面向配电网灵活性的电动汽车充放电控制策略研究

从配电网灵活性出发,提出了面向配电网灵活性的电动汽车充放电双层控制策略。首先建立了配电网灵活性指标体系和配电网灵活性评估方法,在此基础上提出了电动汽车充放电双层控制策略。上层决策者为配电网调度中心,以最优化配电网灵活性为目标制定调度指令发布给下层;下层决策者为各电动汽车充电站,通过制定其所管辖的电动汽车的充放电计划来响应上层指令,达到和上次指令偏差最小化。最后,算例验证了所提电动汽车充放电控制方案能有效地提高配电网灵活性。     

动态分时电价机制下的电动汽车充放电调度策略研究

峰谷分时电价下会在基础负荷低时形成新的负荷峰。建立了一个以电动汽车充放电费用最小,电动汽车接入所引起的电网损失最小以及对电压稳定性的影响最小为优化目标的电动汽车充、放电调度数学模型,并通过凸优化算法解决了多变量、多目标和高维优化问题。考虑反复潮流计算的繁琐性,利用网络损耗灵敏度和L-P灵敏度来提升调度效率,并将电压稳定作为约束来保证调度过程中不会出现电压失稳的情况。最后在改进的IEEE33节点配电网系统中进行算例验证,表明本文所提方法可以降低充放电费用,满足用户对经济性的考虑,另外可以降低电动汽车充电负荷对系统的网络损耗和电压稳定的影响。     

深化调峰的电动私家车/出租车群组合优化充放电策略

文章提出一种深化调峰的私家车/出租车群组合优化充放电策略,该策略采用蒙特卡洛模拟法模拟私家车、出租车和公交车3种类型电动汽车充电负荷,分析不同类型电动汽车对电网负荷峰谷特性的影响。基于车主意愿差异确定车主对充电价格差和放电电价的响应特性,将电动汽车细分为V0G,V1G,V2G 3种类型,并计算出3种类型电动汽车数量。进而以负荷峰谷差和负荷方差最小为优化目标,决策求解各类型电动汽车充放电起止时刻。仿真算例结果表明:文章所提充放电策略能更好地激励各类型电动汽车积极参与调峰,可进一步减小负荷峰谷差。     

考虑电动汽车充放电响应的微电网混合储能配置

由于微电网内部新能源出力以及负荷的波动性,导致微电网内部新能源的弃风弃光以及负荷高峰时段失负荷问题严重。提出了一种考虑电动汽车充放电响应的混合储能配置方法。充分利用电动汽车的灵活充放电特性,以分时电价为背景最大程度减少微电网内的失负荷惩罚成本,并促进弃风弃光功率的消纳;利用混合储能系统对电动汽车响应后微电网内的弃风弃光功率进行吸收并在负荷高峰时段放电,以减少失负荷功率,以净现值作为混合储能配置的目标函数。利用粒子群算法求解净现值最大情况下的混合储能配置方案。仿真结果表明,该配置方案具有更好的经济性。     

基于动态规划的电动汽车用户侧充电优化方法

实现电动汽车有序充电是将电动汽车纳入智能电网的重要内容之一。从电动汽车用户需求角度出发,提出了以用户为充电过程决策主体的有序充电方法。以充电费用最低为目标建立了用户侧优化模型,并应用动态规划建立了求解方法。按照给定算例,对电动汽车的充（放）电过程进行了决策。计算结果表明,在满足功率约束和用户充电目标的前提下,通过用户优化控制策略可有效减少电动汽车充电过程产生的费用。该文提供了电动汽车有序充电的用户层方案。     

基于智能代理分布式管理系统的电动汽车充放电策略

随着电动汽车的发展与普及,提出了一种用于协调电动汽车充放电的智能多代理分布式管理系统.建立以用户充电费用支出和负荷曲线峰谷差最小化为目标的多目标优化策略,采用蒙特卡洛方法模拟电动汽车用户行为.在峰谷电价的引导下,用户利用谷时段充电、峰时段放电提高自身利益,在初步得到理想的充电时段区间后,引入学习训练机制调整可间断充放电...     

电动汽车有序充放电控制与利用研究新进展

电动汽车大规模接入电网会增加电网运行的不确定性,电网侧亟需做好相应准备。采用有序充放电控制可以减小电动汽车的时空不确定性给电网带来的影响,缓解电网升级扩容压力,采用V2G技术可以为电网提供辅助服务,丰富电网的运行方式。根据国内外最新研究进展,从充电负荷建模与计算、电动汽车接入对电网产生的影响、电动汽车有序充电控制、电动汽车为电网提供辅助服务4个方面进行评述,并指出未来的研究方向。     

基于分时充电电价的电动汽车消纳风电的机组调度优化模型

随着风电的大规模入网,其间隙性和随机性导致弃风现象严重,电动汽车的快速发展为风电消纳提供了新途径。文章以风电消纳最多、负荷方差最小和火电机组发电成本最低为目标函数,综合考虑电力系统的功率供需平衡、火电机组和风电出力等约束条件,建立了基于分时充电电价的电动汽车消纳风电的机组调度优化模型。根据电动汽车负荷对充电电价的响应,得出电动汽车的充电负荷,进而得到电力系统总负荷,以此为基础采用分步优化的方法对模型进行求解。首先以负荷方差最小和风电消纳最多为目标,通过多目标遗传算法NSGA-II对风电出力进行优化;然后以火电机组的发电成本最低为目标对火电机组的出力进行优化,达成风火机组的联动调度。算例结果表明,对电动汽车实行分时充电电价能够提升风电的消纳能力,平滑负荷曲线,降低火电机组的发电成本。     

基于智能电网的电动汽车充放电分时电价及引导策略

为了以绿色、环保能源满足全球可持续发展的需求,可再生能源和电动汽车在全球范围内受到广泛推崇.在此情形下,高比例可再生能源发电和大规模电动汽车无序分散接入电网必将导致供求曲线的不稳定.为此,借助云存储技术和智能电网,提出了一种基于供求曲线的电动汽车充放电分时电价,并在制定充放电价格时考虑充电站的空闲率.以实现充电站和用户...     

分时电价环境下计及电动汽车充放电影响的微电网优化控制策略

文章结合电动汽车出行特性与电池特性,提出了电动汽车有序充放电模型;以计及电动汽车充放电影响的负荷曲线为基础,提出了以经济效益和环境效益为优化目标的微电网运行优化模型。在典型微电网中,通过差异进化算法求解模型,比较不同情况下电动汽车的充放电功率变化与微电网的优化控制策略及优化结果,验证了所提策略的正确性与有效性。     

Impact of charging efficiency variations on the effectiveness of variable-rate-based charging strategies for electric vehicles

The huge energy demand coming from the increasing diffusion of plug-in electric vehicles (PEVs) poses a significant challenge to electricity utilities and vehicle manufacturers in developing smart charging systems interacting in real time with distribution grids.These systems will have to implement smart charging strategies for PEV batteries on the basis of negotiation phases between the user and the electric utility regarding information about battery chemistries, tariffs, required energy and time available for completing the charging. Strategies which adapt the charging current to grid load conditions are very attractive. Indeed, they allow full exploitation of the grid capacity, with a consequent greater final state of charge and higher utility financial profits with respect to approaches based on a fixed charging rate.The paper demonstrates that the charging current should be chosen also taking into account the effect that different charging rates may have on the charging efficiency. To this aim, the performances of two smart variable-rate-based charging strategies, taken as examples, are compared by considering possible realistic relationships between the charging efficiency and the charging rate. The analysis gives useful guidelines for the development of smart charging strategies for PEVs as well as for next-generation battery charging and smart grid management systems.     

基于用户需求分类的电动汽车参与的微网运行优化

文章从电动汽车用户需求分类的角度切入,提出基于用户需求分类的充、放电策略。在广义需求侧资源的框架下,运用启发式规则与粒子群算法相结合的方式进行求解,综合利用微网中各类广义需求侧资源。算例分析表明,可利用用户需求的差异,抑制微网负荷波动、降低运行成本及用户充电成本,达到微网与电动汽车用户双赢的局面。     

并联式混合动力汽车模糊能量管理策略优化

文章分析了并联式混合动力汽车电机和发动机功率的实时分配问题,为提高发动机和电机的工作效率,设计了一种基于蚁群优化的模糊控制策略。通过发动机台架性能测试的实验数据,拟合得到发动机工作效率函数,并绘制发动机工作效率图。根据发动机工作效率图制定模糊控制规则,并以发动机工作效率为优化目标,利用蚁群算法对模糊隶属度函数参数进行优化。ADVISOR的仿真结果表明,与未优化的模糊控制策略相比,优化后的模糊控制策略能更有效地改善混合动力汽车的整车燃油经济性,并能更好地解决电池过度放电的问题,提高电池的工作寿命。     

An improved energy management strategy for FC/UC hybrid electric vehicles propelled by motor-wheels

The hybridization of the fuel-cell electric-vehicle (FCEV) by a second energy source has the advantage of improving the system's dynamic response and efficiency. Indeed, an ultra-capacitor (UC) system used as an energy storage device fulfills the FC slowest dynamics during fast power transitions and recovers the braking energy. In FC/UC hybrid vehicles, the search for a suitable power management approach is one of the main objectives. In this paper, an improved control strategy managing the active power distribution between the two energy sources is proposed. The UC reference power is calculated through the DC link voltage regulation. For the FC power demand, an algorithm with five operating modes is developed. This algorithm, depending on the UC state of charge (SOC) and the vehicle speed level, minimizes the FC power demand transitions and therefore ameliorates its durability. The traction power is provided using two permanent magnetic synchronous motor-wheels to free more space in the vehicle. The models of the FC/UC vehicle system parts and the control strategy are developed using MATLAB software. Simulation results show the effectiveness of the proposed energy management strategy.     

An online prognostics-based health management strategy for fuel cell hybrid electric vehicles

As the energy transformation in the transportation sector is taking place driven by the development of fuel cell technologies, fuel cell hybrid electric vehicles become promising solutions owing to their long driving duration and zero emissions. However, the unsatisfied lifespan of fuel cells is an inevitable obstacle for their massive commercialization. This paper aims to propose an online adaptive prognostics-based health management strategy for fuel cell hybrid electric vehicles, which can improve the durability of the fuel cell thanks to online health monitoring. Here, particle filtering method is adapted for online fuel cell prognostics and the uncertainty of the predicted results is calculated based on the distribution of particles. A health management strategy is developed based on prognostics and a decision-making process is designed by considering the prognostics uncertainty through a decision fusion method. The obtained results show that the developed strategy has effectively improved the durability of the on-board fuel cell by up to 95.4%. Moreover, a sensitivity analysis of the prognostics occurrence frequency and probability calculation has also been conducted in this paper.     

Multi-mode control strategy for fuel cell electric vehicles regarding fuel economy and durability

A proton electrolyte membrane (PEM) fuel cell system and a Li-ion battery (LIB) are two power sources in a fuel cell electric vehicle (FCEV). The fuel cell system is composed of a fuel cell stack and subsystems for air/hydrogen supply and cooling water. The operation procedure of the fuel cell system can be generally separated into several processes, e.g. starting up, normal/abnormal working and shutting down. In this paper, a multi-mode real-time control strategy for a FCEV is proposed. The strategy is established based on three typical processes (starting up, normal working, shutting down) of the fuel cell system, taking the fuel economy and system durability into consideration. The strategy is applied into a platform vehicle for the 12th 5-year project of “the next generation technologies of fuel cell city buses”. Experiments of the “China city bus typical cycle” on a test bench for the bus were carried out. Results show that, the fuel economy is 7.6 kg (100 km)⁻¹ in the battery charge-sustaining status. In a practical situation, a total driving mileage of more than 270 km can be achieved. Cycle testing also showed that, the degradation rate of the fuel cell was reduced to half of the original level. No performance degradation of the LIB system was observed in the cycling test.     

Optimal dispatch of hydrogen/electric vehicle charging station based on charging decision prediction

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts.     

The pricing of charging for electric vehicles in China—Dilemma and solution

The number of electric vehicles in China is expected to grow rapidly, triggering the nationwide large-scale construction of charging stations. At the same time, a reasonable charging price has not been established. This article records the views of station operators and EV users and calculates a charging pricing range. The price should not only ensure the profit of operators, but also help reduce EV users’ expenditure compared to using internal-combustion-engine vehicles. Based on current energy prices and battery costs, charging stations are unable to make profit, and the pricing shortfall is up to 0.78 RMB yuan(kWh)⁻¹. Only with a 25% increase in energy price or 25% reduction in battery cost can charging stations become profitable. Several suggestions are proposed to improve station profits. First, ensuring a high station load is helpful to increase profits, and it is estimated the reasonable number of chargers in Beijing is approximately 6000, distributed among 672 stations. Second, the use of storage batteries for on/off-peak electricity self-management can also increase the annual profit by 600,000 RMB yuan. In addition, other methods like a jointed station alliance, multiple energy supplement approaches, vehicle-to-grid technology and state subsidy can also accelerate the development of the charging service industry.