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。     

基于混合整数线性规划的充电桩群优化调度

针对充电站内电动汽车充电成本过高的问题，提出了一种基于混合整数线性规划（MLP）的充电桩群优化调度的方法。在满足电I动汽车充电需求的前提下该调度方法以充电站内总充电成本最低为优化目标，并采用模型预测控制器（MPC）模拟电动汽车行程，利用MLP求解器求解充电站各个时间段I内的充电功率，并按照充电时间顺序将每个时段内充电功率分配给对应时段内在充电站内停车场充电的电动汽车。实验结果表明，基于MLP的充放电调度方法有效降低了充电站内的总充电成本，达到了优化电动汽车充放电调度的目的。     

基于二阶圆锥规划的大规模电动汽车充电调度优化研究

提出一种基于二阶圆锥规划的大规模电动汽车充电调度优化策略。该策略能够在电力成本、需量充电、电池退化成本和负荷波动之间找到平衡点,结合约束函数,对电动汽车的充电调度进行优化,最终给出优化结果。使用ACNData数据集进行验证,实验结果表明,该充电调度优化策略,能大幅度地缓解电网在充电高峰期的压力,降低充电站基础设施的成本和电动汽车用户充电的成本,以及缓解电池的退化问题。     

基于DEB-ABC算法的电动汽车充电优化调度模型

随着电动汽车保有量不断上升, 其相关配套设施也面临巨大挑战, 不合理的充电资源分配在充电高峰期会造成部分充电站过度拥挤, 并且影响电网稳定运行. 提出一种考虑多目标优化的调度模型, 通过分析充电站内不同充电选项的排队时间, 并根据排队率和分时电价提出一种动态定价模型, 影响车主充电行为, 结合动态定价模型与充电需求计算充电成本, 考虑基于起讫点的充电总路径行驶时间, 以总成本最少为优化目标, 基于DEB-ABC算法进行求解. 在某区域内对1 500辆电动汽车进行仿真验证, 结果表明提出的优化调度模型可减少充电等待时间、充电成本和总行驶时间, 提高区域内充电站利用率.     

基于收益分析的充电站选址规划模型

为了解决电动汽车充电站建站选址过程中出现的重复投入、资源浪费等问题,使其一方面可以满足电动汽车用户的充电需求,另一方面还可以提高自身经济收益,本文依据某公司车桩一体平台所采集的新能源车运行数据和充电桩充电数据,结合公司的实际投资建设需求,在充分考虑了充电站的经济收入和各类投资费用后,以最小扭亏为盈年限作为目标函数,建立了充电站选址规划模型。为了验证模型的正确性,我们在初步给定的15个预选站点中,采用差分进化算法进行模型求解,得出各站点最小扭亏为盈年限以及所需配备的充电桩的个数。根据模型求解结果,发现站点6是以收回投资成本的最短年限为依据的最佳站点,而以年均收入为依据的最佳站点是站点1,这是与实际工作经验想吻合的。     

考虑充电桩最优效率的电动汽车有序充电模型

在电动汽车有序充电过程中,充电效率的评估和优化有助于提升调度模型的精确度。提出一种考虑直流充电桩最优充电效率的电动汽车有序充电模型。首先,分别对直流充电桩中Vienna整流器部分与双有源桥DC-DC部分进行损耗建模,并使用Pareto优化方法求取最优充电效率。然后,以电网运行、电池电量、充电功率和用户行为作为约束条件,构建最优负荷波动率目标函数,并通过粒子群优化方法进行求解。最后以南京城区某大型充电站的夏季负荷历史数据为参考,利用MATLAB软件分析有序充电模型的执行效果,负荷波动率从0.912降低到0.833。实验结果表明,考虑直流充电桩最优充电效率的有序充电模型能较好地降低负荷波动率。     

考虑差异化需求的电动汽车充电调度策略

电动汽车行驶里程短、充电时间长是影响驾驶体验的关键.通过对电网、充电设备进行大规模升级的方法减少充电时间,成本昂贵,因此充分利用现有路网、电网资源,制定智能充电调度策略成为提高驾驶体验的重要手段.考虑到驾驶者对充电时间敏感度的异质性,提出具有差异化的调度策略以满足不同优先级驾驶者的需求.首先,为均衡不同优先级驾驶者的利益,提出一种基于动态截断机制的两优先级队列模型;其次,定义充电站的准入原则,保证高优先级驾驶者对预留桩的使用权及对空闲桩的优先抢占权;然后,提出基于截断机制的双层优化模型CCPQ(charging with cut-off priority queue),在顶层高优先级车辆与充电桩最优匹配的基础上,设计底层低优先级车辆的分配策略优化模型,将最小化低优先级驾驶者的总等待时间构建为凸优化问题;最后,通过仿真验证策略的有效性及优越性.     

资金约束下单向共享电动汽车系统的充电站选址

针对单向共享电动汽车系统提出了一种充电站位置优化的方法,充电站的容量和服务范围内的需求量相适应;该方法基于混合整数规划模型,其目标考虑了满足服务的收入,车辆折旧成本和充电站的充电桩运行成本,目标函数为最大化共享电动汽车服务商的利润.最后进行了模拟仿真来测试方法的松弛度和性能,模型能够在合理的时间内解决较大规模的问题.     

面向用户行驶计划的电动汽车充电调度策略

充电站（桩）尚未普及以及电动汽车有限的行驶里程,使得大多数汽车用户关于是否选择电动汽车犹豫不决。为了减少用户对于电动汽车有限电池容量的担心,并且降低因频繁充电以及偏离原定行驶路线绕路进行充电所增加的电动汽车使用费用,提出一种基于匹配理论面向用户行驶计划的电动汽车充电调度方案TPCS。首先,分别根据电动汽车用户的行驶计划和对各充电站的电量需求构建电动汽车用户与充电站的偏好表;然后,建立电动汽车用户与充电站之间的多对一匹配模型;最后,以系统总效用为优化目标进行充电站接口分配。数值仿真结果显示,与随机分配（RCS）算法和仅考虑电动汽车效用分配（OEVS）算法相比,TPCS算法将系统总效用较RCS算法最多提高了39.3%,较OEVS算法最多提高了5%;而在电动汽车充电需求轻负载时,TPCS算法始终保证用户满意度在90%以上,高于RCS算法。所提算法能够有效地提高系统总效用和用户满意度,同时降低计算复杂度。     

基于区块链的充电交易数据安全存储平台设计

电动汽车充电平台作为智能电网的重要组成部分,越来越多的用户使用充电平台内的充电桩进行充电,充电平台将会存储大量的用户交易信息。在现有的充电桩共享平台中,用户的充电交易数据被保存在中心化的数据库中,但这种过度中心化的存储方式极易遭受恶意攻击而引发单点失效以及重要交易数据被恶意篡改等信息安全问题,并因此可能造成大量用户隐私信息泄露。针对这些问题,利用区块链技术在电动汽车充电平台中选定若干充电站作为数据中心节点,设计了一个充电交易数据存储方案。用户可对交易后的个人数据进行加密控制数据使用权,各数据中心节点之间使用共识机制对加密数据进行去中心化地同步存储。安全分析表明所设计的数据存储平台能实现安全、有效的数据存储。     

A distributed charging strategy based on day ahead price model for PV-powered electric vehicle charging station

This paper studies a distributed charging model based on day-ahead optimal internal price for PV-powered Electric Vehicle (EV) Charging Station (PVCS). Considering the feed-in-tariff of PV energy, the price of utility grid and the forecast model of PV based on back-propagation neural network (BPNN), a system operation model of PVCS is introduced, which consists of the profit model of PVCS operator (PO) and the cost model of EV users. The model proposed in this paper can be designed as a Stackelberg game model, where the PO acts as the leader and all EV users participated are regarded as the followers. An optimization strategy based on heuristic algorithm and nonlinear constrained programming are adopted by the PO and each EV user, respectively. Moreover, a real-time billing strategy is proposed to deal with the errors from the forecasted PV energy and the expected charging arrangements. Finally, through a practical case, the validity of the model is verified in terms of increasing operation profit and reducing charging cost.     

Traffic Equilibrium and Charging Facility Locations for Electric Vehicles

This study investigates the electric vehicle (EV) traffic equilibrium and optimal deployment of charging locations subject to range limitation. The problem is similar to a network design problem with traffic equilibrium, which is characterized by a bi-level model structure. The upper level objective is to optimally locate charging stations such that the total generalized cost of all users is minimized, where the user’s generalized cost includes two parts, travel time and energy consumption. The total generalized cost is a measure of the total societal cost. The lower level model seeks traffic equilibrium, in which travelers minimize their individual generalized cost. All the utilized paths have identical generalized cost while satisfying the range limitation constraint. In particular, we use origin-based flows to maintain the range limitation constraint at the path level without path enumeration. To obtain the global solution, the optimality condition of the lower level model is added to the upper level problem resulting in a single level model. The nonlinear travel time function is approximated by piecewise linear functions, enabling the problem to be formulated as a mixed integer linear program. We use a modest-sized network to analyze the model and illustrate that it can determine the optimal charging station locations in a planning context while factoring the EV users’ individual path choice behaviours.     

An Intelligent Energy Management Mechanism for Electric Vehicles

Electric vehicles (EVs) have become increasingly popular all over the world in recent years. Many countries have been offering reward policies and facilitating the establishment of EV charging stations and battery exchange stations to encourage use of these vehicles by the public. However, in terms of electricity demand, the rapid establishment of EV charging stations and battery exchange stations may lead to significant increases in peak loads, the contracted capacities, and basic electricity charges. In this work, an intelligent EV energy management mechanism is proposed to make use of scheduling systems for the charging stations in order to determine when to store electricity in batteries according to the real-time electricity price and the recharging requirements of EVs. Meanwhile, a recharging suggestion module is presented in this work for locating the most suitable charging station or battery exchange station for an EV according to the available information on hand. When an EV cannot reach any charging station because it is running out of electric power, a mobile CV management module is used to assist the EV to find a suitable mobile CV for recharging. Notably, a well-known machine learning technique, multiobjective particle swarm optimization, was employed in this work to assist in solving the multiobjective optimization problems during the design of an energy management mechanism. The experimental results show that the proposed mechanism can balance the loading of battery charging and exchange stations, and lower the load peak to keep electricity cost down. Meanwhile, the recharging suggestion module can decrease the driving distance of EVs for finding the charging stations, as well as decreasing the waiting time wasted while charging. The mobile CV management module, for its part, can effectively prevent EVs from becoming stranded on the road because they have run out of electricity.     

电动汽车典型快充站优化运行配置方法

为了降低大功率快充桩对电网冲击波动, 并考虑典型快充站分布式电源和储能优势, 提出一种电动汽车典型快充站优化运行配置方法. 通过分析站内分布式电源出力特点以及电动汽车充电行为规律, 以充电站运行成本最小为优化目标, 建立典型快充站优化运行配置模型, 以站内功率平衡、分布式电源出力等为约束条件, 利用遗传优化算法求解模型最优解. 最后通过不同配置算例验证所提方法的可行性, 为典型快充站的优化运行提供技术支撑.     

A game-theory analysis of charging stations selection by EV drivers

We address the problem of Electric Vehicle (EV) drivers’ assistance through Intelligent Transportation System (ITS). Drivers of EVs that are low in battery may ask a navigation service for advice on which charging station to use and which route to take. A rational driver will follow the received advice, provided there is no better choice i.e., in game-theory terms, if such advice corresponds to a Nash-equilibrium strategy. Thus, we model the problem as a game: first we propose a congestion game, then a game with congestion-averse utilities, both admitting at least one pure-strategy Nash equilibrium. The former represents a practical scenario with a high level of realism, although at a high computational price. The latter neglects some features of the real-world scenario but it exhibits very low complexity, and is shown to provide results that, on average, differ by 16% from those obtained with the former approach. Furthermore, when drivers value the trip time most, the average per-EV performance yielded by the Nash equilibria and the one attained by solving a centralized optimization problem that minimizes the EV trip time differ by 15% at most. This is an important result, as minimizing this quantity implies reduced road traffic congestion and energy consumption, as well as higher user satisfaction.     

基于GPU并行计算的电动出租车新建充电站选址模型

针对电动出租车充电站优化选址问题，构建了以未满足的电动出租车充电需求量和新建充电站的固定成本最小为目标函数的电动出租车新建充电站选址模型，并提出基于改进的多目标粒子群算法的模型求解方法。为解决未满足充电需求量计算的性能瓶颈问题，设计了一个基于图形处理器（GPU）的未满足充电需求量并行计算算法，并通过实验验证其运行时间约为基于CPU串行算法运行时间的10%~12%。以北京为例，收集、处理相关多源数据，对提出的选址模型进行了应用示例分析，表明所提出的充电站优化选址方案具有可行性。     

EVLibSim: A tool for the simulation of electric vehicles’ charging stations using the EVLib library

Electric Vehicles (EVs) are considered an efficient alternative to internal combustion engined ones, aiming to reduce global CO₂ emissions. In the last years, EVs are entering the market in an increasing pace. In contrast to conventional cars, EVs have a more complicated recharging procedure. Thus, the development of tools for the efficient simulation of the charging of large numbers of EVs is critical. In this vein, EVLibSim is a tool for the simulation of EV activities at a charging station level. EVLibSim unifies EVLib’s primary functions such as the charging and dis-charging of batteries, battery swapping, as well as parking/inductive charging. EVLib is a Java library that provides a simple, yet efficient framework for the management of a number of Electric Vehicle (EV) activities, at a charging station level, within a Smart Grid. EVLibSim provides a great variety of configuration options such as the types and number of chargers, the available energy, the waiting queues, etc. Furthermore, through plots and overview dashboards each user can supervise the operation of the tool in real time. Both EVLib’s and EVLibSim’s efficiency and scalability have been tested in realistic scenarios, while the correctness and safety of the code have been verified using state of the art tools. Finally, the user experience of the EVLibSim has been evaluated and improved through a detailed user evaluation.     

考虑用户选择偏好的电动汽车充电站规划研究

在国家大力发展新能源汽车的过程中,充电问题一直阻碍着电动汽车的发展,充电基础设施尤其是快速充电站的规划和建设尤为重要。大规模发展电动汽车（electric vehicle,EV）的关键是根据用户的充电选择偏好,建立完善的充电基础设施,减少用户的里程焦虑,彻底解决充电不方便的问题。在考虑了各方面社会因素并确定一定数量的候选节点背景研究的基础上,提出了一种双目标规划模型,在满足需求、距离、容量等约束条件下,分析了建设充电站总成本和充电覆盖范围之间的关系,寻找最优的充电站建设方案,并以A城市B区为例,通过多目标粒子群算法进行求解,求出充电站的最佳节点和数量。用不同算法进行求解,通过对结果进行分析比较,表明多目标粒子群算法（MOPSO）在求解双目标问题时更具有实际意义。