EN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
Optimization Model of Distributed Multi Energy Fusion System Considering the Mobile Energy Storage Characteristics of Electric Vehicles
- 中国电力 2025年58卷第11期 页码：164-174
- 作者机构：
1. 福建电力职业技术学院,福建,泉州,362000
2. 北京科东电力控制系统有限责任公司,北京,100192
- 作者简介：
- 基金信息：国网福建省电力有限公司科技项目（SGFJJP00BDXX2310105）。
DOI：10.11930/j.issn.1004-9649.202412068
中图分类号：-
网络出版：2025-12-01，
纸质出版：2025-12-01
- 稿件说明：
移动端阅览
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.
WANG Jinkun, XUE Xian, WU Huimin, et al. Optimization Model of Distributed Multi Energy Fusion System Considering the Mobile Energy Storage Characteristics of Electric Vehicles[J]. 2025, 58(11): 164-174.
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. DOI： 10.11930/j.issn.1004-9649.202412068.
WANG Jinkun, XUE Xian, WU Huimin, et al. Optimization Model of Distributed Multi Energy Fusion System Considering the Mobile Energy Storage Characteristics of Electric Vehicles[J]. 2025, 58(11): 164-174. DOI： 10.11930/j.issn.1004-9649.202412068.
摘要
电动汽车（electric vehicle，EV）为分布式多能互补系统（distributed multi-energy system，DMS）带来海量移动储能资源。考虑EV移动储能特性，以系统成本与碳排放为优化目标，构建了基于非支配排序遗传算法与混合整数线性规划融合的DMS优化模型，分析了EV储能特性对系统的影响。通过仿真算例验证了所提方法的有效性。结果表明：在系统设计时，考虑EV储能特性能够有效促进各主体相互协同，提升系统经济性。
Abstract
Electric vehicles (EVs) bring a large number of mobile energy storage resources to the distributed multi-energy complementary system (DMS). Considering mobile storage characteristics of EVs
an optimization model for DMS is constructed based on the fusion of non-dominated sorting genetic algorithm and mixed-integer linear programming with system cost and emission as the optimization objective
and the influence of EV storage characteristics on the system is analyzed. The effectiveness of the proposed method is verified by a simulation example. The results show that the storage characteristics of EVs can effectively promote the mutual cooperation among the main bodies and improve the economic performance of the system when designing the system.
关键词
Keywords
references
1颜俊, 罗宇杰, 颜安, 等. 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.
YAN Jun, LUO Yujie, YAN An, et al. Optimal equipment configuration method for electric vehicle charging stations considering user response characteristics[J]. Electric Power, 2025, 58 (4): 140- 147.
2吕志鹏, 宋振浩, 李立生, 等. 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.
LV Zhipeng, SONG Zhenhao, LI Lisheng, et al. Optimization scheduling of integrated energy system scheduling in industrial park containing electric vehicles[J]. Electric Power, 2024, 57 (4): 25- 31.
3侯治吉, 李成鑫, 刘广生. 电动汽车需求响应研究综述[J]. 电测与仪表, 2025, 62 (9): 37- 50. DOI
HOU Zhiji, LI Chengxin, LIU Guangsheng. Review of electric vehicle demand response research[J]. Electrical Measurement & Instrumentation, 2025, 62 (9): 37- 50. DOI
4张怡, 郝思鹏. 电动汽车充电站变压器容量及储能优化配置[J]. 电测与仪表, 2023, 60 (7): 19- 25. DOI
ZHANG Yi, HAO Sipeng. Optimized configuration of transformer capacity and energy storage for electric vehicle charging stations[J]. Electrical Measurement & Instrumentation, 2023, 60 (7): 19- 25. DOI
5沈运帷, 岑闻雨, 周波. 考虑供电设备差异化报价的虚拟电厂电能量-调峰联合市场优化策略[J]. 中国电力, 2025, 58 (7): 115- 127. DOI
SHEN Yunwei, CEN Wenyu, ZHOU Bo. Optimization strategy for virtual power plant combined electric energy and peak regulation market considering differentiated quotation of power supply equipment[J]. Electric Power, 2025, 58 (7): 115- 127. 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.
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.
8李明扬, 窦梦园. 基于强化学习的含电动汽车虚拟电厂优化调度[J]. 综合智慧能源, 2024, 46 (6): 27- 34. DOI
LI Mingyang, DOU Mengyuan. Optimal scheduling of virtual power plants integrating electric vehicles based on reinforcement learning[J]. Integrated Intelligent Energy, 2024, 46 (6): 27- 34. DOI
9闫威, 李南, 沈月秀, 等. 基于CNN-GAN与半监督回归的电动汽车充电负荷预测[J]. 浙江电力, 2023, 42 (2): 83- 89.
YAN Wei, LI Nan, SHEN Yuexiu, et al. Electric vehicle charging load forecasting based on CNN-GAN and semi-supervised regression[J]. Zhejiang Electric Power, 2023, 42 (2): 83- 89.
10郑伟, 张乐, 张建军, 等. 基于虚拟同步的V2G调度控制策略[J]. 南方能源建设, 2025, 12 (2): 116- 127. DOI
ZHENG Wei, ZHANG Le, ZHANG Jianjun, et al. V2G scheduling control strategy based on virtual synchronization[J]. Southern Energy Construction, 2025, 12 (2): 116- 127. DOI
11杨彪, 李佳蓉, 魏子轲. 美国分布式能源发展对我国的启示[J]. 中国电力企业管理, 2024 (25): 94- 96. DOI
12宣羿, 樊立波, 孙智卿, 等. 考虑低碳交通的电动汽车充电站优化配置方法[J]. 浙江电力, 2024, 43 (6): 69- 79. DOI
XUAN Yi, FAN Libo, SUN Zhiqing, et al. An optimal allocation method for electric vehicle charging stations considering lowcarbon transportation[J]. Zhejiang Electric Power, 2024, 43 (6): 69- 79. DOI
13庞松岭, 赵海龙, 张晨佳. 计及充电需求差异的电动汽车充电设施协同优化配置[J]. 电测与仪表, 2024, 61 (12): 171- 177. DOI
PANG Songling, ZHAO Hailong, ZHANG Chenjia. Collaborative optimization configuration of electric vehicle charging facilities considering differences in charging demand[J]. Electrical Measurement & Instrumentation, 2024, 61 (12): 171- 177. DOI
14王振, 丁孝华, 余昆, 等. 计及自治运行优化的配电网台区调节潜力区间建模[J]. 中国电力, 2025, 58 (10): 97- 109. DOI
WANG Zhen, DING Xiaohua, YU Kun, et al. Regulation potential range modeling for distribution transformer zones considering autonomous operation optimization[J]. Electric Power, 2025, 58 (10): 97- 109. DOI
15魏飞, 许迪, 陈鑫, 等. 基于机器学习的智能光伏充电桩能源调度系统研究[J]. 可再生能源, 2025, 43 (2): 268- 274. DOI
WEI Fei, XU Di, CHEN Xin, et al. Research on intelligent photovoltaic charging station energy testing system based on machine learning[J]. Renewable Energy Resources, 2025, 43 (2): 268- 274. DOI
16彭钢, 寇启龙, 方涛, 等. 考虑新能源消纳率的主动配电网储能优化配置策略[J]. 浙江电力, 2025, 44 (1): 84- 94. DOI
PENG Gang, KOU Qilong, FANG Tao, et al. An optimal strategy for energy storage allocation in active distribution networks considering new energy consumption rates[J]. Zhejiang Electric Power, 2025, 44 (1): 84- 94. DOI
17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.
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.
18李建林, 彭禹宸, 王茜, 等. 锂离子电池建模研究现状与展望[J]. 发电技术, 2025, 46 (5): 857- 871. DOI
LI Jianlin, PENG Yuchen, WANG Qian, et al. Research status and prospect of lithium-ion battery modelling[J]. Power Generation Technology, 2025, 46 (5): 857- 871. DOI
19黄晓凡, 李佳瑞, 刘晖, 等. 梯次利用动力电池储能系统综合效益分析[J]. 综合智慧能源, 2024, 46 (7): 63- 73. DOI
HUANG Xiaofan, LI Jiarui, LIU Hui, et al. Comprehensive benefit analysis on the cascade utilization of a power battery system[J]. Integrated Intelligent Energy, 2024, 46 (7): 63- 73. DOI
20卢冬琳, 赵兴勇. 考虑电动汽车响应度的住宅区能源优化控制策略[J]. 自动化与仪表, 2020, 35 (9): 1- 5, 10. DOI
LU Donglin, ZHAO Xingyong. Residential energy optimization control strategy considering the responsiveness of electric vehicles[J]. Automation & Instrumentation, 2020, 35 (9): 1- 5, 10. DOI
21刘玉婷, 张晓烽, 孔晓英, 等. 基于电动汽车V2B模式的多联产系统两阶段优化[J]. 工程热物理学报, 2023, 44 (11): 3091- 3102.
LIU Yuting, ZHANG Xiaofeng, KONG Xiaoying, et al. Two-stage optimization of multi-generation system based on electric vehicle V2B mode[J]. Journal of Engineering Thermophysics, 2023, 44 (11): 3091- 3102.
22ALEXAKIS K, BENEKIS V, KOKKINAKOS P, et al. Genetic algorithm-based multi-objective optimisation for energy-efficient building retrofitting: a systematic review[J]. Energy and Buildings, 2025, 328, 115216. DOI
23胡晓雅. 改进快速非支配排序遗传算法在多目标食谱推荐问题上的应用研究[D]. 长春: 吉林大学, 2023.
HU Xiaoya. Research on the application of improved fast non-dominate sorting genetic algorithm in multi-objective recipe recommendation problem[D]. Changchun: Jilin University, 2023.
24高媛. 非支配排序遗传算法(NSGA)的研究与应用[D]. 杭州: 浙江大学, 2006.
GAO Yuan. Non-dominated sorting genetic algorithm and its applications[D]. Hangzhou: Zhejiang University, 2006.
25谭玲玲, 孙鹏, 郭沛璇, 等. 含氢储能的微电网低碳-经济协同优化配置[J]. 发电技术, 2024, 45 (5): 983- 994.
TAN Lingling, SUN Peng, GUO Peixuan, et al. Low-carbon and economic synergy optimization configuration for microgrid with hydrogen energy storage[J]. Power Generation Technology, 2024, 45 (5): 983- 994.
26GE Y K, MA Y, WANG Q R, et al. Techno-economic-environmental assessment and performance comparison of a building distributed multi-energy system under various operation strategies[J]. Renewable Energy, 2023, 204, 685- 696. DOI
27黄堃, 付明, 翟家祥, 等. 基于改进线性化ADMM的多微网经济运行分布式协调优化[J]. 中国电力, 2025, 58 (2): 193- 202. DOI
HUANG Kun, FU Ming, ZHAI Jiaxiang, et al. Distributed coordination optimization for economic operation of the multi-microgrid system based on improved linearization ADMM[J]. Electric Power, 2025, 58 (2): 193- 202. DOI
28伏绍鑫, 张路, 唐翰峰, 等. 考虑柔性电热负荷的区域综合能源系统低碳经济调度[J]. 电力科技与环保, 2023, 39 (5): 417- 428. DOI
FU Shaoxin, ZHANG Lu, TANG Hanfeng, et al. Low-carbon economic dispatch of community integrated energy system considering flexible electric heating load[J]. Electric Power Technology and Environmental Protection, 2023, 39 (5): 417- 428. DOI
29荆朝霞, 袁灼新, 胡荣兴, 等. 供能设备模型对冷热电联供微网系统经济调度的影响[J]. 南方电网技术, 2016, 10 (8): 32- 39. DOI
JING Zhaoxia, YUAN Zhuoxin, HU Rongxing, et al. Effect of component models on economic dispatch of CCHP-type microgrid[J]. Southern Power System Technology, 2016, 10 (8): 32- 39. DOI
30刘雪敏, 胡浩南, 刘超, 等. 我国燃煤工业锅炉碳排放强度分布及影响因素研究[J]. 电力科技与环保, 2024, 40 (6): 625- 634. DOI
LIU Xuemin, HU Haonan, LIU Chao, et al. Study on the distribution of carbon emission intensity from coal-fired industrial boilers in China and its influencing factors[J]. Electric Power Technology and Environmental Protection, 2024, 40 (6): 625- 634. DOI
31程文煜, 张健, 熊卓, 等. “双碳”目标下煤电机组节能改造技术发展与实践[J]. 电力科技与环保, 2024, 40 (5): 455- 464. DOI
CHENG Wenyu, ZHANG Jian, XIONG Zhuo, et al. Development and practice of energy-saving retrofit technologies for coal power units under carbon peaking and carbon neutrality goals[J]. Electric Power Technology and Environmental Protection, 2024, 40 (5): 455- 464. DOI
32陈铭宏天, 耿江海, 赵雨泽, 等. 基于两阶段随机优化的电氢耦合微电网周运行策略[J]. 中国电力, 2025, 58 (5): 82- 90. DOI
CHEN Minghongtian, GENG Jianghai, ZHAO Yuze, et al. Two-stage stochastic optimization based weekly operation strategy for electric-hydrogen coupled microgrid[J]. Electric Power, 2025, 58 (5): 82- 90. DOI
33闫志彬, 李立, 阳鹏, 等. 考虑构网型储能支撑能力的微电网优化调度策略[J]. 中国电力, 2025, 58 (2): 103- 110. DOI
YAN Zhibin, LI Li, YANG Peng, et al. Optimal scheduling strategy for microgrid considering the support capabilities of grid forming energy storage[J]. Electric Power, 2025, 58 (2): 103- 110. DOI
34ZHOU S Y, HAN Y, MAHMOUD K, et al. A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation[J]. Applied Energy, 2023, 348, 121566. DOI
35李奕杰, 宋恒, 叶晨晖, 等. 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. DOI
LI Yijie, SONG Heng, YE Chenhui, et al. EV charging load forecasting based on model driven and data driven[J]. Hunan Electric Power, 2023, 43 (3): 9- 15. DOI
36李美洲, 陈辰, 倪洪飞, 等. 数据驱动下小型纯电动汽车充电策略研究[J]. 公路交通科技, 2024, 41 (3): 190- 198. DOI
LI Meizhou, CHEN Chen, NI Hongfei, et al. Study on charging strategy of light-duty BEV driven by data[J]. Journal of Highway and Transportation Research and Development, 2024, 41 (3): 190- 198. DOI
37刘奕辰, 肖红谊, 刘晓宏, 等. 考虑绿证交易及碳排放约束的含风光储微网低碳优化调度[J]. 浙江电力, 2024, 43 (8): 66- 73. DOI
LIU Yichen, XIAO Hongyi, LIU Xiaohong, et al. Low-carbon optimal scheduling for hybrid wind/PV/storage microgrids considering GEC trading and carbon emission constraints[J]. Zhejiang Electric Power, 2024, 43 (8): 66- 73. DOI
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