Study on site selection planning of urban electric vehicle charging station

The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.

First large charging station with typical design put into operation

On March 31, in accordance with the typical design requirements of the State Grid, the f irst large electric vehicle (EV) charging station, built by the North China Grid,

城市EV时空充电负荷预测及充电站规划研究

针对目前城市电动汽车(electric vehicle,EV)充电站存在盲目建设、规划不合理导致的部分充电站利用率低、用户充电满意度低等问题,同时为适应“双碳”目标下发展大规模EV的充电站规划需求,提出一种基于蒙特卡洛模拟和回声状态网络(echo state network,ESN)拟合的城市EV时空充电负荷预测方法,进一步开展EV充电站规划研究。首先考虑城市交通路网结构和区域主要功能,将待规划区域进行网格划分并作为待建充电站备选位置;利用蒙特卡洛方法对各类EV进行多种模式的出行链模拟,获取各网格区域内的EV充电负荷数据集;为拟合各网格内EV充电负荷的多样化分布特征,建立基于回声状态网络ESN学习算法的EV时空充电负荷预测模型,实现一定EV保有量下待规划区内EV时空充电负荷的预测。进一步考虑待规划网格区域内的最大充电预测负荷等约束条件﹑以充电站的建设和运维成本、EV用户充电出行成本以及配网损耗的综合成本最小为目标,建立EV充电站的规划模型,利用粒子群算法进行模型求解得到待规划区的充电站建设位置、数量及容量;最后以某城区EV充电负荷预测及充电站规划为例进行计算,验证了所提方法及模型的有效性。

考虑风电消纳的“风-网-EV充换电站”协同优化策略研究

为降低风电功率波动性、反调峰性对电网运行的不利影响,提出一种考虑风电消纳的“风-网-EV充换电站”协同优化调度策略。首先将火电作为可调节电源辅助风电上网,并通过碳交易机制促使火电系统在电网负荷低、弃风率高的时段,主动降低出力减碳运行,等效提升风电上网空间;然后在满足电网用电需求基础上,接入充换电站负荷进一步抑制风电功率波动,同时提升风电消纳量,以电网负荷峰谷差最小以及系统综合运行成本最优为目标函数,构建联合系统低碳经济运行模型;最后通过NSGA-Ⅱ算法对不同情景求解分析。结果表明:该策略能够有效降低系统运行成本、电网负荷峰谷差,提高电网风电消纳率。

考虑负荷空间均衡和充电站合作博弈的快速充电定价机制

为了平衡快速充电负荷空间分布,提高电动汽车用户充电满意度,提出考虑负荷空间均衡和充电站合作博弈的快速充电动态定价机制。首先,通过实际订单数据逆地理编码得到出行概率转移矩阵,结合路网模型和速度-流量模型建立电动汽车充电负荷时空分布模型;然后,考虑用户主观意愿,建立计及用户偏好的充电站选择决策模型,模拟用户对于充电站选择的理性决策行为;最后,以充电站节点负荷均衡性为目标,建立区域内快速充电站合作模式下的动态定价博弈模型,通过迭代算法求解均衡值,并利用Shapley值法对合作联盟所得剩余收益进行分配。仿真实例表明,所提快速充电站动态定价策略能有效降低充电站节点的负荷方差,提高充电桩利用率和充电站收益,并减小用户排队时间,提高用户满意度。

Access Control Method for EV Charging Stations Based on State Aggregation and Q-Learning

This paper presents intelligent access control for a charging station and a framework for dynamically and adaptively managing charging requests from randomly arriving electric vehicles(EVs),to increase the revenue of the station.First,charging service requests from random EV arrivals are described as an event-driven sequential decision process,and the decision-making relies on an eventextended state that is composed of the real-time electricity price,real-time charging station state,and EV arrival event.Second,a state aggregation method is introduced to reduce the state space by first aggregating the charging station state in the form of the remaining charging time and then further aggregating it via sort coding.Besides,mathematical calculations of the code value are provided,and their uniqueness and continuous integer characteristics are proved.Then,a corresponding Q-learning method is proposed to derive an optimal or suboptimal access control policy.The results of a case study demonstrate that the proposed learning optimisation method based on the event-extended state aggregation performs better than flat Q-learning.The space complexity and time complexity are significantly reduced,which substantially improves the learning efficiency and optimisation performance.

Planning and Real-time Pricing of EV Charging Stations Considering Social Welfare and Profitability Balance

This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.

考虑低碳交通的电动汽车充电站优化配置方法

交通运输产生的碳排放是城市碳排放的主要来源之一,而城市充电站规划中较少考虑交通碳排放因素。对此,提出了一种考虑低碳交通约束的电动汽车充电站优化配置方法。首先,基于充电站候选点划分区域碳排放测算网格,在此基础上考虑充电站位置、容量等因素的影响并建立网格化交通碳排放模型;然后,建立综合考虑碳排放量、交通能源结构、交通电力能源清洁度以及充电站使用率四个因素的低碳交通指标体系,量化分析充电站规划方案对交通碳排放的影响;之后,以低碳交通指标为约束条件,兼顾经济性和节能减排,建立考虑交通碳排放因素的双目标优化配置模型;最后,通过某地区案例对所提方法进行仿真验证,结果表明,考虑低碳交通约束的优化配置方法可以在不增加投资成本的同时有效降低城市各区域交通碳排放水平。

考虑用户出行成本预算的电力-交通耦合网络充电站定价策略

随着电动汽车的规模化发展,研究如何有效考虑用户的出行行为机理并制定合理的充电站充电价格,对电力-交通网络的协同优化调度具有重大意义。针对此问题,提出了考虑用户出行成本预算的电力-交通耦合网络充电站定价策略。首先,建立考虑出行成本预算的交通用户均衡模型,将均衡状态通过变分不等式进行等效描述,从而对电动汽车出行需求和充电行为进行刻画。其次,构建考虑功率削减的配电网二阶锥优化模型,将充电站定价问题转化为含有变分不等式约束的优化问题,并根据问题设计交替迭代算法和外梯度算法进行求解。最后,通过算例对所提模型和方法的有效性进行验证,结果表明了考虑出行成本预算对耦合网络充电定价的必要性。

考虑电动汽车和5G基站的电力-信息-交通耦合网络需求响应策略

电动汽车(EV)是具有移动负荷和通信用户双重属性的主体,为充分挖掘其参与需求响应产生的可调度潜力并降低电网负荷波动,提出了电力-信息-交通网络耦合背景下EV和5G基站需求响应策略。首先,分析了EV和5G基站参与下的多网络耦合关系。其次,建立了EV集群和5G基站集群的灵活性模型。基于此模型,提出了两阶段需求响应优化调度策略:第1阶段以通信成本最小为目标,为EV提供充电导航和路径规划并优化基站用能模式;第2阶段以配电网负荷波动最小为目标,制定EV的充放电策略。最后,通过某城市交通模型的测试,分析了调度策略对基站运行、配电网负荷、潮流和用户的影响,验证了模型和方法的有效性。

含新能源发电的电动汽车充电站充电功率在线优化策略研究

针对含新能源发电系统的电动汽车充电站充电功率优化问题,提出了一种充电功率在线实时优化策略,依据新能源发电出力及电动汽车当前状态信息动态调整未来24 h内充电站各充电桩充电功率,该在线优化策略依据电动汽车充电负荷特点,在初始化阶段利用基于状态依赖的决策向量分类方法,降低了每次优化待优化矩阵维度;在优化过程中利用微分演化算法,分别对有效决策向量组合以及决策向量进行优化,旨在快速、准确地对充电站中各充电桩未来24 h内的充电计划进行实时优化。该优化策略对降低充电站运行成本、改善因电动车充电引起的负荷波动具有一定的参考价值。

考虑用户动态充电需求的充电站选址定容优化

针对城市EV(电动汽车)充电站规划建设中投资高、效率低的问题,提出一种考虑用户动态充电需求不确定性的充电站选址定容优化模型。首先,基于出行链理论和OD(起讫点)矩阵研究EV出行特性,结合Dijkstra算法和蒙特卡洛法建立EV的充电负荷时空分布预测模型。其次,建立以充电站经营者年化成本、EV用户年化经济损失之和最小为目标的充电站选址定容模型,采用加权Voronoi图与自适应模拟退火粒子群优化算法求解,确定充电站的最优数量、位置及服务范围。最后,在定容模型中引入不确定情境集描述用户动态充电需求的不确定性,并采用鲁棒优化理论求解充电站容量。针对北方某市部分城区的EV充电站规划问题开展算例分析,验证了模型的有效性。

Remote Monitoring of Electric Vehicle Charging Stations in Smart Campus Parking Lot

Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles(EVs).In order to manage EV charging,the parking lot local controller(PLLC)requires data exchange with EV charging stations(EVCSs)through communication infrastructures.However,data losses and communication delays are unavoidable and may significantly degrade the system performance.This work aims to investigate the underlying communication networks for remote monitoring of EVCSs in a smart campus parking lot.The communication network consists of two subnetworks:parking area network(PAN)and campus area network(CAN).PAN covers communication among EVs,charging stations and PLLC,while CAN enables dedicated communication between PLLCs and a global controller of the university.As one of the major obstacles in EV system is the lack of unified communication architecture to integrate EVCS in the power grid,we develop communication models for the in-vehicle system and EVCSs based on logical node concept of IEC 61850 standard.Furthermore,we implement network models for EVCSs using OPNET modeler.Different communication technologies and configurations are considered in modeling and simulations,and end-toend delay is evaluated and discussed.

Multi-objective simultaneous optimal planning of electric vehiclefast charging stations and DGs in distribution system

The large-scale construction of fast charging stations(FCSs)for electric vehicles(EVs)is helpful inpromoting the EV.It creates a significant challenge for the distribution system operator to determine the optimal planning,especially the siting and sizing of FCSs in the electrical distribution system.Inappropriate planning of fast EV charging stations(EVCSs)cause a negative impact on the distribution system.This paper presented a multiobjective optimization problem to obtain the simultaneous placement and sizing of FCSs and distributed generations(DGs)with the constraints such as the number of EVs in all zones and possible number of FCSs based on the road and electrical network in the proposed system.The problem is formulated as a mixed integer non-linear problem(MINLP)to optimize the loss of EV user,network power loss(NPL),FCS development cost and improve the voltage profile of the electrical distribution system.Non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ)is used for solving the MINLP.The performance of the proposed technique is evaluated by the 118-bus electrical distribution system.

含EV充电站的园区微电网孤岛运行可靠性评估

以园区微电网为研究对象,将充电站内电动汽车分为私家电动车和电动班车2种主要类型,进行孤岛运行可靠性评估。首先,通过分析不同类型电动汽车的行驶时间特性,提出电动汽车充电站联合储能设备与微电网电力系统间的功率互动策略以及互动功率计算模型。随后,以开关为边界对微电网馈线区域进行分级,提出不同故障隔离操作方法。并在传统评估指标的基础上,提出电动汽车充电站平均充电、放电、负荷削减深度等评估指标。最后,采用序贯蒙特卡罗智能算法,以IEEE配电系统可靠性评估测试系统RTBS Bus6的F4馈线区域为基础而修改的配电网络为算例,进行仿真计算。计算结果表明:当响应V2G技术的电动汽车充电站接入微电网后,合理的功率互动策略可有效提高微电网孤岛运行状态下的可靠性。

考虑源荷相关性的含EV充电站的DG优化配置研究

在清洁能源大规模发展的背景下,分布式电源(Distributed Generation,DG)和电动汽车(Elec⁃tric Vehicle,EV)接入配电网,会对其规划和运行带来诸多影响,为保证系统高效、经济和安全运行,文中建立了DG与EV充电站协调规划模型。利用拉丁超立方采样(LHS)以及矩阵变换法,对DG与传统负荷间存在的相关性进行描述;通过蒙特卡洛模拟法对EV充电负荷进行预测,根据K means算法进行场景聚类得到典型运行场景;构建以综合年费用、供电可靠性成本以及电压偏移量最小为目标的规划模型,采用改进的粒子群算法(Chaos Particle Swarm Optimization,CPSO)进行求解,结合IEEE-33节点系统进行仿真验证。结果表明,所建模型下的综合协调规划可以提高系统的经济性与可靠性,并达到改善电压质量的目的,也验证了CPSO算法的可行性与快速有效性。

考虑用户便捷性和配网接纳能力的EV充电站选址定容

充分考虑规模化电动汽车(EV)使用过程中受限于交通便利性、接入过程中不宜超过配电网接纳能力的问题,提出一种区域范围内EV充电站选址定容优化规划方法。首先,应用蒙特卡罗方法分析有序、无序充电2种不同场景下EV接入配电网的负荷需求特点;其次,构建考虑交通便利性和配电网接纳能力的EV充电站选址定容优化模型,并应用粒子群和Voronoi相结合的方法对其进行求解,先后实现区域范围内EV充电站选址确定、充电站内充电桩个数优化;最后,通过典型的北方城区实际案例,验证所提优化规划模型的合理性,以期为我国EV充电站的规划建设提供借鉴。

多场景下EV充电站与DG多目标规划

一直依赖化石能源不是长久之计,可再生分布式电源(Distributed Generation,DG)及电动汽车(Electric Vehicle,EV)作为两大新能源技术,受到各国的重视。但是大规模的EV与DG接入配电网会造成许多影响,在进行配电网规划时需要加以考虑,文中提出了一种多场景下EV充电站与DG的联合规划模型,利用K-means算法对一年内的DG出力与常规负荷数据进行聚类,然后利用蒙特卡洛方法预测电动汽车的充电负荷,最后构建以网损最小、负荷波动最小为目标函数的多目标规划模型,以改进的人工蜂群-粒子群算法进行求解,结合IEEE-33节点配电网进行仿真分析,验证了模型的有效性与可行性。

基于改进粒子群算法的EV充电站站址与检测工作规划

从流量需求和点需求两个方面介绍了充电需求的分布情况;又从EV运营商、EV用户和检验检测机构等利益主体的效益出发,构建了社会总成本目标函数模型;并提出了一种粒子群算法与Voronoi图相结合的方法来求解上述目标函数模型;最后利用算法进行仿真分析,得到充电站的站址和检测服务范围,验证了所述方法的可行性,为充电站站址的选择和检测服务提供了参考。

时空需求下的电动汽车充电设施选址优化模型

针对电动汽车充电站布局位置不合理、充电利用率较低等问题,提出了一种时空需求下的充电设施选址优化模型STDM。通过对电动汽车出行数据的时空分布特征进行挖掘,结合电动汽车的出行与充电行为构建充电需求预测模型来获得区域内时空需求分布;采用基于时空统计量的方法获取需求热点区域,考虑到充电站服务覆盖问题,定义充电覆盖率作为模型评估参数;在此基础上从用户角度与运营和社会角度综合构建电动汽车到站距离成本、充电站建设运行成本和碳排放成本的优化模型。最后通过实际数据验证模型的可行性和有效性。结果表明,模型得出了区域内12个充电站的选址方案降低综合成本,同时确定充电站的布局位置与充电桩数量;此外,采用所提方法得到的模型选址结果相比于其他文献方法能够有效缩短电动汽车到站距离,并提高到站覆盖率。