考虑风电消纳的居民小区电动汽车有序充电控制方法

随着电动汽车的保有量不断提升,其充电过程对电网的影响也不断加大。为降低电动汽车无序充电对电网的冲击,提升清洁风电的消纳,提出一种居民小区电动汽车有序充电控制方法。方法使用居民小区的电力预测信息与当地风电产生预测信息,计算获取理想风电消纳的小区电动汽车充电负荷参考曲线。在参考曲线的基础上,使用差分进化算法优化可以得到电动汽车充电功率预分配曲线。在实际应用的实时分配问题上,给出了合适的功率分配策略。最后通过算例仿真实验,验证了算法的有效性。仿真实验表明,提出的电动汽车有序充电控制方法具有较好的有效性和实用性。     

充电站内引导电动汽车有序充电控制方法仿真

传统的电动汽车充电控制通常采用基于多代理、基于T-S模糊控制器、基于交替方向乘子的有序充电控制方法,但这几种方法的电网负荷波动率较高,为此提出一种充电站内引导电动汽车有序充电控制方法。应用滚动优化法对集中充电站进行充电时段划分;结合灰色理论对电动汽车用户短期的用电负荷状况进行预测,通过预测未来一段时间的电动汽车用户用电负荷值,对各个时段电动汽车用户的充电计划进行优化求解,最终实现电动汽车有序充电控制。为了验证上述方法的有效性,与三种传统控制方法进行对比,得出上述方法的充电负荷波动率为49.2%,通过比较可知,所提方法的充电负荷波动率最低,证明了上述方法的有效性。     

电动汽车充电负荷预测研究

电动汽车的快速发展会逐渐增大电网的运行压力,而预测电动汽车的充电负荷有利于电网的运行和合理调度.论文分析了电动汽车充电负荷的影响因素,得到不同充电方式中充电功率的变化,分析了三种类型电动汽车的充电频率、日行驶里程和起始充电时间,并建立了相应的概率密度函数模型.最后采用蒙特卡洛算法模拟计算出日充电负荷,为电动汽车有序充电研究提供了基础.     

电动汽车智能充电策略研究综述

发展电动汽车是减少对石油的依赖以及解决环境污染的有效途径。但是,大规模电动汽车无序充电会严重影响电网的稳定性,主要表现在电网负荷峰值升高、电网电压下降以及电网能量损失变大等方面。众所周知,对电网进行扩容改造将需巨大的成本,而智能充电无需对电网进行大幅度改造即可有效地解决这一难题。智能充电不仅能为电力运营商创造可观的经济效益,同时还能使电动汽车用户受益。智能充电主要分为集中式充电和分散式充电。集中式充电根据电网信息和电动汽车的充电需求规划电动汽车的充电策略。充电策略的制定主要以电网运行成本最小、电网能量损失最小、电网负荷方差最小等为目标。分散式充电主要在获取电网状态信息和电动汽车的充电需求后制定浮动电价以激励电动汽车用户避开用电高峰期。最后,文章简要介绍了在智能充电框架下电动汽车与可再生能源融合的潜力、智能充电对电池的要求以及相应的通讯网络构建等问题。     

电动汽车的充电模式及充电设施建设运营模式研究

电动汽车是节能、环保和低碳经济的发展需求,而电动汽车电池既是发展电动汽车的核心,更是电力工业与汽车行业的关键结合点。在政府对电动汽车产业的大力推动下,我国电动汽车产业将步入快速发展期,这也极大地推动了电动汽车充电站和充电桩的建设,大量电动汽车的充电行为将会给电网带来较大影响。电动汽车的普及程度、类型、充电时间、充电方式以及充电特性的不同会使电动汽车对电网的影响发生变化。介绍了世界范围内电动汽车电池的四种充电模式：换电模式,有线慢充,有线快冲,无线充电。这四种充电模式目前处在不同的发展阶段,面临不同的问题,并各自具有不同的发展策略。对目前已经形成的电动汽车制造厂商主导模式、电网企业主导模式和第三方建设运营企业主导模式,进行分析研究和比较     

计及峰谷电价的电动汽车负荷模型分析

大规模电动汽车无序接入配电网会导致电网负荷峰值增加。首先,本文通过分析电动汽车行驶特性,根据电动汽车电量需求、电池荷电状态、充放电功率等约束条件,基于电动汽车有序充放电概率模型和功率期望,构建最小负荷峰值目标函数。再通过遗传算法求解,以峰谷电价时间作为优化变量,得到优化前后的电网负荷曲线。然后分别研究了在不同峰谷电价时间以及在不同用户响应度下电网负荷曲线的变化,并与电动汽车无序充电时的负荷曲线进行对比。最后讨论分析了用户响应度和充电模式对电网负荷峰值的作用效果。     

基于约束优化的电动汽车快速充电方法研究

在低碳经济的需求推导下,电动汽车的相关技术得到了快速的发展。该文针对电动汽车充电速度慢、电池易损耗等问题,提出了一种基于约束优化的电动汽车快速充电方法。为此,首先构建了等效电路模型、电池组模型、热模型、以及电池老化模型。在此基础上,提出将直流快速充电站优化问题与电动汽车快速充电问题相结合。利用约束优化的方法,在最短的时间内找到充电的最优点,从而在提高充电效率的同时,能够使充电站的利润最大化。最后通过实验验证了所提出方法的有效性。     

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。     

电动汽车铅酸电池脉冲快速充电系统设计

为了缩短电动汽车铅酸电池的充电时间,提高能量接受率,基于带放电电流脉宽调制技术,设计了汽车电池脉冲快速充电系统.该系统采用嵌入式控制和上位机监控相结合的方式,软硬设计合理,性能可靠.试验数据分析表明,该系统有效缓解了电池的极化现象,缩短了汽车铅酸电池的充电时间,提高了电池能量接受率.系统具有广泛的应用前景.     

基于模糊控制的铅酸电池充电系统研究

作为绿色环保电动汽车动力应用的主要电源之一,铅酸蓄电池的充电技术受到了工业界的广泛关注,如何研发高效、高可靠的充电器成为急需解决的关键技术.在研究铅酸电池充电特性的基础上,提出了利用模糊控制技术对充电过程进行智能控制的方法.以NE C0881单片机为控制核心,设计了一种针对铅酸电池的智能充电系统,并详细介绍了模糊控制器的设计.实验结果表明,该系统可对大容量电池的复杂充电过程进行最优控制,充电快速、效率高,充电安全,实现了充电过程的智能控制.     

电动汽车用飞轮电池充放电控制系统研究

针对现有电池技术还不能满足电动汽车对电池系统能量密度和功率密度还有寿命的要求,本文结合蓄电池的高比能量和飞轮电池的高比功率,提出了基于蓄电池和飞轮电池的复合电源。建立了飞轮电池的充放电的数学模型,并提出了飞轮电池充放电的控制策略。飞轮充电过程中采用模糊-PI复合控制策略,放电过程中根据驱动电机需要的瞬时电流值来控制斩波器开关的通断。结果表明系统具有较好的动态特性。     

Intelligent power management in micro grids with EV penetration

Large deployment of Electric Vehicles (EVs) adds new challenges in the operation of a microgrid. Assuming that a number of EV owners allow their batteries to charge when their cars are parked, this paper proposes an approach that aims to find suitable individual active power set-points corresponding to the hourly charging rate of each EV battery connected to the microgrid. A multi agent system based controller is designed to find these active power set points for optimal power management of EVs, distributed energy resources in the microgrid, and the loads.     

Modelling the weekly electricity demand caused by electric cars

The travel demand model mobiTopp is used to analyse the effects of an increasing fleet of electric vehicles on the electricity demand. For this purpose mobiTopp has been extended to support EVs. These extensions comprise an EV ownership model, different types of EVs, namely Battery Electric Vehicles (BEVs) and Extended Range Electric Vehicles (EREVs), the charging process of EVs, and modified travel behaviour for persons using BEVs. The model is used to simulate the electricity demand for charging of EVs in the Greater Stuttgart Area over a period of one week. The daily electricity demand for EV charging contains a distinct peak around 6 p.m. for each workday and an additional morning peak around 8 a.m. when recharging at the workplace is possible. This additional electricity demand is concentrated in zones with residential use during the evening peak and in zones with office/industrial use during the morning peak.     

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.     

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.     

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.     

Discrete event optimization of a vehicle charging station with multiple sockets

The relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

     

考虑充放电不平衡的风电功率平滑电池分组控制

平滑风电场输出功率能够有效减小风电场并网对电力系统的影响.为改善电池分组控制实现风电功率平滑过程中的电池组充放电能量不平衡问题,本文基于充放电不平衡度和寿命衰减指标,分析得出了电池分组控制充放电不平衡状态与电池寿命衰减之间的定量关系.在此基础上,针对充放电不平衡问题提出了一种考虑充放电不平衡的改进控制策略.根据低通滤波...     

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.     

Fuzzy PWM based on Genetic Algorithm for battery charging

This paper investigates the use of fuzzy logic control together with Genetic Algorithm (GA) to tune corresponding membership functions in a pulse width modulation voltage (PWM)-based converter for battery charging. For quick charging purposes, a current-charging mode is built on the charging battery series with small impedance resistance. Once the rechargeable battery voltage reaches the preset output voltage value during the current-charging mode, constant voltage-charging mode should take to do the battery charging. No matter what you use a voltage/current mode for battery charging, the PWM converter with tuning duty cycle is necessary to obtain various output voltages to charge battery. The proposed pulse width modulation (PWM) control strategy based on fuzzy logic systems is applied to charge batteries for buck converter. A simulation is proposed to illustrate the efficiency of the design proposed in this paper.