摘要
光储充电站(PSCS)的规模化部署是电动汽车(EV)快速普及的关键因素。合理规划光储充电站的运行模式并有效调度多种能源,优化需求供给链,最大化运行效益,是光储充电站可持续运营发展的重点。针对目前光储充电站需求侧的不确定性和供给侧的协调性问题,面向汽车充电预约场景,在需求侧综合考虑汽车充电需求和剩余停车时长等因素,决策相应EV的充电方式。在供给调度侧设计一种基于带精英策略的遗传混合递推算法(EGAHR)进行能量优化调度,以最小化电网取电费用。以EV充电时间片为基本调控时间单元,通过协调需求侧和供给侧的调度信息,合理调度光伏、储能、电网等能源的能量,满足当前时间片内EV充电需求的同时优化系统电费。实验结果表明,基于EGAHR算法的策略相比基于遗传算法、灰狼算法、粒子群算法等经典算法的能量调度策略节约了2.1%~21.9%的充电成本。另外,EGAHR算法可以为多种不同的EV充电模型和差异化电价趋势模型提供参考,为PSCS合理配备储能系统和光伏提供科学经济的部署方案。
The large-scale deployment of Photovoltaic Storage Charging Station(PSCS)is a crucial factor for the rapid popularization of Electric Vehicles(EV).Effectively planning the operation of PSCS,dispatching multiple energy sources,optimizing the demand-supply chain,and maximizing operational efficiency are keys to the sustainable growth and development of PSCS.To address the uncertainty of the demand side and coordination problem of the supply side of the current optical storage charging station,this paper focusses on EV charging reservation scenario.For demand,factors,such as the car charging demand and remaining parking time,are considered to decide the charging mode of the corresponding EV.On the supply scheduling side,a genetic hybrid recursive algorithm,Hybrid Recursive(EGAHR),is designed based on the band elite strategy for energy optimization scheduling to minimize the grid withdrawal cost.Using EV charging time slots as the primary regulatory unit,the strategy coordinates both demand and supply information,ensuring energy from Photo Voltaics(PV),storage,and the grid is dispatched efficiently.This satisfies the EV charging requirements within the current time slot and optimizes overall electricity expenses.Experimental results reveal that the EGAHR strategy offers a charging cost reduction in the range of 2.1-21.9%when compared to strategies based on traditional algorithms such as the genetic algorithm,gray wolf algorithm,and particle swarm algorithm.Additionally,the EGAHR strategy can be applied to various EV charging models and differential tariff trends,offering a scientifically and economically sound blueprint for PSCS to adequately equip Energy Storage System(ESS)and PV.
作者
赵文薇
林兵
卢宇
王明芬
ZHAO Wenwei;LIN Bing;LU Yu;WANG Mingfen(College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China;College of Information Science and Technology,Peking University,Beijing 100871,China;Concord University College,Fujian Normal University,Fuzhou 350117,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2023年第12期262-273,281,共13页
Computer Engineering
基金
国家重点研发计划(2018YFB1004800)
国家自然科学基金(62072108,61672159)
福建省高校产学合作项目(2021H6026,2022H6024)
福建省社科规划课题(FJ 2020C046)。
关键词
电动汽车
光储充电站
充电规划
能量调度
需求供给链
目标优化
Electric Vehicle(EV)
Photovoltaic Storage Charging Station(PSCS)
charging planning
energy scheduling
demand supply chain
objective optimization