Electric Vehicle Charging Capacity of Distribution Network Considering Conventional Load Composition

At present,the large-scale access to electric vehicles(EVs)is exerting considerable pressure on the distribution network.Hence,it is particularly important to analyze the capacity of the distribution network to accommodate EVs.To this end,we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load.First,the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed.Second,the charging behavior of an EVis simulated by combining the Monte Carlo method and the trip chain theory.After obtaining the temporal and spatial distribution of the EV charging load,themethod of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network.By adjusting the EV ownership,the EV capacity in the distribution network is analyzed and solved on the basis of the following indices:node voltage,branch current,and transformer capacity.Finally,by considering the 10-kV distribution network in some areas of an actual city as an example,we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.

A Novel Ultra Short-Term Load Forecasting Method for Regional Electric Vehicle Charging Load Using Charging Pile Usage Degree

Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduling plan of regional charging load,which can be derived to realize the optimal vehicle to grid benefit.In this paper,a regional-level EV ultra STLF method is proposed and discussed.The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles,and then constructed by our collected EV charging transactiondata in thefield.Secondly,these usagedegrees are combinedwithhistorical charging loadvalues toform the inputmatrix for the deep learning based load predictionmodel.Finally,long short-termmemory(LSTM)neural network is used to construct EV charging load forecastingmodel,which is trained by the formed inputmatrix.The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditionalmethods.In addition,load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us,and these fluctuation factors are used to assess the prediction accuracy of the EV charging load,together with the mean absolute percentage error(MAPE).

Impact of Electric Vehicle Charging on Power Load Based on TOU Price

Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To build an orderly charging model by researching TOU price and user price reaction model. This article research the impact of electric vehicle charging on grid load by orderly charging model. With this model the grid’s peak and valley characteristics, the utilization of charging equipment, the economics of grid operation can all be improved.

Management of Charging Load of Electric Vehicles for Optimal Capacity Utilisation of Distribution Transformers

A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity utilisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.

Battery Charger for Electric Vehicles based on a Wireless Power Transmission

In this paper,the case of a battery charger for electric vehicles based on a wireless power transmission is addressed.The specificity of every stage of the overall system is presented.Based on calculated and measured results,relevant capacitive compensations of the transformer and models are suggested and discussed in order to best match the operating mode and aiming at simplifying as much as possible the control and the electronics of the charger.

Electric Vehicle Charging Situation Awareness for Ultra-Short-Term Load Forecast of Charging Stations

Electric vehicles(EVs)are expected to be key nodes connecting transportation-electricity-communication networks.Advanced automotive electronics technologies enhance EVs’perception,computing,and communication capacity,which in turn can boost the operational efficiency of intelligent transportation systems(ITSs).EVs couple the ITS to the power system,providing a promising solution to charging congestion and transformer overload via navigation and forecasting approaches.This study proposes a privacy-preserving EV charging situation awareness framework and method to forecast the ultra-short-term load of charging stations.The proposed method only relies on public information from commercial service providers.In the case study,data are powered by the Baidu LBS cloud and EV-SGCC platform,and the experiment is conducted within an area of Pudong New District in Shanghai.Based on the results,the charging load of charging stations can be adequately forecasted more than 1 min ahead with low communication and computing power requirements.This research provides the basis for further studies on operation optimization and electricity market transaction of charging stations.

Impact of EV load uncertainty on optimal planning for electric vehicle charging station

With the development of electric vehicles(EV), there is a huge demand for electric vehicle charging stations(EVCS). The utilization of renewable energy sources(RES) in EVCS can not only decrease the energy fluctuation by participating in peakload reduction of the grid, but also reduce the pollution to the environment by cutting down the use of fossil fuels. In this paper,the optimal planning for grid-connected EVCS with RES is studied by considering EV load uncertainty. Nine scenarios are set based on a different characteristic of EV load to reveal the impact of EV load on net present cost(NPC) and to express the relationship between the optimal capacity and energy flow. Moreover, since electricity price also plays an important role in EVCS planning, an economic comparison between different cases with different electricity prices for peak-valley-flat period is carried out. The results reveal the economic benefits of applying RES in EVCS, and demonstrate that EV load with different characteristics would influence the capacity of each device(PV, battery, converter) in the EVCS optimal planning.

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

The usage of each private electric vehicle(PrEV)is a repeating behavior process composed by driving,parking,discharging and charging,in which PrEV shows obvious procedural characteristics.To analyze the procedural characteristics,this paper proposes a procedural simulation method.The method aggregates the behavior process regularity of the PrEV cluster to model the cluster’s charging load.Firstly,the basic behavior process of each PrEV is constructed by referring the statistical datasets of the traditional private non-electric vehicles.Secondly,all the basic processes are set as a simulation starting point,and they are dynamically reconstructed by several constraints.The simulation continues until the steady state of charge(SOC)distribution and behavior regularity of the PrEV cluster are obtained.Lastly,based on the obtained SOC and behavior regularity information,the PrEV cluster’s behavior processes are simulated again to make the aggregating charging load model available.Examples for several scenarios show that the proposed method can improve the reliability of modeling by grasping the PrEV cluster’s procedural characteristics.

Identification of charging behavior characteristic for large-scale heterogeneous electric vehicle fleet

This paper aims to accurately identify parameters of the natural charging behavior characteristic(NCBC)for plug-in electric vehicles(PEVs) without measuring any data regarding charging request information of PEVs. To this end, a data-mining method is first proposed to extract the data of natural aggregated charging load(ACL) from the big data of aggregated residential load. Then, a theoretical model of ACL is derived based on the linear convolution theory. The NCBC-parameters are identified by using the mined ACL data and theoretical ACL model via the derived identification model. The proposed methodology is cost-effective and will not expose the privacy of PEVs as it does not need to install sub-metering systems to gather charging request information of each PEV. It is promising in designing unidirectional smart charging schemes which are attractive to power utilities. Case studies verify the feasibility and effectiveness of the proposed methodology.

Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling

Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appear at the same time,it may pose a risk to the reliable operation of power grids.For a system integrated with renewable energies,this risk can be much higher because of their unsteady power output.With load measurements more widely collected,this paper presents a data-driven framework to assess the reliability of a power grid considering charging EVs.Specifically,the diffusion estimator is firstly applied to estimate the probability density function of EV charging loads,which possesses both regional adaptivity and good boundary estimation performance.Then,charging load samples are produced through slice sampling.It is capable of sampling from irregularly-shaped distributions with high accuracy.The proposed approach is verified by the numerical results from the simulations on a modified IEEE 30-bus test system based on real measurement data.

考虑奖励机制的电动汽车充电优化引导策略

随着电动汽车(electric vehicle,EV)的大规模推广,其无序充电严重威胁电网的安全稳定运行,积极引导EV用户参与充电优化策略,对于提高电网的安全稳定性具有重要意义。为此,基于充电优化管理调度思路,提出一种考虑奖励机制的EV充电优化引导策略,在分时电价的基础上,计入用户在降低电网负荷波动中的奖励机制,考虑充电位置固定、不确定用户的出行需求,确定EV的充电时间及充电位置,达到用户满意度最高的目的;利用EV动态响应的实时优化算法,对所提的优化调度模型进行求解。仿真结果验证了所提策略的有效性和可行性,该优化调度策略不仅能有效改善负荷低谷时段集中充电形成新的负荷高峰的问题,而且可明显降低用户的充电成本及电网负荷波动。

考虑充放储一体站与电动汽车互动的主从博弈优化调度策略

针对大规模电动汽车(electrical vehicle,EV)接入微电网造成的负荷压力,提出一种考虑充放储一体站(charging-discharging-storage integrated station,CDSIS)与EV互动的主从博弈优化调度策略。首先,通过建立CDSIS模型,并针对CDSIS多场景进行分段设置。其次,建立动态路网模型并结合EV出行特性,预测城市区域路网约束下的EV充电负荷时空分布。并根据预测结果建立EV及CDSIS多目标主从博弈优化调度模型,对EV用户、CDSIS收益进行多目标协调。最后,以某城市主城区域部分交通路网结合IEEE33节点配电系统进行仿真,分析电价与CDSIS储能设备容量对城市区域内EV用户和CDSIS站收益的影响。结果表明,所提主从博弈模型与调度策略能够使得EV用户与CDSIS双方得到最大收益。

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。

考虑分布式电源时空相关及电动汽车充电负荷分布特性的有源配电网概率潮流

目前分布式电源(distribution generation,DG)和电动汽车(electric vehicle,EV)充电负荷等随机变量接入电力系统时时空特性考虑不足,会导致概率潮流计算偏离实际。因此该文提出了考虑DG时空相关及EV分布特性的有源配电网概率潮流模型。首先,利用场景生成建立时空相关的风电、光伏出力概率模型;然后,考虑路网约束和用户心理,建立考虑EV时空分布特性的负荷概率模型;最后,以Nataf变换结合奇异值分解处理相关性输入变量,运用三点估计法对概率潮流进行计算,通过Cornish-Fisher级数拟合得到累积分布函数。以IEEE-33节点系统进行测试,利用所提模型分析了DG时空相关性和EV分布特性对配电网潮流的影响。结果表明DG时空相关性主要影响系统的波动性,EV分布特性主要影响系统的运行特性。该文研究可为新型配电网的安全运行提供理论指导。

含循环换电负荷及充电站的微电网多目标优化调度

电动换电重卡由于具有充电功率高、周期性强的循环换电负荷特性,一旦短时大量接入微电网中,将带来经济性和安全性的挑战。首先讨论电动汽车充电、循环换电负荷的不同特性,建立含循环换电负荷及充电站的微电网模型;提出一种综合考虑微电网经济性及联络线功率波动性的多目标调度优化方法;采用线性加权方法将多目标问题转化为单目标问题;最后利用CPLEX工具对仿真算例进行求解,仿真算例表明,所提优化方案可有效求解综合考虑不同权重下微电网净利润、联络线交换功率及换电站内电池最优调度问题,能在尽可能减小微电网与上级电网交换功率波动性的同时,使得微电网获得最大净利润。

考虑负荷时空均衡和弹性响应的电动汽车快充电价定价策略

为了引导电动汽车有序充电,提出了一种考虑负荷时空均衡和弹性响应的电动汽车快充电价定价策略。引入交通流理论描述交通路网,建立电动汽车快充负荷时空分布模型;考虑配电网调度和电动汽车快充负荷的弹性需求,构建源-荷互动下的快充电价定价架构,并基于潮流追踪法从时空双维度推导快充电价的计算方法;在此基础上,计及电动汽车负荷弹性响应特性,以提升配电网的负荷均衡性为目标,建立快充电价定价策略,并通过迭代算法求解该定价策略。算例仿真结果表明,所提定价策略能够有效引导电动汽车进行有序快充,激励电动汽车用户与配电网友好互动,提升负荷的时空分布均衡性。

基于改进LSTM神经网络的电动汽车充电负荷预测

当前对电动汽车(EV)充电负荷预测的研究缺少真实的数据支撑,并且模型考虑场景过于简单,影响因素考虑不到位,预测结果缺乏说服力。基于此,提出一种考虑多种电动汽车充电负荷影响因素的电动汽车充电负荷预测方法。首先,考虑天气、季节、温度、工作日、节假日等因素对电动汽车充电负荷的影响,采用三标度层次分析法分析各影响因素权重;其次,建立LSTM神经网络预测模型,通过真实数据训练得到用于预测的LSTM神经网络模型,结合影响因素权重分析结果对预测模型进行修正,得到最终的改进LSTM神经网络负荷预测模型;最后,采用常州某小区的真实数据对所提预测方法进行试验验证。结果表明,所提方法可以实现电动汽车充电负荷的精确预测,且负荷预测结果可为有序充电策略研究提供参考。

计及电动汽车保有量增长需求的充电负荷预测

当前对电动汽车充电负荷的研究大多集中在短期演变,对长时间尺度下的发展情况并未有较多研究。文中提出一种电动汽车保有量增长需求的充电负荷预测模型。首先采用萤火虫算法优化电动汽车保有量灰色预测模型的相关参数,对某地区2023—2033年电动汽车保有量进行预测;其次,综合考虑保有量预测结果、用户出行链、行驶里程及充电起始时间,结合在不同温度下的电动汽车电池容量和充电效率搭建充电负荷预测模型;最后,对江苏省某地区2023—2033年电动汽车充电负荷进行仿真预测。仿真结果有效地预测了电动汽车在未来10年中保有量发展趋势以及考虑保有量增长需求的充电负荷。