伏系统异常检测[J]. 中国电力, 2024, 57(5): 222-231.
[10]	吕志鹏, 宋振浩, 李立生, 刘洋. 含电动汽车的工业园区综合能源系统优化调度[J]. 中国电力, 2024, 57(4): 25-31.
[11]	杨磊, 浑连明, 祖国强, 李树军, 李心达, 郭俊龙, 张玉涛. 电动汽车充电机器人发展现状和标准需求[J]. 中国电力, 2024, 57(4): 89-99.
[12]	张雷, 肖伟栋, 蒋纯冰, 刘耀, 李少杰, 张吉. 光伏办公建筑的关键设备容量配置方法[J]. 中国电力, 2024, 57(3): 152-159, 169.
[13]	周原冰, 龚乃玮, 王皓界, 肖晋宇, 张赟. 中国电动汽车发展及车网互动对新型储能配置的影响[J]. 中国电力, 2024, 57(10): 1-11.
[14]	张俊成, 黎敏, 刘志文, 谭靖, 陶毅刚, 罗天禄. 配电网用户侧多类型柔性资源调节能力评估方法[J]. 中国电力, 2023, 56(9): 96-103,119.
[15]	孙景博, 王阳, 杨晓帆, 陆峥, 何源, 巢清尘. 中国风光资源气候风险时空变化特征分析[J]. 中国电力, 2023, 56(5): 1-10.

动态电价下计及能量回收的电动汽车负荷时空引导策略

Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 46

相关文章 15

编辑推荐

Metrics

模态框（Modal）标题

动态电价下计及能量回收的电动汽车负荷时空引导策略

Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 46

相关文章 15

编辑推荐

Metrics