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基于灰狼優(yōu)化算法的電動汽車充電站選址研究

來源：電工電氣發(fā)布時間：2025-01-07 16:07 瀏覽次數(shù)：182

基于灰狼優(yōu)化算法的電動汽車充電站選址研究

黃燦¹，姚澤宇²，葛華鋒¹

(1 國網(wǎng)江蘇省電力有限公司蘇州供電分公司，江蘇蘇州 215004；

2 河海大學(xué) 計算機(jī)與軟件學(xué)院，江蘇南京 211100)

摘要：隨著電動汽車(EV)數(shù)量的增加，EV 充電站的不合理選址使得配電網(wǎng)的功率損耗增大、電壓分布不均。分別從運(yùn)營商和投資者角度提出了兩種 EV 充電站選址優(yōu)化目標(biāo)，然后基于配電網(wǎng)的功率損耗和充電站的安裝成本得到綜合目標(biāo)函數(shù)，基于灰狼優(yōu)化算法(GWO)尋找目標(biāo)函數(shù)全局最優(yōu)解，得到充電站的最優(yōu)選址配置。以 IEEE 34 節(jié)點(diǎn)系統(tǒng)為例，對接入最優(yōu)選址采用不同控制模式的 EV 充電站的潮流進(jìn)行對比分析，結(jié)果表明優(yōu)化配置后的配電網(wǎng)功率損耗明顯降低，電壓分布更加均衡；與粒子群算法和遺傳算法的優(yōu)化結(jié)果對比表明，GWO 算法具有更好的收斂性和魯棒性。

關(guān)鍵詞： 電動汽車；充電站；選址優(yōu)化；灰狼優(yōu)化算法；配電網(wǎng)；功率損耗；粒子群算法；遺傳算法

中圖分類號：TM910.6 ；U469.72 文獻(xiàn)標(biāo)識碼：A 文章編號：1007-3175(2024)12-0022-05

Research on Location of Electric Vehicle Charging

Station Based on Gray Wolf Optimizer Algorithm

HUANG Can¹, YAO Ze-yu², GE Hua-feng¹

（1 State Grid Jiangsu Electric Power Co., Ltd. Suzhou Power Supply Branch, Suzhou 215004, China;

2 College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China）

Abstract: With the increase of the number of electric vehicles(EV), the unreasonable location of EV charging stations has led to increased power loss in the distribution network and uneven voltage distribution. In this paper, two location optimization objectives for EV charging stations are proposed from the perspectives of operators and investors respectively. Then, a comprehensive objective function is derived based on the power loss of the distribution network and the installation cost of the charging stations. The global optimal solution of the objective function is sought based on the gray wolf optimizer(GWO) algorithm, and the optimal location configuration of the charging stations is obtained. Taking the IEEE 34-bus system as an example, a comparative analysis of the power flow in EV charging stations with different control modes at the optimal locations is conducted. The results indicate that the power loss in the optimized distribution network is significantly reduced, and the voltage distribution becomes more balanced; comparison of the optimization results with those of the particle swarm optimization(PSO) and genetic algorithm(GA) shows that the GWO algorithm has better convergence and robustness.

Key words: electric vehicle; charging station; optimal location; gray wolf optimizer algorithm; distribution network; power loss; particle swarm optimization; genetic algorithm

參考文獻(xiàn)

[1] 韓克勤，丁丹軍，錢科軍，等. 基于 NSGA-Ⅱ 的電動汽車充電站多目標(biāo)優(yōu)化規(guī)劃[J] . 電力需求側(cè)管理，2017，19(S1) ：72-76.

[2] 方兵，李琳瑋，黃亮，等. 計及儲能與電動汽車充電站的配電網(wǎng)經(jīng)濟(jì)運(yùn)行研究[J] . 電力需求側(cè)管理，2022，24(2) ：59-64.

[3] 孫銓杰. 大規(guī)模電動汽車接入電網(wǎng)的負(fù)荷需求及其影響研究[D]. 上海：上海電力大學(xué)，2021.

[4] SANGUESA J A, TORRES-SANZ V, GARRIDO P, et al.A Review on Electric Vehicles:Technologies and Challenges[J].Smart Cities，2021，4(1) ：372-404.

[5] 張強(qiáng). 基于多目標(biāo)優(yōu)化的電動汽車充電站選址研究[D].秦皇島：燕山大學(xué)，2021.

[6] ELDEEB H H, FADDEL S, MOHAMMED O A.Multi-Objective Optimization Technique for the Operation of Grid Tied PV Powered EV Charging Station[J].Electric Power Systems Research，2018， 164 ：201-211.

[7] 倪超，王菲，仇經(jīng)緯，等. 基于 MPGA 的電動汽車充電站選址規(guī)劃[J] . 電子技術(shù)與軟件工程，2020(19) ：226-227.

[8] 張藝涵，徐菁，李秋燕，等. 基于密度峰值聚類的電動汽車充電站選址定容方法[J] . 電力系統(tǒng)保護(hù)與控制，2021，49(5) ：132-139.

[9] ISLAM M M , SHAREEF H , MOHAMED A . Optimal Location and Sizing of Fast Charging Stations for Electric Vehicles by Incorporating Traffic and Power Networks[J].IET Intelligent Transport Systems， 2018， 12(8) ：947-957.

[10] NEYESTANI N, DAMAVANDI M Y, SHAFIE-KHAH M, et al.Allocation of Plug-In Vehicles' Parking Lots in Distribution Systems Considering Network-Constrained Objectives[J].IEEE Transactions on Power Systems， 2015， 30(5) ：2643-2656.

[11] 程宏波，肖永樂，王勛，等. 考慮低碳收益的電動汽車充電站選址規(guī)劃[J] . 中國電力，2016，49(7) ：118-121.

[12] 曹佳佳，王淳，霍崇輝，等. 考慮配電網(wǎng)負(fù)荷波動和電壓偏移的充電站優(yōu)化規(guī)劃[J] . 電力科學(xué)與技術(shù)學(xué)報，2021，36(4) ：12-19.

[13] 謝林偉. 基于自適應(yīng)粒子群算法的電動汽車充電站優(yōu)化規(guī)劃[J]. 陜西電力，2012，40(11) ：34-37.

[14] MIRJALILI S, ALJARAH I, MAFARJA M, et al.Grey Wolf Optimizer:Theory， Literature Review，and Application in Computational Fluid Dynamics Problems[J].Nature-Inspired Optimizers，2020，811 ：87-105.

[15] 趙超，王斌，孫志新，等. 基于改進(jìn)灰狼算法的獨(dú)立微電網(wǎng)容量優(yōu)化配置[J] . 太陽能學(xué)報，2022，43(1) ：256-262.

[16] NICK M , CHERKAOUI R , PAOLONE M . Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support[J].IEEE Transactions on Power Systems， 2014， 29(5) ：2300-2310.

[17] OWUOR J O, MUNDA J L, JIMOH A A.The IEEE 34 Node Radial Test Feeder as a Simulation Testbench for Distributed Generation[C]//IEEE Africon'11， 2011 ：1-6.

[18] 馬臨超，楊捷，郭貝貝，等. 考慮電壓約束的分布式光伏最大準(zhǔn)入功率模型[J] . 河南工學(xué)院學(xué)報，2022，30(1) ：6-12.

[19] LONG Wen, WU Tiebin, CAI Shaohong, et al.A Novel Grey Wolf Optimizer Algorithm with Refraction Learning[J].IEEE Access， 2019， 7 ：57805-57819.

[20] 王清玉，李宏亮，朱玉，等. 基于牛頓-拉夫遜算法和 P-Q 分解法的潮流計算對比分析[J] . 機(jī)電信息，2019(24) ：20-21.

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基于灰狼優(yōu)化算法的電動汽車充電站選址研究