基于大规模V2G的区域电源低碳优化策略

doi:10.12204/j.issn.1000-7229.2022.12.006

电力建设 ›› 2022, Vol. 43 ›› Issue (12): 56-65.doi: 10.12204/j.issn.1000-7229.2022.12.006

基于大规模V2G的区域电源低碳优化策略

檀勤良¹^,²^,³(), 郭明鑫¹(), 刘源¹(), 韩健¹(), 梅书凡¹(), 丁毅宏¹()

1.华北电力大学经济与管理学院,北京市 102206
2.北京市能源发展研究基地,北京市 102206
3.华北电力大学新能源电力与低碳发展研究北京市重点实验室,北京市 102206

收稿日期:2022-04-12 出版日期:2022-12-01 发布日期:2022-12-06
通讯作者: 檀勤良 E-mail:tan.qinliang1@gmail.com;878118840@qq.com;clarencely517@163.com;hj19940929@163.com;524053493@qq.com;dingyh0528@163.com
作者简介:郭明鑫(1998),男,硕士研究生,主要研究方向为管理科学与工程,E-mail:878118840@qq.com;
刘源(1988),男,博士,讲师,主要研究方向为能源系统分析、环境政策研究,E-mail:clarencely517@163.com;
韩健(1994),男,博士研究生,主要研究方向为能源经济与低碳发展,E-mail:hj19940929@163.com;
梅书凡(1995),男,博士研究生,主要研究方向为能源环境建模与优化,E-mail:524053493@qq.com;
丁毅宏(1995),男,博士研究生,主要研究方向为低碳能源经济,E-mail:dingyh0528@163.com。
基金资助:
国家自然科学基金项目(71874053)

Research on Low-Carbon Optimization Strategy of Regional Power Supply Based on Large-Scale V2G

TAN Qinliang¹^,²^,³(), GUO Mingxin¹(), LIU Yuan¹(), HAN Jian¹(), MEI Shufan¹(), DING Yihong¹()

1. School of Economics and Management, North China Electric Power University, Beijing 102206, China
2. Research Center for Beijing Energy Development, Beijing 102206, China
3. Beijing Key Laboratory of Renewable Electric Power and Low Carbon Development, North China Electric Power University, Beijing 102206, China

Received:2022-04-12 Online:2022-12-01 Published:2022-12-06
Contact: TAN Qinliang E-mail:tan.qinliang1@gmail.com;878118840@qq.com;clarencely517@163.com;hj19940929@163.com;524053493@qq.com;dingyh0528@163.com
Supported by:
National Natural Science Foundation of China(71874053)

摘要/Abstract

摘要：

碳达峰背景下可再生能源占比增加会降低系统灵活性、提高经济成本,并对电网运行稳定性造成冲击。随着电动汽车规模的扩大,其大规模接入电网时也会因充电不确定性而影响电网的稳定性。V2G(vehicle-to-grid)技术的实施使电动汽车规模化参与调峰辅助服务成为可能,故应将其纳入到未来的电力系统规划中。在考虑大规模电动汽车参与V2G调峰的基础上,重点研究了季节因素对电动汽车参与V2G出力的影响。以系统运行成本最小、电网侧负荷波动最小、用户侧经济收益最大建立了多目标规划模型,来优化电源结构,减少电源侧碳排放,提高系统整体经济效益。以我国河北省区域作为算例,设置不同情景进行研究分析。结果表明,规划期内V2G参与比例为70%时结果最优,电源侧碳排放降低3.45%,风、光消纳量提高10.18%,能够有效推动电源结构转型。

关键词: 电源规划, V2G, 碳达峰, 可再生能源, 多目标优化, 削峰填谷

Abstract:

The increase of the proportion of renewable energy will reduce the flexibility, increase the economic cost, and have an impact on the stability of power grid operation. With the expansion of the scale of electric vehicles, their large-scale access to the power grid will also affect the stability of the power grid due to charging uncertainty. The implementation of vehicle to grid (V2G) technology makes it possible for electric vehicles to participate in peak-shaving auxiliary services on a large scale, so it should be included in the future power system planning. Aiming at large-scale electric vehicles participating in peak shaving, this paper focuses on the influence of seasonal factors on electric vehicles participating in V2G output. In the research process, a multi-objective programming model is established to minimize the system operation cost, minimize the load fluctuation on the grid side and maximize the economic benefits on the user side, so as to optimize the power supply structure, reduce the carbon emission on the power supply side and improve the overall economic benefits of the system. Taking Hebei Province of China as an example, different scenarios are set for research and analysis. The results show that, when the participation ratio of V2G is 70% in the planning period, the result is the best, and the carbon emission on the power side is effectively reduced by 3.45%. The consumption of wind and solar energy can be increased by 10.18%, promoting the transformation of power structure.

Key words: power planning, V2G, carbon peak, renewable energy, multi-objective optimization, peak-shaving and valley-filling

中图分类号:

TM74

檀勤良, 郭明鑫, 刘源, 韩健, 梅书凡, 丁毅宏. 基于大规模V2G的区域电源低碳优化策略[J]. 电力建设, 2022, 43(12): 56-65.

TAN Qinliang, GUO Mingxin, LIU Yuan, HAN Jian, MEI Shufan, DING Yihong. Research on Low-Carbon Optimization Strategy of Regional Power Supply Based on Large-Scale V2G[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(12): 56-65.

图/表 14

表1 规划期内部分预测数据

Table 1 Partial forecast data in the planning period

表2 规划期内电源侧部分基础数据

Table 2 Basic data of power supply side during the planning period

表3 规划期内电动汽车基础数据

Table 3 Basic data of electric vehicles in the planning period

表4 各情景设置

Table 4 Scenario settings

图1 各情景规划期内电源侧年总碳排放量

Fig.1 Total annual carbon emission of power supply side in the planning period of each scenario

图2 各情景规划期内电源侧总成本变化情况

Fig.2 Changes in total cost of power supply side in the planning period of each scenario

表5 各情景规划期机组新建容量情况

Table 5 New capacity of units in the planning period of each scenario

图3 情景d中2021年各季节V2G参与前后负荷曲线

Fig.3 Load curve before and after V2G participation in each season in 2021 in scenario d

图4 情景d中2029年各季节V2G参与前后负荷曲线

Fig.4 Load curve before and after V2G participation in each season in 2029 in scenario d

图5 情景d中2035年各季节V2G参与前后负荷曲线

Fig.5 Load curve before and after V2G participation in each season in 2035 in scenario d

图6 情景d中电动汽车参与V2G前后总充电成本

Fig.6 Total charging cost of EV before and after V2G in scenario d

图7 情景d中电源新建装机规划结果

Fig.7 Planning results of new power installation in scenario d

图8 情景d中各电源年发电量

Fig.8 Annual power generation of each power source in scenario d

图9 2035年各情景可再生能源发电量占比

Fig.9 Proportion of renewable energy power generation in 2035

参考文献 20

[1]	贺瑜环, 杨秀媛, 陈麒宇, 等. 电动汽车智能充放电控制与应用综述[J]. 发电技术, 2021, 42(2): 180-192. doi: 10.12096/j.2096-4528.pgt.20022
	HE Yuhuan, YANG Xiuyuan, CHEN Qiyu, et al. Review of intelligent charging and discharging control and application of electric vehicles[J]. Power Generation Technology, 2021, 42(2): 180-192. doi: 10.12096/j.2096-4528.pgt.20022
[2]	李文博, 龙如银, 张琳玲. 电力跨区传输视角下电动汽车规模化应用的碳排放转移[J]. 北京理工大学学报(社会科学版), 2022, 24(1): 12-23.
	LI Wenbo, LONG Ruyin, ZHANG Linling. Carbon emission transfer by large-scale application of electric vehicles from the perspective of inter-regional electricity transmission[J]. Journal of Beijing Institute of Technology (Social Sciences Edition), 2022, 24(1): 12-23.
[3]	国家发展和改革委员会能源研究所, 国家可再生能源中心, 清华大学, 等. 新能源发电与电动汽车协同发展战略研究[R/OL]. (2019-11-08) [2022-01-12]. https://max.book118.com/html/2019/1108/5143213144002200.shtm.
[4]	黄艳岩, 朱斌, 谷泓杰, 等. 基于V2G技术的微电网最优运营规划策略[J]. 智慧电力, 2021, 49(3): 26-31, 45.
	HUANG Yanyan, ZHU Bin, GU Hongjie, et al. Optimal operation planning strategy for microgrid considering V2G technology[J]. Smart Power, 2021, 49(3): 26-31, 45.
[5]	蒋红进, 蒋红彪, 朱兰, 等. 计及电动汽车不确定性的微电网规划研究[J]. 电测与仪表, 2018, 55(8): 58-65, 76.
	JIANG Hongjin, JIANG Hongbiao, ZHU Lan, et al. Research on micro-grid planning considering uncertainty of electric vehicle[J]. Electrical Measurement & Instrumentation, 2018, 55(8): 58-65, 76.
[6]	王博, 艾欣. 考虑V2G用户响应度的峰谷电价时段优化有序充电[J]. 现代电力, 2016, 33(2): 39-44.
	WANG Bo, AI Xin. Coordinated charging of peak-valley time-period optimization by considering V2G user reactivity[J]. Modern Electric Power, 2016, 33(2): 39-44.
[7]	王誉博, 龚庆武, 乔卉, 等. 考虑电动汽车用户意愿的滚动优化调度[J]. 电力自动化设备, 2022, 42(10):54-61.
	WANG Yubo, GONG Qingwu, QIAO Hui, et al. Adaptive adjustment mechanism of electric vehicle charging service fee based on smart contract[J]. Electric Power Automation Equipment, 2022, 42(10):54-61.
[8]	陈盛凯, 杨伟, 徐泽, 等. 考虑电动汽车V2G的分布式电源优化配置[J]. 电力电容器与无功补偿, 2021, 42(5): 143-149.
	CHEN Shengkai, YANG Wei, XU Ze, et al. Optimal allocation for distributed generation considering V2G of electric vehicle[J]. Power Capacitor & Reactive Power Compensation, 2021, 42(5): 143-149.
[9]	FATHABADI H. Utilization of electric vehicles and renewable energy sources used as distributed generators for improving characteristics of electric power distribution systems[J]. Energy, 2015, 90: 1100-1110. doi: 10.1016/j.energy.2015.06.063 URL
[10]	黄汉远, 顾丹珍, 郑德博. 远期大规模的电动汽车与分布式光伏接入配电网的负荷预测模型[J]. 可再生能源, 2021, 39(5): 650-657.
	HUANG Hanyuan, GU Danzhen, ZHENG Debo. The load forecasting model of long-term large-scale electric vehicle and distributed photovoltaic access distribution network[J]. Renewable Energy Resources, 2021, 39(5): 650-657.
[11]	潘振宁, 余涛, 王克英. 考虑多方主体利益的大规模电动汽车分布式实时协同优化[J]. 中国电机工程学报, 2019, 39(12): 3528-3541.
	PAN Zhenning, YU Tao, WANG Keying. Decentralized coordinated dispatch for real-time optimization of massive electric vehicles considering various interests[J]. Proceedings of the CSEE, 2019, 39(12): 3528-3541.
[12]	罗继东, 邹梦丽, 侯宝华, 等. 考虑V2G及碳排放量的风光储综合能源系统协调优化运行[J/OL]. 电测与仪表, 2022 [2022-04-02]. http://kns.cnki.net/kcms/detail/23.1202.TH.20211008.1549.002.html.
	LUO Jidong, ZOU Mengli, HOU Baohua, et al. Coordinated and optimal operation of wind solar energy storage system considering V2G and carbon emission[J/OL]. Electrical Measurement & Instrumentation, 2022 [2022-04-02]. http://kns.cnki.net/kcms/detail/23.1202.TH.20211008.1549.002.html.
[13]	杨蕾, 吴琛, 黄伟, 等. 含高比例风光新能源电网的多目标无功优化算法[J]. 电力建设, 2020, 41(7): 100-109. doi: 10.12204/j.issn.1000-7229.2020.07.013
	YANG Lei, WU Chen, HUANG Wei, et al. Pareto-based multi-objective reactive power optimization for power grid with high-penetration wind and solar renewable energies[J]. Electric Power Construction, 2020, 41(7): 100-109. doi: 10.12204/j.issn.1000-7229.2020.07.013
[14]	詹长杰, 周步祥. 基于PCA-SVM模型的中长期电力负荷预测[J]. 电测与仪表, 2015, 52(9): 6-10, 40.
	ZHAN Changjie, ZHOU Buxiang. The medium and long term power load forecasting model based on PCA-SVM[J]. Electrical Measurement & Instrumentation, 2015, 52(9): 6-10, 40.
[15]	杨伍梅, 刘陶文. 基于MATLAB的多目标规划问题的理想点法求解[J]. 湖南城市学院学报(自然科学版), 2017, 26(4): 60-63.
	YANG Wumei, LIU Taowen. Research on the ideal point method of multi-objective programming based on MATLAB[J]. Journal of Hunan City University (Natural Science), 2017, 26(4): 60-63.
[16]	CHEN C, ZENG X T, YU L, et al. Planning energy-water nexus systems based on a dual risk aversion optimization method under multiple uncertainties[J]. Journal of Cleaner Production, 2020, 255: 120100. doi: 10.1016/j.jclepro.2020.120100 URL
[17]	ZHEN J L, WU C B, LIU X R, et al. Energy-water nexus planning of regional electric power system within an inexact optimization model in Tangshan City, China[J]. Journal of Cleaner Production, 2020, 266: 121997. doi: 10.1016/j.jclepro.2020.121997 URL
[18]	王彪, 尹霞. 实时电价下含V2G功能的电动汽车理性充放电模型及其分析[J]. 电力系统保护与控制, 2016, 44(24): 90-96.
	WANG Biao, YIN Xia. Modeling and analysis on the rational charging and discharging of electric vehicle with V2G function under real-time prices[J]. Power System Protection and Control, 2016, 44(24): 90-96.
[19]	罗浩成, 胡泽春, 张洪财. 环境温度对电动汽车充电负荷的影响分析[J]. 电力建设, 2015, 36(7): 69-74. doi: 10.3969/j.issn.1000-7229.2015.07.009
	LUO Haocheng, HU Zechun, ZHANG Hongcai. Effect analysis of ambient temperature on electric vehicle charging load[J]. Electric Power Construction, 2015, 36(7): 69-74. doi: 10.3969/j.issn.1000-7229.2015.07.009
[20]	檀勤良, 丁毅宏, 魏咏梅, 等. 碳交易及模糊预算下火电企业碳减排最优策略研究[J]. 电网技术, 2019, 43(10):3707-3715.
	TAN Qinliang, DING Yihong, WEI Yongmei, et al. Research on optimal strategy of carbon emission reduction for thermal power enterprises under carbon trading and fuzzy budget[J]. Power System Technology, 2019, 43(10):3707-3715.

基于大规模V2G的区域电源低碳优化策略

Research on Low-Carbon Optimization Strategy of Regional Power Supply Based on Large-Scale V2G

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