基于动态服务价的充电站有序充电控制方法

由于电动汽车的不断增长与不均衡发展,社会中的大多充电站存在着部分充电设施闲置与部分充电排队并存的现象。为了解决电动汽车充电困难与充电设施利用率低这一对新矛盾,本文首先调查分析了各种电动车辆的出行习惯和充电特点,然后对社会充电站归纳成城市公共充电站和公交物流车等专用充电站两类,提出了分时电价响应和动态服务价响应两种有序充电方式,并制定以风电、光伏等绿色新能源出力反向激励的动态服务价引导机制,建立了以用户充电费用最低为目标的数学模型。最后通过仿真分析,分时电价响应、动态服务价响应有序充电方式下均比无序充电方式降低负荷波动性,动态服务价响应有序充电方式能够有效提高西部地区绿色电能利用率,大幅减少弃风弃光,为环境保护、能源结构调整起到重要作用。     

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。     

Electric vehicles charging control in a smart grid: A model predictive control approach

The paper presents an event driven model predictive control (MPC) framework for managing charging operations of electric vehicles (EV) in a smart grid. The objective is to minimize the cost of energy consumption, while respecting EV drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and both market and grid constraints (by seeking the tracking of a reference load profile defined by the grid operator). The proposed control approach allows “flexible” EV users to participate in demand side management (DSM) programs, which will play a crucial role in improving stability and efficiency of future smart grids. Further, the natural MPC formulation of the problem can be recast into a mixed integer linear programming problem, suitable for implementation on a calculator. Simulation results are provided and discussed in detail.     

基于用户数据的电动汽车充电桩网络攻击模型

电动汽车(electrical vehicle, EV)的用户数据是优化EV充电成本的关键数据,对用户数据的操纵可能导致错误的充电成本,使充电桩运营商蒙受经济损失。针对EV充电桩,提出了一种基于用户数据的网络攻击模型,该模型通过篡改接入EV充电桩的用户数据生成虚假的充电计划,从而提高充电桩的充电成本。所提出的攻击模型为一种基于混合整数线性规划的两层优化模型,在上层生成注入EV充电桩的恶意用户数据,下层为EV充电计划优化算法,通过应用KKT条件使两层优化模型转化为单层优化攻击模型。仿真以一个虚拟充电站场景为例,相较于无攻击情况,该攻击模型通过提高EV的充电能量或移动EV的充电时段增加了充电站的总充电成本,验证了该攻击模型的可行性和危害性。     

Electric vehicle smart charging and vehicle-to-grid operation

Electric vehicle (EV) charging must be optimised for grid load while guaranteeing that drivers' schedules and range requirements are met. A system encompassing EV owner input via a mobile application, an aggregation middleware, a charge scheduling and vehicle-to-grid (V2G) operation algorithm and a radio-frequency identification reader is proposed. The algorithm's parameters and effectiveness are presented and discussed using simulation results. Simulation results show the algorithm to effectively optimise charging and V2G operation for a given electricity price curve. The proposed system is shown to alleviate grid load during peak hours, take advantage of off-peak charging benefits and generate revenue for the parking garage operator.     

Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

As the number of electric vehicles (EVs) grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a decentralised online EV charging scheduling algorithm for large populations of EVs, where the EVs can be highly heterogeneous and may join the charging system dynamically. The algorithm couples a clustering-based strategy that dynamically classifies heterogeneous EVs into multiple groups and a sliding-window iterative approach that schedules the charging demand for the EVs in each group in real time. Extensive simulation results demonstrate that our approach provides near-optimal solutions at significantly reduced complexity and communication overhead. It flattens the aggregated load on the power grid and reduces the costs of both the users and the utility.     

基于负荷平衡的电动汽车模糊多目标充电调度算法

居民小区电动汽车（EV）的单相充电方式导致配电网出现三相不平衡和负荷峰谷差问题,因此提出基于负荷平衡的EV模糊多目标充电调度策略。基于三相网络,将总延迟时间和充电平衡作为目标函数,考虑三相不平衡度和负荷峰谷差等约束,建立静态和在线调度问题下EV充电调度模型。采用改进非支配排序遗传算法-Ⅱ（NSGA-Ⅱ）进行多目标求解,通过设计交叉算子、自适应调整变异概率和局部优化等来优化结果。通过设置一定容量的外部档案和拥挤距离判定来获得Pareto最优前沿,并用模糊隶属度方法得到折中最优解。最后,通过算例分析可同时活动充电点和三相不平衡度的不同取值对优化结果的影响,并与无序充电进行比较,验证了所提模型和策略的有效性。     

A self-interested distributed economic model predictive control approach to battery energy storage networks

In this work, a dissipativity based distributed economic model predictive control (DEMPC) approach is developed for the operation of battery energy storage (BES) networks in residential microgrids. With the presence of a microgrid power market (MPM), control of the BES systems is formulated as a self-interested distributed control problem, as individual DEMPC controllers minimize their local economic cost functions based on the price prediction of MPM. Due to the intermittent nature of photovoltaic (PV) power generations and load demands, the DEMPC without proper coordination or constraints may lead to excessive energy trading and price oscillations in MPM. To solve this problem, dissipativity theory with dynamic supply rates is adopted in this paper to deal with the interactions between individual users and the MPM. The microgrid-wide performance requirement of attenuation of the net power fluctuations with respect to time-varying PV generation and demands, is converted into the dissipative trajectory constraints imposed on individual DEMPC controllers. The proposed approach is scalable as it does not require online iterative optimizations across the controller network. A case study is presented to illustrate the proposed method.     

基于区块链的共享充电桩安全监管方案

共享充电桩可缓解当前电车充电难的现状。然而,基于第三方平台的共享充电桩平台面临着信任问题;而基于区块链的共享充电桩平台虽可提供信任环境,但缺乏对用户、桩主、充电量等信息核查。为解决以上问题,提出了一种基于区块链的共享充电桩安全监管方案。该方案通过基于双链的共享充电信任模型,安全地存储用户、桩主或运营商的关键信息,并在该模型上设计穿透式监管方案,向上核查用户、桩主或运营商的身份,向下核查充电量、充电速度等信息正确性。实验评估表明,该方案能够以不大的开销提高平台的安全性。     

面向电动汽车的分布式储能主动汇聚与协同优化方法研究

分布式储能在支撑原有配置需求的基础上具有一定的闲置容量和闲置时间, 若能汇聚这部分闲置储能形成规模化的储能集群, 将能为电网提供多类型辅助服务. 以电动汽车(EV)这一特殊分布式储能为例, 研究考虑个体有限理性特征下的微电网运营商(MGO)对主动电动汽车的汇聚方案: 首先, 建立含主动EV的系统架构, 设计了微电网营运商与主动EV之间的互动流程; 然后, 基于EV的实际出行需求, 利用灵活充放电范围理论建立统一化的EV约束模型; 考虑现实EV用户的有限理性特征, 采用前景理论对其进行有限理性建模; 最后, 将MGO和主动EV之间的互动过程建立为斯塔克伯格博弈(Stackelberg game)模型并验证了博弈均衡解的存在性和唯一性. 算例基于一个商/居型并网微网, 验证了所提模型能有效汇聚分布式EV储能, 促进可再生能源发电就地消纳     

三相负荷平衡的光伏逆变器与充电桩协调优化控制策略

平衡的三相四线制或五线制低压配电网能夠显著增加对分布式电源、电动汽车的接纳能力。大量光伏发电、电动汽车接入低压配电网可能导致网损大幅增加,三相不平衡加剧,配变过载,电压越限,威胁配电网的安全、经济运行。针对已有的负荷平衡方法代价高、适应性低的不足,从将重载相负荷转移至轻载相的基本原理出发,提出了在配电网中配置多副公共直流母线,设计了两种将位置相近、从不同相接入的光伏逆变器、充电桩直流侧连接至此共同直流母线的具有不同结构与造价的方案。构建了光伏逆变器、充电桩协调优化控制的线性约束混合整数二次规划的模型。模型的目标函数能够兼顾降损与平衡负荷。采用实际71节点低压配电系统仿真计算,与无序充电及经典方法进行了对比,验证了所提方法计算速度快,能夠满足在线运行的要求,具有完全平衡三相负荷,降低网损,削峰填谷,改善供电电压质量等优越性能。#$NL关键词: 光伏发电;电动汽车;配电网;协调优化控制;三相不平衡     

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。     

基于实时电价响应的电动出租车充电负荷优化策略

与电动私家车相比,电动出租车充电负荷高得多且难以预测。因此电动出租车不可控的充电负荷可能会给配电系统带来巨大且无法预测的峰值,并造成严重损害。为解决该问题,本文提出了一种基于实时电价响应的电动出租车充电负荷优化策略,该策略可以有效调节充电负荷用来跟踪给定的负荷曲线。策略设计包括三个步骤,首先根据应用场景,简化电动出租车最佳充电决策。然后,提出了两种充电负荷聚合模型,分别描述了充电决策和实时电价之间的关系,以及充电决策和总电量之间的关系。最后,提出了一种有效的在线估计方法来计算适当的实时电价,以使充电负荷跟踪目标负荷,作为对价格的响应。仿真结果验证了所提策略的有效性。     

基于单亲遗传算法混合动态规划的电动汽车充电调度优化策略

充电调度是电动汽车运营的一个重要内容,合理有效的充电策略在帮助运营商降低成本的同时还能减轻电网高峰时段的供电负担。从充电站运营商的角度出发,在实时电价和每个充电任务时间必须连续的假设下,建立了一个电动汽车充电成本最小模型,给出一个单亲遗传算法混合动态规划的两阶段常规充电调度算法。与电桩一旦闲置即刻分配车辆进行充电的策略以及传统单亲遗传算法相比较,该充电调度策略在电桩负载均衡的情况下有效降低了电费成本,说明了算法的有效性。此外,实验结果表现出了充电任务在多数相同时段聚集从而避开高电价时段的特征,说明充电策略对减轻高峰时段的电网压力也有一定帮助。     

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.     

考虑产能需求方交期和价格敏感的产能分享平台定价策略

基于双边市场理论,考虑产能分享双边市场具有网络外部性特性,同时考虑产能需求方对加工交期和价格敏感,研究垄断型制造业产能分享平台的定价策略.首先,建立平台和双边用户的两阶段决策模型;然后,通过计算求解探索了注册费和交易费收费模式下的平台均衡利润,并分析了交叉网络外部性等外生变量对各方决策和平台利润的影响.研究发现:注册费模式下的平台均衡利润大于固定交易费模式下的平台均衡利润;两种定价模式下的平台利润与双边用户的网络外部性均正相关,与产能需求方的交期预期偏差均负相关,与产能需求方的产能价格预期均正相关;产能需求方的交期敏感度在实际交期早于或晚于预期交期时对平台利润有不同影响,产能需求方的产能价格敏感度在产能实际价格低于或高于预期价格时对平台利润也有不同影响.     

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.     

考虑灵活充电策略的带时间窗物流配送路径优化研究

电动汽车作为一种新型绿色交通运输工具,目前被广泛的应用于多种物流场景中.然而,电池容量有限、充电时间长以及配套设施不健全等问题制约着其在物流配送领域中的有效推广.为此,针对电动汽车的物流配送路径优化问题,引入一种部分充电策略,提出了考虑部分充电策略的带时间窗电动汽车物流配送路径优化问题,建立了该问题的整数规划模型,并设计混合模拟退火算法对其求解.最后,利用一个研究算例对模型和算法进行了测试和数值分析,验证了其有效性.     

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.     

大范围WRSNs的数据路由和无线充电算法

针对传统电池供电系统能量有限问题,提出一种大范围无线可充电传感器网络(WRSNs)的数据路由和无线充电算法.以无线感知识别平台为基础,利用等边三角形的强覆盖性,并综合考虑网络路由协议和无线充电器特性对节点能量的影响,提出一种基于六边形路径的动态无线充电算法(IJRC_HP).算法包括两部分:依据充电器特性设计数据路由方法,使得能量接收功率高的节点承担更多的通信任务;依据该路由的特点设计充电方案,为能耗速度快的节点分配更多的充电时间.与等边三角形算法(TRIANGLE)、GRID算法进行仿真对比,实验结果表明,IJRC_HP算法在网络寿命提升、能量均衡、充电器移动效率和节点平均充电延时等方面具有优越性.