基于阶段分时电价响应的电动汽车充电负荷优化策略

doi:10.3969/j.issn.1008-0198.2024.01.013

湖南电力 ›› 2024, Vol. 44 ›› Issue (1): 94-100.doi: 10.3969/j.issn.1008-0198.2024.01.013

基于阶段分时电价响应的电动汽车充电负荷优化策略

许文哲, 方八零, 张淇菲, 李伟, 刘浩

湖南工业大学电气与信息工程学院,湖南株洲 412007

收稿日期:2023-12-01 修回日期:2023-12-18 出版日期:2024-02-25 发布日期:2024-03-11
通信作者: 方八零(1980),男,讲师,研究方向为智能电网和云计算。
作者简介:许文哲(1999),男,硕士研究生在读,研究方向为电动汽车充电负荷优化。
张淇菲(2000),女,硕士研究生在读,研究方向为电动汽车充电负荷优化。
李伟(2001),男,硕士研究生在读,研究方向为电动汽车充电负荷优化。
刘浩(2000),男,硕士研究生在读,研究方向为电动汽车充电负荷优化。
基金资助:
湖南省教育厅基金项目(2B0563)

Optimization Strategy of Electric Vehicle Charging Load Based on Stage-Sharing Tariff Response

XU Wenzhe, FANG Baling, ZHANG Qifei, LI Wei, LIU Hao

School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China

Received:2023-12-01 Revised:2023-12-18 Online:2024-02-25 Published:2024-03-11

摘要/Abstract

摘要： 针对电动汽车的充电负荷优化问题,提出一种基于阶段分时电价响应的电动汽车充电负荷优化策略。该策略在考虑电动汽车负荷因素的前提下,建立电动汽车充电负荷模型,并通过蒙特卡洛算法模拟电动汽车无序充电的负荷变化。在峰、谷、平段电价弹性矩阵的基础上,细分不同的等分时间段建立阶段分时电价模型,构建以用户充电总费用和系统总负荷均方差最小为优化目标的电动汽车分时电价优化模型,采用改进的混沌多目标遗传算法对模型进行求解优化。实验对比结果验证了该策略的有效性和经济性,在给定数据条件下用户充电总费用和系统总负荷均方差较传统分时电价方案分别降低了0.409 1万元和37.151 MW。

关键词: 电动汽车, 分时电价, 需求响应, 价格弹性矩阵

Abstract: Aiming at the charging load optimization problem of electric vehicles, this paper proposes a charging load optimization strategy for electric vehicles based on stage-sharing tariff response. The strategy establishes an electric vehicle charging load model and simulates the load variation of uncontrolled charging of electric vehicles by Monte Carlo algorithm, taking into account the electric vehicle load factor. On the basis of the elasticity matrix of peak-valley leveling tariffs, the stage time-sharing tariff model is established by subdividing different equal time periods, and the optimization model of time-sharing tariffs for electric vehicles is constructed with the optimization objectives of minimizing the mean square deviation of the total charging costs of the users and the total load of the system, and the improved chaotic multi-objective genetic algorithm is used to solve and optimize the model. Finally, the effectiveness and economy of the strategy are verified by the experimental comparison results, where the total user charging cost and the total system load mean squared deviation are reduced by 4091 yuan and 37.151 MW respectively, compared with the traditional time-sharing tariff scheme under the given data conditions.

Key words: electric vehicle, time-sharing tariff, demand response, price elasticity matrix

中图分类号:

TM715.1
F426

许文哲, 方八零, 张淇菲, 李伟, 刘浩. 基于阶段分时电价响应的电动汽车充电负荷优化策略[J]. 湖南电力, 2024, 44(1): 94-100.

XU Wenzhe, FANG Baling, ZHANG Qifei, LI Wei, LIU Hao. Optimization Strategy of Electric Vehicle Charging Load Based on Stage-Sharing Tariff Response[J]. Hunan Electric Power, 2024, 44(1): 94-100.

参考文献

[1] 洪睿洁,顾丹珍,莫阮清,等.基于用户偏好的电动汽车储能V2G策略优化[J].储能科学与技术,2023,12(8):2659-2667.
[2] ZHANG X,CHAN K W, LI H, et al. Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model[J]. IEEE Transactions on Cybernetics, 2020, 51(6):3157-3170.
[3] DONG L,WANG C,LI M,et al.User decision-based analysis of urban electric vehicle loads[J]. CSEE Journal of Power and Energy Systems, 2020, 7(1):190-200.
[4] SIMOLIN T,RAUMA K,JÄRVENTAUSTA P,et al.Optimised controlled charging of electric vehicles under peak power-based electricity pricing[J].IET Smart Grid, 2020,3(6):751-759.
[5] SALMANI H, REZAZADE A, SEDIGHIZADEH M.Stochastic peer to peer energy trading among charging station of electric vehicles based on blockchain mechanism[J]. IET Smart Cities, 2022, 4(2): 110-126.
[6] 华远鹏,王圆圆, 韩丁, 等. 考虑有序充电的居民区电动汽车中长期充电负荷预测[J]. 电力系统及其自动化学报, 2022,34(6):142-150.
[7] 魏振华,王黎黎,郑亚锋,等.基于主从博弈的含电动汽车综合能源系统运行策略[J].科学技术与工程,2022,22(28):12417-12423.
[8] HE J, YAN N, ZHANG J, et al. Battery electric buses charging schedule optimization considering time-of-use electricity price[J]. Journal of Intelligent and Connected Vehicles, 2022,5(3):138-145.
[9] 赵冬梅, 马泰屹. 基于动态分时电价的含电动汽车电网无功优化策略[J]. 华北电力大学学报(自然科学版), 2022,49(1):1-11.
[10] TENG F,DING Z,HU Z,et al. Technical review on advanced approaches for electric vehicle charging demand management, Part I: Applications in electric power market and renewable energy integration[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5684-5694.
[11] 廖菁, 涂钊颖, 胡毕正, 等. 湖南省峰谷分时电价分析及优化策略研究[J]. 湖南电力, 2021, 41(1):64-70.
[12] 邓慧琼, 张晓飞, 曾凡淦, 等. 基于动态分时电价的电动汽车有序充放电调度策略[J]. 河北科技大学学报, 2022, 43(3):240-248.
[13] LIU D, WANG W, WANG L, et al. Dynamic pricing strategy of electric vehicle aggregators based on DDPG reinforcement learning algorithm[J]. IEEE Access, 2021, 9(1): 21556-21566.
[14] HUANG S, WU Q, ZHAO H, et al. Distributed optimization based dynamic tariff for congestion management in distribution networks[J]. IEEE Transactions on Smart Grid,2019,10(1):184-192.
[15] 王均, 黄钶然, 许潇, 等. 基于阶梯碳价和自适应分时电价的电动汽车有序充电[J/OL]. 电力自动化设备, 2023:1-12(2023-04-21)[2023-11-20].https://doi.org/10.16081/j.epae.202304012.
[16] 李波, 廖其龙. 基于网格遗传算法的家用电动汽车有序充电策略[J]. 制造业自动化, 2022, 44(12):138-142.
[17] 陈艳波, 田昊欣, 刘宇翔, 等. 计及电动汽车需求响应的高速公路服务区光储充鲁棒优化配置[J/OL]. 中国电机工程学报, 2023:1-16(2023-11-20)[2023-12-01].https://doi.org/10.13334/j.0258-8013.pcsee.231850.
[18] 李楠,马宏忠.基于综合预测模型和蒙特卡洛的电动汽车保有量及负荷预测方法研究[J].电机与控制应用,2022,49(12):74-80.
[19] 陈文颖,刘蓓迪.基于粒子群算法的电动汽车有序充放电优化[J].山东电力技术,2023,50(1):52-58.
[20] 俞叶锋,张宸,高展,等.基于多目标遗传算法的行驶工况构建优化[J].内燃机工程,2023,44(5):57-65.
[21] 何兆蓉,高逸云,黄瀚霆,等.基于电网分时电价机制考虑电动汽车灵活充电的微电网能量管理[J].电器与能效管理技术,2022(5):65-70.
[22] 孔峰,杨金辉,任恒君,等.考虑风电消纳的峰谷分时电价模型研究[J].现代电子技术,2023,46(21):160-165.

基于阶段分时电价响应的电动汽车充电负荷优化策略

Optimization Strategy of Electric Vehicle Charging Load Based on Stage-Sharing Tariff Response

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	杜善周, 黄涌波, 刘瑞平, 韩硕, 唐建玲, 刘绪斌, 吴晋波. 面向多时间尺度的光-火-铝孤岛微电网综合能量管理方法[J]. 湖南电力, 2023, 43(6): 37-44.
[2]	黄珉淇, 戴丹丹, 张莉, 廖菁, 潘馨. 湖南省居民峰谷分时电价方案研究[J]. 湖南电力, 2023, 43(6): 63-67.
[3]	康童, 朱吉然, 唐海国, 周恒逸, 周可慧. 采用改进共生生物搜索算法的大规模电动汽车接入微电网协调优化研究[J]. 湖南电力, 2023, 43(5): 144-150.
[4]	李奕杰, 宋恒, 叶晨晖, 申庆祥. 基于融合模型驱动和数据驱动的电动汽车充电负荷预测[J]. 湖南电力, 2023, 43(3): 9-15.
[5]	刘莹, 宋丽敏, 龚强, 吕森, 陈森, 谢宁. 考虑需求响应的乡村综合能源系统源网荷储协调规划模型研究[J]. 湖南电力, 2023, 43(3): 21-28.
[6]	谢鹰, 郑众, 刘剑峰, 高希, 李大祥. 基于遗传算法计及充电行驶距离的电动汽车充电网络规划[J]. 湖南电力, 2023, 43(3): 29-36.
[7]	胡真, 汪盼. 计及可再生能源不确定性的实时需求响应策略研究[J]. 湖南电力, 2023, 43(1): 30-34.
[8]	邓元望, 尹会春, 鄂加强, 刘帅, 朱浩. 电动汽车电池组绝缘监测方法研究[J]. 湖南电力, 2023, 43(1): 46-49.
[9]	伍也凡, 陈锦, 张谜, 刘浩田, 邓凯, 李达伟. 湖南省面向大工业负荷需求侧响应研究[J]. 湖南电力, 2020, 40(4): 7-12.