面向低碳交通的含新能源汽车共享站电-氢微能源网区间-随机混合规划方法

doi:10.19595/j.cnki.1000-6753.tces.L10038

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (2122 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要

在新能源汽车快速发展下,为充分发挥其对交通与能源领域低碳转型目标实现的支撑作用,该文考虑由电动汽车与氢燃料汽车间用车需求可替代性带来的负荷灵活性,采用综合资源规划理念,提出一种含新能源汽车共享站的电-氢微能源网（VSEHS）两阶段规划框架。首先,结合不同参数数据源的异质性,采用区间和概率相结合的混合建模方法处理不确定性,并开展基于演化博弈的用户多车型选择长期偏好及其演化建模,刻画共享汽车服务下用户需求对租赁定价的潜在灵活性;其次,在利用租赁价格引导用户需求的基础上,考虑多种不确定性因素影响下的系统运行模拟,构建基于区间-随机混合规划的VSEHS容量配置与定价决策优化模型;然后,提出区间-随机混合优化方法,将不同类型的不确定性变量归一化,并在同一框架下基于启发式算法实现高效求解;最后,通过相关算例分析,验证所提方法的有效性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	王雨晴
	王文诗
	徐心竹
	蔡瑞宁
	张卫翔
	曾博

关键词 ：新能源汽车共享站, 电-氢微能源网规划, 演化博弈, 分时租赁定价, 区间-随机混合优化

Abstract：

Under the rapid development of electric vehicles (EV) and hydrogen fuel vehicles (HFV), the construction of electric-hydrogen coupling micro-energy networks based on terminal parking lots and renewable energy hydrogen production technology is an important means to effectively promote the low-carbon transformation of the transportation field. The current research on micro-energy grid planning based on parking lot rarely considers the load flexibility brought by the traffic substitutability between EV and HFV, and guides users' long-term vehicle choice preferences by optimizing the price to promotes the consumption of renewable energy source(RES). Considering that the new energy vehicles (NEV) sharing station under the leasing mode is one of the typical scenarios to realize the vehicle demand substitution and energy conversion demand transformation between EV and HFV, this paper takes the new energy vehicle sharing station-based electro-hydrogen micro-energy system (VSEHS) as the research object. In this paper, a two-stage VSEHS planning framework with capacity allocation and vehicle rental price co-optimization is proposed based on the concept of comprehensive resource planning and hybrid interval/stochastic optimization method.
Firstly, the VSEHS architecture and the low-carbon operation mode considering multi-energy and multi-vehicle coordination is described. Secondly, according to the difficulty of obtaining the probabilistic distribution, a hybrid modeling method combining interval and probability is used to deal with the uncertainty of supply side and load side. Secondly, the long-term preference and evolutionary modeling of users' car-rental based on evolutionary game are carried out to describe the potential flexibility of user demand on rental pricing under car-sharing services. And then, on the basis of using rental price to guide user demand, considering the influence of various uncertain factors, the capacity allocation and pricing decision optimization model of VSEHS based on interval-stochastic hybrid programming is constructed. Finally, an interval-stochastic hybrid optimization method is proposed to normalize different types of uncertain variables, and achieve efficient solutions based on heuristic algorithms under the same framework.
The simulation results of VSEHS planning for an urban community show that, under the optimum planning scheme, the rental prices of EV and HFV are 0.483 ¥/min and 0.717 ¥/min respectively. At this price level, 30% of users refuse to participate in the rental service, 49% and 21% of users choose to rent EV and HFV, respectively. Through the coordinated optimization of vehicle scheduling and electric-hydrogen energy conversion scheduling, the hydrogen self-sufficiency rate of the system reaches 91.3%, which can ensure that HFV is basic comes from the RES. The planning schemes under different scenarios show that, compared with the independent operation, the annual net investment and environmental benefits of electric-hydrogen integrated planning can be increased by 11.97% and 24.71%, respectively, and the dynamic optimal pricing mechanism has obvious advantages over the fixed price mechanism. This proves that the electric-hydrogen integrated planning can make greater use of RES and reduce the dependence on external market for electricity and hydrogen energy; for another, it illustrates that under the optimal pricing mechanism, the system can guide the user's choice preferences by energy substitutability in the user's vehicle demand, and then adjust its own planning and operation strategy according to the user's demand. Thus, the improvement of investment efficiency is realized. Finally, the planning schemes of interval/stochastic hybrid optimization and pure stochastic optimization are compared. The results show that, the planning scheme obtained by the pure stochastic optimization is highly dependent on the distribution information of the given data, and the proposed interval/stochastic hybrid planning scheme has better general reliability.
The following conclusions can be drawn from the simulation analysis: (1) the VSEHS planning model considering electron-hydrogen coupling and joint operation of different models can achieve the balance between supply and demand, also realize that NEVs can basically supplied by RES while ensuring the economic benefits. (2) The optimal mechanism of vehicle rental price can not only guide the long-term preferences of users in rental cars to match the planning and operation of VSEHS, but also realize the linkage between price and system cost, which makes full use of the low cost advantage of comprehensive resource planning mode, and increase the interaction profit between supply and demand side. (3) Compared with the traditional pure stochastic optimization method, the VSEHS planning scheme based on interval-random hybrid optimization method can effectively adapt to the situation where the prior information of some parameters is unknown, and has better versatility and reliability.

Key words： New energy vehicle sharing station electro-hydrogen micro-energy network planning evolutionary game pricing for vehicle sharing interval-stochastic hybrid optimization

收稿日期: 2023-01-13

PACS:

TM715

基金资助:

河北省自然科学基金（G2022502004）、国家自然科学基金（62133015）和中央高校基本科研业务费专项资金（2022JG005）资助项目

通讯作者: 王文诗女,1998年生,硕士,研究方向为电-氢综合能源系统规划。E-mail：qqmavis121@163.com

作者简介: 王雨晴女,1994年生,博士,讲师,硕士生导师,研究方向为综合能源系统规划。E-mail：wangyuqingncepu@foxmail.com

引用本文:

王雨晴, 王文诗, 徐心竹, 蔡瑞宁, 张卫翔, 曾博. 面向低碳交通的含新能源汽车共享站电-氢微能源网区间-随机混合规划方法[J]. 电工技术学报, 2023, 38(23): 6373-6390. Wang Yuqing, Wang Wenshi, Xu Xinzhu, Cai Ruining, Zhang Weixiang, Zeng Bo. Hybrid Interval/Stochastic Planning Method for New Energy Vehicle Sharing Station-based Electro-Hydrogen Micro-Energy System for Low-Carbon Transportation. Transactions of China Electrotechnical Society, 2023, 38(23): 6373-6390.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.L10038 https://dgjsxb.ces-transaction.com/CN/Y2023/V38/I23/6373

[1] 国务院. 国务院关于印发2030年前碳达峰行动方案的通知[Z/OL]. (2021-10-24). http://www.gov.cn/zhengce/content/2021-10/26/content_5644984.htm.
[2] 国务院办公厅. 国务院办公厅关于印发新能源汽车产业发展规划(2021—2035年)的通知[Z/OL]. (2020-10-20). http://www.gov.cn/zhengce/content/2020-11/02/content_5556716.htm.
[3] 中国氢能源行业发展前景预测与投资战略规划分析报告[R]. https://bg.qianzhan.com/report/detail/1607281732570984.html?v=title.
[4] 陈中, 陈妍希, 车松阳. 新能源汽车一体充能站框架及能量优化调度方法[J]. 电力系统自动化, 2019, 43(24): 41-49.
Chen Zhong, Chen Yanxi, Che Songyang.Framework of integrated charging station for renewable energy vehicle and energy optimal dispatching method[J]. Automation of Electric Power Systems, 2019, 43(24): 41-49.
[5] 王海鑫, 袁佳慧, 陈哲, 等. 智慧城市车-站-网一体化运行关键技术研究综述及展望[J]. 电工技术学报, 2022, 37(1): 112-132.
Wang Haixin, Yuan Jiahui, Chen Zhe, et al.Review and prospect of key techniques for vehicle-station-network integrated operation in smart city[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 112-132.
[6] 毛玲, 张钟浩, 赵晋斌, 等. 车-桩-网交融技术研究现状及展望[J]. 电工技术学报, 2022, 37(24): 6357-6371.
Mao Ling, Zhang Zhonghao, Zhao Jinbin, et al.Research status and prospects of fusion technology of vehicle-charging pile-power grid[J]. Transactions of China Electrotechnical Society, 2022, 37(24): 6357-6371.
[7] 翁菖宏, 胡志坚, 刘妍, 等. 计及互联调控的新能源汽车一体化供能站规划[J]. 智慧电力, 2021, 49(9): 24-31, 94.
Weng Changhong, Hu Zhijian, Liu Yan, et al.Integrated power supply station planning of new energy vehicles with interconnection control[J]. Smart Power, 2021, 49(9): 24-31, 94.
[8] Cao Xiaoyu, Sun Xunhang, Xu Zhanbo, et al.Hydrogen-based networked microgrids planning through two-stage stochastic programming with mixed-integer conic recourse[J]. IEEE Transactions on Automation Science and Engineering, 2022, 19(4): 3672-3685.
[9] 吴孟雪, 房方. 计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化[J]. 电工技术学报, 2023, 38(13): 3473-3485.
Wu Mengxue, Fang Fang.Distributionally Robust optimization of electricity-heat-hydrogen integrated energy system with wind and solar uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485.
[10] Liu Jia, Cao Sunliang, Chen Xi, et al.Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage[J]. Applied Energy, 2021, 281: 116038.
[11] 李珂, 邵成成, 王雅楠, 等. 考虑电-气-交通耦合的城市综合能源系统规划[J]. 中国电机工程学报, 2023, 43(6): 2263-2273.
Li Ke, Shao Chengcheng, Wang Yanan, et al.Optimal planning of urban integrated energy systems considering electricity-gas-transportation interactions[J]. Proceedings of the CSEE, 2023, 43(6): 2263-2273.
[12] 袁铁江, 计力, 田雪沁, 等. 考虑燃料电池汽车加氢负荷的电-氢系统协同优化运行[J]. 电力系统自动化, 2023, 47(5): 16-25.
Yuan Tiejiang, Ji Li, Tian Xueqin, et al.Synergistic optimal operation of electricity-hydrogen systems considering hydrogen refueling loads for fuel cell vehicles[J]. Automation of Electric Power Systems, 2023, 47(5): 16-25.
[13] 闫佳佳, 滕云, 邱实, 等. 计及供能可靠性动态约束与碳减排的充能型微电网互联系统优化模型[J]. 电工技术学报, 2022, 37(23): 5956-5975.
Yan Jiajia, Teng Yun, Qiu Shi, et al.Optimization model of charging microgrid interconnection system considering dynamic constraints of energy supply reliability and carbon emission reduction[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5956-5975.
[14] Ran Cuiling, Zhang Yanzi, Yin Ying.Demand response to improve the shared electric vehicle planning: managerial insights, sustainable benefits[J]. Applied Energy, 2021, 292: 116823.
[15] Aliasghari P, Mohammadi-Ivatloo B, Alipour M, et al.Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program[J]. Journal of Cleaner Production, 2018, 186: 293-303.
[16] Salyani P, Abapour M, Zare K.Stackelberg based optimal planning of DGs and electric vehicle parking lot by implementing demand response program[J]. Sustainable Cities and Society, 2019, 51: 101743.
[17] Shojaabadi S, Abapour S, Abapour M, et al.Optimal planning of plug-in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties[J]. IET Generation, Transmission & Distribution, 2016, 10(13): 3330-3340.
[18] 程妤. 基于心理账户的共享汽车出行选择行为研究[D]. 重庆: 重庆交通大学, 2020.
[19] 程瑜, 邰宇峰, 丁肇豪, 等. 基于网络流的共享电动汽车优化调度[J]. 电工技术学报, 2022, 37(增刊1): 145-152.
Cheng Yu, Tai Yufeng, Ding Zhaohao, et al.Optimal scheduling of sharing electric vehicles based on network flow[J]. Transactions of China Electrotechnical Society, 2022, 37(S1): 145-152.
[20] 王亮. 考虑个体异质性的新能源汽车分时租赁出行选择行为研究[D]. 成都: 西南交通大学, 2020.
[21] Fan Jingli, Wang Jiaxing, Zhang Xian.An innovative subsidy model for promoting the sharing of electric vehicles in China: a pricing decisions analysis[J]. Energy, 2020, 201: 117557.
[22] Wang Yue, Yao Enjian, Pan Long.Electric vehicle drivers’ charging behavior analysis considering heterogeneity and satisfaction[J]. Journal of Cleaner Production, 2021, 286: 124982.
[23] Wu Fuzhang, Yang Jun, Zhan Xiangpeng, et al.The online charging and discharging scheduling potential of electric vehicles considering the uncertain responses of users[J]. IEEE Transactions on Power Systems, 2021, 36(3): 1794-1806.
[24] 冰雪. 考虑供需时空特征的共享电动汽车差异化定价研究[D]. 长春: 吉林大学, 2022.
[25] 姬杨蓓蓓, 陈欣萌, 成枫. 考虑排放成本的共享电动汽车和共享停车位最优组合定价研究[J]. 管理工程学报, 2022, 36(1): 134-145.
Ji Yangbeibei, Chen Xinmeng, Cheng Feng.Research on optimal pricing of shared vehicles and shared parking considering traffic emission cost[J]. Journal of Industrial Engineering and Engineering Management, 2022, 36(1): 134-145.
[26] Zeynali S, Nasiri N, Marzband M, et al.A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets[J]. Applied Energy, 2021, 300: 117432.
[27] Kou Xiao, Li Fangxing.Interval optimization for available transfer capability evaluation considering wind power uncertainty[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 250-259.
[28] Daryabari M K, Keypour R, Golmohamadi H.Robust self-scheduling of parking lot microgrids leveraging responsive electric vehicles[J]. Applied Energy, 2021, 290: 116802.
[29] Tan Jun, Wang Lingfeng.Integration of plug-in hybrid electric vehicles into residential distribution grid based on two-layer intelligent optimization[J]. IEEE Transactions on Smart Grid, 2014, 5(4): 1774-1784.
[30] Lu Xinhui, Zhou Kaile, Yang Shanlin, et al.Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles[J]. Journal of Cleaner Production, 2018, 195: 187-199.
[31] Khezri R, Mahmoudi A, Haque M H.Impact of optimal sizing of wind turbine and battery energy storage for a grid-connected household with/without an electric vehicle[J]. IEEE Transactions on Industrial Informatics, 2022, 18(9): 5838-5848.
[32] 陆剑清. 现代消费行为学[M]. 北京: 北京大学出版社, 2013.
[33] 梅生伟, 刘锋, 魏韡. 工程博弈论基础及电力系统应用[M]. 北京: 科学出版社, 2016.
[34] 杨雄. 分时租赁电动汽车运行数据驱动的充/换电设施布局研究[D]. 北京: 北京交通大学, 2020.
[35] Ahn H, Rim D, Pavlak G S, et al.Uncertainty analysis of energy and economic performances of hybrid solar photovoltaic and combined cooling, heating, and power (CCHP+PV) systems using a Monte-Carlo method[J]. Applied Energy, 2019, 255: 113753.
[36] Mei Fei, Zhang Jiatang, Lu Jixiang, et al.Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations[J]. Energy, 2021, 219: 119629.
[37] Farham H, Mohammadian L, Alipour H, et al.Energy procurement of large industrial consumer via interval optimization approach considering peak demand management[J]. Sustainable Cities and Society, 2019, 46: 101421.
[38] Sengupta A, Pal T K.On comparing interval numbers[J]. European Journal of Operational Research, 2000, 127(1): 28-43.
[39] Liu Yangyang, Jiang Chuanwen, Shen Jingshuang, et al.Coordination of hydro units with wind power generation using interval optimization[J]. IEEE Transactions on Sustainable Energy, 2015, 6(2): 443-453.
[40] Pan Guangsheng, Gu Wei, Qiu Haifeng, et al.Bi-level mixed-integer planning for electricity-hydrogen integrated energy system considering levelized cost of hydrogen[J]. Applied Energy, 2020, 270: 115176.
[41] Dong Xiangxiang, Wu Jiang, Xu Zhanbo, et al.Optimal coordination of hydrogen-based integrated energy systems with combination of hydrogen and water storage[J]. Applied Energy, 2022, 308: 118274.
[42] Zhong Xiaoqing, Zhong Weifeng, Liu Yi, et al.Cooperative operation of battery swapping stations and charging stations with electricity and carbon trading[J]. Energy, 2022, 254: 124208.
[43] Zhang Xizheng, Wang Zeyu, Lu Zhangyu.Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm[J]. Applied Energy, 2022, 306: 118018.
[44] Li Junjie, Cheng Wanjing.Comparative life cycle energy consumption, carbon emissions and economic costs of hydrogen production from coke oven gas and coal gasification[J]. International Journal of Hydrogen Energy, 2020, 45(51): 27979-27993.
[45] Jiang Qian, Mu Yunfei, Jia Hongjie, et al.A Stackelberg Game-based planning approach for integrated community energy system considering multiple participants[J]. Energy, 2022, 258: 124802.
[46] Mu Yunfei, Chen Wanqing, Yu Xiaodan, et al.A double-layer planning method for integrated community energy systems with varying energy conversion efficiencies[J]. Applied Energy, 2020, 279: 115700.
[47] Isaac N, Saha A K.Analysis of refueling behavior of hydrogen fuel vehicles through a stochastic model using Markov Chain process[J]. Renewable and Sustainable Energy Reviews, 2021, 141: 110761.
[48] Li Jiale, Liu Zhenbo, Wang Xuefei.Public charging station localization and route planning of electric vehicles considering the operational strategy: a bi-level optimizing approach[J]. Sustainable Cities and Society, 2022, 87: 104153.