基于改进粒子群算法的电动汽车停车场V2G策略研究

为解决电动汽车大规模并网带来的一系列问题,国内外逐步在城市商业停车场内提供电动汽车充电服务。在此背景下,提出一种基于电动汽车并网技术的电动汽车充放电停车场模型。该模型响应实时电价,对电动汽车的充电并网行为进行动态调度,继而与电网进行能量交互。在求解电动汽车最优调度策略时采用粒子群优化算法,从可行性编码、自适应搜索半径、边界变异修正等方面进行改进,以提高算法的效率及收敛精度。仿真实验采用美国PJM公司的实时电价数据及主流电动汽车的型号参数,对比分析了3种不同情景下电动汽车停车场的运营过程及结果,验证了所提模型的合理性以及改进算法的有效性。     

Interfacing Vehicle Charging Systems with User and Grid Requirements

The rising number of electrically powered vehicles (EV) challenges the distribution grid. Analyses revealed that the uncoordinated, simultaneous charging results in heavy load. This load leads to further increase of the already existing load peaks and thus to an even greater strain on the distribution grid. These effects can be counteracted by a system for coordinated vehicle charging. Interfaces are necessary to enable the user to communicate with the charging system. Based on the information provided by customers, network operators, and energy suppliers, an optimized schedule for EV charging can be created. Furthermore it is possible to react on unforeseen events in order to ensure a high level of power quality.     

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.     

电动汽车参与经济调度的多目标模型

电动汽车通过V2G技术可以作为电网负荷侧的备用容量,由此提出了一个多目标优化模型,将电动汽车车主成本和经济调度成本作为其目标函数,并让电动汽车通过有序充放电来作为经济调度时的备用容量.在满足各种约束条件下,采用多目标遗传算法(NSGA-Ⅱ)对模型进行求解.电动汽车的负荷特性、负荷波动、真实风能输出和机组停运状态均采用蒙特卡洛算法得到,并以一小时为时间间隔来进行仿真.由模型的求解结果可知,通过选择合适的pareto解集中的值,可以节省车主成本和经济调度的成本,并且可以实现对负荷削峰填谷的功能.     

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.     

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.     

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。     

风力发电与电动汽车充放电协同优化调度策略

大规模电动汽车无序充电以及风力发电在电网中的渗透率不断提高,给电力系统带来安全经济运行问题。在考虑电动汽车电池容量约束、充放电功率约束以及24 h的电动汽车运行行为特性基础上,建立了风力发电及电动汽车负荷平抑、降低电动汽车充放电费用和负荷峰谷差率的多目标协调优化调度模型;采用传统遗传算法和自适应非线性遗传算法对所建模型进行求解。仿真结果验证了模型的合理性以及算法的正确性。     

引导电动汽车用户驾驶行为的充电导航策略

电动汽车充电导航便于用户合理选择充电站,降低用户自身的时间成本和经济成本,缓解配电网端的负荷压力.在电网分时电价的基础上,考虑电动汽车充电路径的选择与车主的驾驶行为密切相关,通过对电动汽车的负荷设备分类建模,根据不同设备类型的重要程度及用户的电动汽车实际工况和地形因素,利用遗传算法分析最佳出行路径,提出以时间成本与经济成本之和最优为目标,引导用户驾驶行为的充电导航策略.在20kmtimes10km含3个充电站的区域内,通过3种不同充电导航策略仿真结果对比,验证所提出的导航策略的可行性和有效性.     

Nonlinear output feedback control of V2G single‐phase on‐board BEV charger

In this work, we consider the problem of controlling a single‐phase on‐board battery electric vehicle (BEV) charger with vehicle‐to‐grid (V2G) technology. The BEV charger consists of a bidirectional ac‐dc power converter connected to the single‐phase power grid, followed by a bidirectional dc‐dc power converter interfacing an EV battery pack. The main control objectives are fourfold: (i) Unitary Power Factor (UPF) in grid‐side; (ii) tight dc‐bus voltage regulation; (iii) safety battery charge and battery discharge during the grid‐to‐vehicle (G2V) mode and V2G mode, respectively; and (iv) asymptotic stability of the closed loop system. After an accurate system modelling, a nonlinear controller is designed using a backstepping design technique. The point is that the battery inner voltage is not accessible to measurement. Therefore, a nonlinear observer is invoked in order to estimate all non‐measured variables making the solution cheaper and noiseless. It is shown using a formal analysis and numerical simulations, that the proposed output feedback controller (combining a nonlinear controller and a nonlinear observer) meets all control objectives.     

智慧园区电动汽车有序共享充电需求分析

针对智慧园区充电设施少不能满足电动汽车发展需求问题,提出一种适用于智慧园区电动汽车有序共享充电需求建模分析方法,通过分析充电数据提供充电计划,并辅助园区能量管理系统制定有序充电策略.利用功率谱密度估计方法统计分析单个EV充电电流,并通过人工智能网络完成EV在线识别分类工作,基于插入时间、能量和工作日之间的相关性统计分析每个EV充电习惯,利用测量电网侧的电流来预测充电需求,基于核密度估计建立充电需求统计模型.通过某住宅小区充电设施采集的实际数据验证该模型精度的有效性和适用性,为智能园区电动汽车充电提供帮助.     

Impact of technology uptake on an Australian electricity distribution network

This paper presents simulation results for future electricity grids using an agent-based model developed with MODAM (MODular Agent-based Model). MODAM is introduced and its use demonstrated through four simulations based on a scenario that expects a rise of on-site renewable generators and electric vehicles (EV) usage. The simulations were run over many years, for two areas in Townsville, Australia, capturing variability in space of the technology uptake, and for two charging methods for EV, capturing people's behaviours and their impact on the time of the peak load. Impact analyses of these technologies were performed over the areas, down to the distribution transformer level, where greater variability of their contribution to the assets peak load was observed. The MODAM models can be used for different purposes such as impact of renewables on grid sizing, or on greenhouse gas emissions. The insights gained from using MODAM for technology assessment are discussed.     

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     

计及评价指标冲突的电动汽车充电负荷区间预测

电动汽车充电负荷具有强随机性,且受电池容量与用户用车行为影响。为有效预测充电负荷时序分布,本文提出一种计及评价指标冲突的充电负荷区间预测方法。首先,该方法分析日间充电负荷间时序相关性,并用强相关历史日充电负荷数据构建充电负荷预测所需的特征集。接着,采用弯曲高斯过程（warped Gaussian process , WGP）方法,并结合多种协方差函数来构建多个充电负荷区间预测模型。为解决多指标评价存在冲突和仅选择最优的一个预测模型会出现极端误差问题,本文应用面积灰关联决策方法,对各模型开展计及评价指标冲突的综合评价,并依据获取的面积灰关联贴近度,构建电动汽车充电负荷组合区间预测模型。实验结果表明,本文提出的方法能够获得更精确、覆盖率更高的充电负荷预测区间。     

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。     

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。     

储能式充电桩参与电网需求侧响应联合运行优化

储能式充电桩参与电网的联合运行,不但能够减小电网扩容成本,还可以获得参与电网需求侧响应辅助管理服务收益,从而降低充电桩运营成本。为探究需求侧响应中实时定价激励对储能式充电桩经济效益的影响,本文进行储能式充电桩参与电网需求侧响应的联合运行优化能量调度研究。本文考虑电网需求响应实时电价,建立以储能式充电桩参与需求侧响应的经济效益函数模型;以实现经济效函数最大化为目标,建立以储能式充电桩电量和容量约束的充电桩的电网联合运行优化模型;为应用对偶分解方法,将优化模型转化为典型凸优化问题,从而求得储能式充电桩最优化能量调度。基于储能式充电桩实际运行电量、容量和经济性参数进行仿真,仿真结果表明所提出的方法能够实现较快收敛,并且能够获得收益。     

电动汽车充电设施现场电磁兼容特性及防护技术研究

随着电动汽车快速发展,电动汽车充电设施的市场规模及需求日益扩大,充电设施的安全可靠性越来越受到重视。可靠的电磁兼容性能是电动汽车充电设施现场安全可靠运行的必备条件,研究充电设施现场电磁兼容特性及防护技术,做好充电设施现场电磁兼容防护,保证充电设施的安全可靠性,对推动电动汽车发展具有重要意义。根据电动汽车充电设施现行电磁兼容标准,研究充电设施现场电磁兼容测试方法,选择典型充电站进行现场测试,分析充电设施现场电磁干扰特性,并提出电磁干扰抑制与防护措施。充电机产生的谐波干扰是现场主要的电磁兼容问题,采用集中治理与分散治理相结合的方式或者选用具备APFC功能的充电机,能有效解决现场谐波干扰问题。在充电设施开发建设过程中,充分重视充电设施的电磁干扰抑制与防护,提高充电设施电磁兼容性能,是充电设施安全运行的根本保证。     

插电式电动车有序充电调度控制算法研究

主要研究基于电动车参与的电网有序用电调度控制优化问题.首先,通过智能电表采集某地区历史电荷信息,并通过该信息对该地区未来24 h用电负荷进行动态预测.然后,基于当前实时居民用电负荷情况和已提交的充电订单信息,提出了一种智能有序充电调度控制算法,即根据订单信息对电动车进行有序编排,并在考虑居民实时用电负荷情况下,如果出现实时负荷超出变压器承受最大负荷的冲突,则需要考虑对充电编排做动态更新.所提出的基于智能电表有序充电系统在保证变压器稳定运行前提下,提高了充电效率,优化了谷峰差,从而实现了削峰填谷.最后,仿真结果验证了所提出算法的有效性.     

When smart grid meets PHEVs: a smart load distribution mechanism in smart grid

Along with the increasing popularity of electric vehicles caused by economic and environmental incentives, the penetration of plug-in hybrid electric vehicles (PHEVs) poses a great threat to the power grid, especially to the aggregated load in the power system. Motivated by this observation, in this paper, we analyze the impact of large-scale usage of PHEVs and address the load distribution problem by solving a decentralized optimization problem and smoothening the peak load with pricing strategies in the power grid. We also investigate the influence of charging time and charging mode on load distribution, as the charging price varies with the changes of these two factors. Our simulation study on PJM’s data warehouse shows that the proposed strategies can well smooth the peak load by pricing on the charging time and mode. The results also indicate that our strategies always distribute the load in a smoother manner at a smaller load fluctuation compared with other schemes (e.g., First Come First Service and PMCS) and thus improving the stability and reliability of the power grid.