Bi-uncertainty Network Frame Planning Method for Distribution Network with Electric Vehicles

doi:10.11930/j.issn.1004-9649.201905117

Abstract

Abstract: Considering the stochastic uncertainty of charging load of electric vehicle (EV) and the fuzzy uncertainty of regular load in the distribution system, a hybrid chance-constrained distribution grid planning model including both fuzzy variables and random variables is established on the basis of the theory of uncertain planning. In this paper, the objective function is to minimize the total cost of fixed investment and fuzzy random network loss in the planning period, with the hybrid chance-constraint programming introduced to process the transmission power constraint and node voltage constraint. In addition, the balance can be well maintained between the investment cost and operation risk by setting two different confidence level parameter values. Finally, a genetic algorithm based on hybrid simulation is proposed to solve the model built in this paper, and a 25-node example is used to verify the correctness and validity of the proposed method.

Key words: EV, bi-uncertainty, mixed chance-constrained, hybrid simulation, distribution network planning

SHEN Yiting, ZHANG Jing, WU Peng, LIU Lu, YANG Jianlin. Bi-uncertainty Network Frame Planning Method for Distribution Network with Electric Vehicles[J]. Electric Power, 2020, 53(4): 139-146.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201905117

https://www.electricpower.com.cn/EN/Y2020/V53/I4/139

References

[1] 马建, 刘晓东, 陈轶嵩, 等. 中国新能源汽车产业与技术发展现状及对策[J]. 中国公路学报, 2018, 31(8): 1–19
MA Jian, LIU Xiaodong, CHEN Yisong, et al. Current status and countermeasures for China’s new energy automobile industry and technology development[J]. China Journal of Highway and Transport, 2018, 31(8): 1–19
[2] TABASSUM-ABBASI, PREMALATHA M, ABBASI T, et al. Wind energy: increasing deployment, rising environmental concerns[J]. Renewable and Sustainable Energy Reviews, 2014, 31: 270–288.
[3] 周念成, 熊希聪, 王强钢. 多种类型电动汽车接入配电网的充电负荷概率模拟[J]. 电力自动化设备, 2014, 34(2): 1–7
ZHOU Niancheng, XIONG Xicong, WANG Qianggang. Simulation of charging load probability for connection of different electric vehicles to distribution network[J]. Electric Power Automation Equipment, 2014, 34(2): 1–7
[4] 蔡德福, 钱斌, 陈金富, 等. 含电动汽车充电负荷和风电的电力系统动态概率特性分析[J]. 电网技术, 2013, 37(3): 590–596
CAI Defu, QIAN Bin, CHEN Jinfu, et al. Analysis on dynamic probabilistic characteristic of power grid connected with electric vehicle charging load and wind power[J]. Power System Technology, 2013, 37(3): 590–596
[5] 陈蓉珺, 何永秀, 陈奋开, 等. 基于系统动力学和蒙特卡洛模拟的电动汽车日负荷远期预测[J]. 中国电力, 2018, 51(9): 130–138
CHEN Rongjun, HE Yongxiu, CHEN Fenkai, et al. Long-term daily load forecast of electric vehicle based on system dynamics and monte carlo simulation[J]. Electric Power, 2018, 51(9): 130–138
[6] 杨方, 张义斌, 何博, 等. 电动汽车大规模充电对电网经济性的影响评价[J]. 中国电力, 2016, 49(3): 178–182
YANG Fang, ZHANG Yibin, HE Bo, et al. Evaluation of impact of large-scale electric vehicle loading on economic value of power system[J]. Electric Power, 2016, 49(3): 178–182
[7] 耿晓娜. 含光伏电源与充电站的配电网规划研究[D]. 北京: 华北电力大学, 2016.
GENG Xiaona. Research on distribution network planning with photovoltaic generation and charging station[D]. Beijing: North China Electric Power University, 2016.
[8] 颜炯, 万涛, 李浩松, 等. 计及不确定性因素的配电网网架规划方法[J]. 电力系统保护与控制, 2017, 45(18): 76–81
YAN Jiong, WAN Tao, LI Haosong, et al. Distribution network planning considering the uncertainties[J]. Power System Protection and Control, 2017, 45(18): 76–81
[9] 丁晓东. 不确定系统优化理论与应用研究[D]. 上海: 东华大学, 2002.
DING Xiaodong. A study on optimal theory and its application for uncertain system[D]. Shanghai: East China University, 2002.
[10] 盛四清, 孙晓霞. 考虑节能减排和不确定因素的含风电场机组组合优化[J]. 电力系统自动化, 2014, 38(17): 54–59
SHENG Siqing, SUN Xiaoxia. Unit commitment optimization containing wind farms considering energy saving,emission reduction and uncertainties[J]. Automation of Electric Power Systems, 2014, 38(17): 54–59
[11] 盛四清, 张立. 考虑风光荷预测误差的电力系统经济优化调度[J]. 电力系统及其自动化学报, 2017, 29(9): 80–85
SHENG Siqing, ZHANG Li. Economic dispatch of power system considering the prediction error of wind power, solar energy and load[J]. Proceeding of the CSU-EPSA, 2017, 29(9): 80–85
[12] 盛四清, 孙晓霞. 含风电场的混合机会约束经济调度模型[J]. 电力系统及其自动化学报, 2015, 27(8): 82–86, 102
SHENG Siqing, SUN Xiaoxia. Hybrid chance constrained economic dispatch model with wind farm[J]. Proceedings of the CSU-EPSA, 2015, 27(8): 82–86, 102
[13] 亓尧, 王瑛, 孟祥飞, 等. 基于乐观值和悲观值的不确定机会约束规划模型及应用[J]. 系统工程与电子技术, 2019, 41(2): 336–341
QI Yao, WANG Ying, MENG Xiangfei, et al. Model and application of uncertain chance-constrained programming based on optimistic and pessimistic value[J]. Systems Engineering and Electronics, 2019, 41(2): 336–341
[14] VYAS A, SANTINI D. Use of national surveys for estimation ‘full’ PHEV potential for oil use reduction[EB/OL]. (2008-07-21).http://www.transportation.anl.gov/HV/525.pdf.
[15] TAYLOR J, MAITRA A, ALEXANDER M, et al. Evaluation of the impact of plug-in electric vehicle loading on distribution system operations[C]//2009 IEEE Power & Energy Society General Meeting. Calgary, Canada. IEEE, 2009: 1-6.
[16] 田立亭, 史双龙, 贾卓. 电动汽车充电功率需求的统计学建模方法[J]. 电网技术, 2010, 34(11): 126–130
TIAN Liting, SHI Shuanglong, JIA Zhuo. A statistical model for charging power demand of electric vehicles[J]. Power System Technology, 2010, 34(11): 126–130
[17] 钱甜甜, 李亚平, 郭晓蕊, 等. 基于时空活动模型的电动汽车充电功率计算和需求响应潜力评估[J]. 电力系统保护与控制, 2018, 46(23): 127–134
QIAN Tiantian, LI Yaping, GUO Xiaorui, et al. Calculation of electric vehicle charging power and evaluation of demand response potential based on spatial and temporal activity model[J]. Power System Protection and Control, 2018, 46(23): 127–134
[18] 刘文学, 梁军, 贠志皓, 等. 基于可信理论的多目标模糊机会约束无功优化[J]. 电工技术学报, 2015, 30(21): 82–89
LIU Wenxue, LIANG Jun, YUN Zhihao, et al. Multi-objective fuzzy chance constrained optimal reactive power flow based on credibility theory[J]. Transactions of China Electrotechnical Society, 2015, 30(21): 82–89
[19] 杨宁, 文福拴. 基于机会约束规划的输电系统规划方法[J]. 电力系统自动化, 2004, 28(14): 23–27
YANG Ning, WEN Fushuan. Transmission system expansion planning based on chance constrained programming[J]. Automation of Electric Power Systems, 2004, 28(14): 23–27
[20] 李鹏, 王雪莹, 韩鹏飞. 双重不确定环境下的微网优化运行调度风险分析研究[J]. 中国电机工程学报, 2017, 37(15): 4296–4303, 4569
LI Peng, WANG Xueying, HAN Pengfei. Risk analysis of microgrid optimal operation scheduling under double uncertainty environment[J]. Proceedings of the CSEE, 2017, 37(15): 4296–4303, 4569
[21] 玄光男, 程润伟著, 汪定伟等译. 遗传算法与工程设计[M]. 北京: 科学出版社, 2000.
[22] 刘宝碇, 赵瑞清, 王纲.不确定规划及应用[M]. 北京: 清华大学出版社, 2003.
[23] NAHMAN J M, PERIC D M. Optimal planning of radial distribution networks by simulated annealing technique[J]. IEEE Transactions on Power Systems, 2008, 23(2): 790–795.