Operation Strategy of EV Battery Charging and Swapping Station

An operation strategy of the electric vehicle （EV） battery charging and swapping station is proposed in the paper. The strategy is established based on comprehensively consideration of the EV charging behaviors and the possible mutual actions between battery charging and swapping. Three energy management strategies can be used in the station： charging period shifting, energy exchange between EVs, and energy supporting from surplus swapping batteries. Then an optimization model which minimizes the total energy management costs of the station is built. The Monte Carlo simulation is applied to analyze the characteristics of the EV battery charging load, and a heuristic algorithm is used to solve the strategy providing the relevant information of EVs and the battery charging and swapping station. The operation strategy can efficiently reduce battery charging during the high electricity price periods and make more reasonable use of the resources. Simulations prove the feasibility and rationality of the strategy.

Research on SFLA-Based Bidirectional Coordinated Control Strategy for EV Battery Swapping Station

As a good measure to tackle the challenges from energy shortages and environmental pollution,Electric Vehicles(EVs)have entered a period of rapid growth.Battery swapping station is a very important way of energy supply to EVs,and it is urgently needed to explore a coordinated control strategy to effectively smooth the load fluctuation in order to adopt the large-scale EVs.Considering bidirectional power flow between the station and power grid,this paper proposed a SFLA-based control strategy to smooth the load profile.Finally,compared simulations were performed according to the related data.Compared to particle swarm optimization(PSO)method,the presented SFLA-based strategy can effectively lower the peak-valley difference with the faster convergence rate and higher convergence precision.It is important for the swapping station that energy exchanging mode can supply energy for large-scale EVs with a smoother load profile than one-way charging mode.

Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

考虑换电需求响应的换电站参与电能量-调频市场竞价策略

电动汽车(EV)大规模发展会加大电网的调峰调频压力。通过换电站市场化运营,可有效激励换电站利用EV换电需求和换电站电池灵活性资源,缓解电网调峰调频压力。为此,提出了换电站参与电能量市场和调频辅助服务市场的协同竞价策略及其两阶段优化模型。第1阶段依据调频市场需求制定换电价格,引导EV调整换电需求,优化冗余电池数量及时序,以便与调频需求趋势一致,进而提升换电站调频服务收益;第2阶段考虑换电站需求响应和市场调峰调频需求,制定换电站竞价策略。所提出竞价策略在优先满足EV换电需求的同时,可降低换电站充电成本、提升调频服务收益,并助力电网调峰调频。算例仿真结果验证了所提策略的有效性。

结合车主期望的充换电站辅助调频策略

随着可再生能源的高渗透与新型电力系统构架的不断完善,电动汽车既可作为可控负荷,也可作为分布式电源,具有优良的调频特性。为此,提出了一种结合车主期望的充换电站辅助调频策略。首先,基于入/离网时间、入网/期望荷电状态量、电动汽车充放电限制等车主期望建立电动汽车充/放电能力评估模型。其次,结合电动汽车充/放电能力与有功-频率特性曲线建立电动汽车双层控制的调频模型。最后,考虑电网侧和车主侧的需求,提出充换电站参与电网调频策略。仿真结果表明,所提控制策略在满足车主期望及电动汽车自身限制的情况下可有效调节电网频率、提高电网安全稳定运行水平及灵活性。

集中型充电站高维多目标优化调度模型研究

为了增强电动汽车电池调度的合理性,在集中型充电站换电模式下,结合集中型充电站、配送站和换电站,分析电池的联合调度问题。首先,考虑充电成本、充电负荷、运输成本和时间成本,提出了一种集中型充电站换电模式高维多目标优化调度模型。然后,采用非支配排序遗传算法-Ⅲ(non-dominated sorting genetic algorithm-Ⅲ,NSGA-Ⅲ)对该调度模型进行优化。同时,根据实际调度问题,对NSGA-Ⅲ的交叉和变异方式进行改进。最后,通过仿真实验测试算法的性能。实验结果表明,与其他高维多目标优化算法相比,该算法在换电模式下的优化性能具有明显的优势。

参与电网削峰调节的电动重卡换电站调度策略

换电站因其快速、便捷的特点获得了迅猛的推广和发展。在电动重卡领域,换电站建设已初具规模并保持高速发展。如何对电动重卡换电站进行充电调度以实现电网友好,是一个重点研究方向。首先,针对电动重卡换电需求在时间上分布规律较弱的特点,使用统计分析法分析换电站历史运行数据,得到一天内各时段的换电需求预测值;其次,提出基于换电需求预测的日内调度策略和基于实际换电需求的日内实时修正策略,以确定电池充电数量和允许充电时间;然后,建立以电网削峰为目标的时间-功率模型,结合电网背景负荷曲线,使用差分算法对电池的充电功率和实际充电时间进行求解;最后,通过算例证明了调度策略和时间-功率模型的有效性。

基于改进多目标遗传算法的电动汽车换电站选址研究

换电模式高效、便捷的补能形式预计成为未来电动汽车充能的主流方式。换电站选址是否合理影响重大,本文以换电站建设成本及用户出行成本最小化、用户覆盖率最大化为目标函数,建立双目标混合整数规划模型,设计改进带精英策略的非支配排序遗传算法,获得帕累托最优解集。通过数值模拟,验证了模型可行性,为电动汽车换电站网络规划提供了依据。

典型场景下重型载货车换电站运营经济性研究

换电重型载货车是近年来新能源重型载货车推广的主要车型。重型载货车换电站为用户车辆提供换电服务,其运营经济性直接关系到未来换电重型载货车能否规模化发展。建立了重型载货车换电站运营经济性测算模型,针对5种典型应用场景下的重型载货车换电站的盈亏水平及盈利平衡点进行了分析。研究发现,重型载货车换电站运营经济性与换电站年换电量密切相关,换电运营企业为保证运营收益,应重点考察目标应用场景下换电重型载货车车队规模以及月行驶里程。

电动汽车换电服务使用意愿等研究动态

选取来自国际学术期刊的论文,以概述形式对城市交通理论方法、实证分析等学术研究成果进行总结性介绍,旨在增强城市交通业界和学界对国际学术动向和研究热点的关注,促进学术交流。本期共选取4篇论文,前3篇论文与电动汽车充电、换电相关,分别关注电动汽车换电服务使用意愿、共享微出行服务和新型补能基础设施用户使用意愿,以及公众是否支持建设电动汽车换电站,并针对政策制定者提出了富有建设性的意见;最后一篇论文关注北京市城市绿化对共享单车使用的影响,认为城市绿化具有显著潜力,能够降低汽车依赖并减少出行相关的碳排放。

基于换电服务定价策略及动态调控方法的含充换电站微电网系统双层优化调度

换电服务价格高是电动汽车换电模式普及率低的重要因素之一,为了提高换电模式使用程度,充分发挥换电模式参与系统调度时所发挥的削峰填谷作用,该文提出一种考虑用户参与度的换电服务定价策略及动态调控方法。首先,建立计及时间成本的充电服务与换电服务总费用差价模型,并依据消费者心理学原理构建服务差价-用户参与度曲线;其次,制定换电服务定价策略,并提出相应的动态调控方法;最后,建立含充换电站(battery charging and swapping station,BCSS)的微电网联合系统双层优化模型。上层根据换电服务定价策略及动态调控方法,制定出用户参与度高的换电服务电价;下层根据用户响应换电服务电价后的负荷量,以微电网联合系统总运行成本最低为目标调度机组出力,并以用户满意度作为衡量换电服务电价的指标,合理调整下一时段换电服务电价。通过算例分析,所提方法在实现系统负荷削峰的同时,降低微电网联合系统总运行成本,体现了所提定价策略及动态调控方法的有效性。

考虑充放电策略的换电站与风电-碳捕集虚拟电厂的低碳经济调度

风电-碳捕集虚拟电厂与换电站均具有一定程度的低碳性能,有助于电力系统低碳化。考虑系统源荷两侧低碳配合问题,提出一种考虑充放电策略的换电站与风电-碳捕集虚拟电厂协调低碳调度方法。在源侧引入了采用综合灵活运行方式的碳捕集电厂与风电场聚合,形成风电-碳捕集虚拟电厂,在荷侧考虑了具备“源荷”双重角色的换电站,并制定充放电策略,分析二者各自在低碳运行方面的不足之处,研究二者的低碳互补问题,并建立了以系统综合成本最优为目标函数的低碳经济调度模型。最后,采用改进的IEEE 30节点系统进行仿真验证,算例结果表明所提调度模型能在提高全网风电消纳能力的同时,实现系统低碳经济运行。

计及柔性负荷和换电站的综合能源系统优化调度

在“碳达峰,碳中和”背景下,未来能源发展将由单一的能源系统向综合能源系统转变。随着电动汽车车网互动技术的发展与应用,柔性负荷占比的显著提升,综合能源系统中的可调度资源将愈加丰富。在此背景下,本文将含电动汽车换电站和多类型柔性负荷的综合能源系统作为研究对象。首先,建立包含风电、储能、热电联产机组、燃气锅炉等设备的综合能源系统模型。其次,在计及电、热、气柔性负荷响应的基础上,将电动汽车换电站的充放电特性考虑到综合能源系统的优化调度中,以系统运行成本最小为目标,构建综合考虑多类型柔性负荷响应和换电站有序调度的综合能源系统优化模型。最后采用Yalmip进行建模并调用Cplex求解器对模型求解。算例结果表明,调度周期内系统运行成本降低了13.04%,风电消纳率提高8.65%,负荷峰谷差降低24.58%,验证了所提模型在降低系统运行成本、削峰填谷以及消纳风电等方面的有效性。

中国新能源汽车基础设施发展现状及油气企业应对策略

中国新能源汽车市场发展速度超过预期,未来几年发展仍将处于快车道,中央和地方政府相继发布规划类文件推动产业加速升级。油气企业正在积极探索转型路径,加速进入新能源汽车基础设施市场,谋求新旧业务融合。考虑到新能源汽车配套基础设施需求潜力大、充换电业务可以与现有加油站实现协同发展等机遇,以及终端服务新的商业模式和布局方式出现、未来市场主体更加多元化、市场竞争加剧等挑战,油气企业需要适应新的市场竞争,逐步构建自身的核心竞争力。一是融合创新,抓住新能源汽车基础设施发展机遇,优化销售网络布局,开展多种模式的示范试点;二是因地制宜,充分利用现有资产,在不同区域实施差异化的销售终端发展策略;三是因势利导,关注换电业务,考虑与新能源汽车企业合作,通过优势互补共同推动新能源汽车基础设施的布局和建设,实现合作共赢。

可移动式矿用电动卡车换电站的设计和应用

随着社会的进步和科技的发展,矿用电动车近些年发展迅速,但仍受续航里程和充电时间制约,电池交换式换电站是解决这一问题的有效方案。基于为某露天煤矿再电气化配置的矿用电动卡车配套建设换电站项目,提出可移动式矿用电动卡车换电站设计方案并顺利实施。介绍换电站主要系统,结合矿用电动卡车换电流程对各系统进行模块化设计,通过对结构和基础进行优化设计,使各模块便于拆装和整体搬运,使换电站具备可移动的条件,可以有效控制矿用电动卡车往返换电站过程中时间和能量损耗的增加,同时缩短建设周期。

考虑换电站的综合能源系统低碳经济调度

换电站作为分布式能源存储以及需求响应资源,能够为综合能源系统调度提供更大的灵活性,同时,碳交易是提高能源利用效率、减少环境污染的有效手段。为此提出了考虑换电站的综合能源系统低碳经济调度模型。首先,建立考虑换电站的综合能源系统拓扑。其次,引入奖惩阶梯型碳交易机制,建立了以系统运行成本和碳交易成本最低为优化目标的低碳经济调度模型,运用分段线性插值将原模型转化为混合整数线性规划问题。使用Yalmip对模型进行建模并利用Cplex求解器进行求解,通过对比分析换电站不同运行方式,结果表明有序充放电方式的换电站在综合能源系统中具备灵活调度优势,在低谷时段进行电池充电,高峰时段灵活释放电能,有效地减小了负荷峰值,缩小峰谷差。此外,奖惩阶梯型碳交易机制为综合能源系统提供了经济激励,通过出售多余的碳配额获得利润,有助于降低碳交易成本,并且分析了碳交易价格参数,碳交易价格增长会促使综合能源系统增加低碳能源比例,降低碳排放,保持系统的经济性和环保性。

Optimal sizing of PV and battery-based energy storage in an offgrid nanogrid supplying batteries to a battery swapping station

Nanogrids are expected to play a significant role in managing the ever-increasing distributed renewable energy sources. If an off-grid nanogrid can supply fullycharged batteries to a battery swapping station(BSS)serving regional electric vehicles(EVs), it will help establish a structure for implementing renewable-energyto-vehicle systems. A capacity planning problem is formulated to determine the optimal sizing of photovoltaic(PV) generation and battery-based energy storage system(BESS) in such a nanogrid. The problem is formulated based on the mixed-integer linear programming(MILP)and then solved by a robust optimization approach. Flexible uncertainty sets are employed to adjust the conservativeness of the robust optimization, and Monte Carlo simulations are carried out to compare the performance of the solutions. Case studies demonstrate the merits of the proposed applications and verify the approach.

Configuration and operation combined optimization for EV battery swapping station considering PV consumption bundling

Integration of electric vehicles(EVs),demand response and renewable energy will bring multiple opportunities for low carbon power system.A promising integration will be EV battery swapping station(BSS)bundled with PV(photovoltaic)power.Optimizing the configuration and operation of BSS is the key problem to maximize benefit of this integration.The main objective of this paper is to solve infrastructure configuration of BSS.The principle challenge of such an objective is to enhance the swapping ability and save corresponding investment and operation cost under uncertainties of PV generation and swapping demand.Consequently this paper mainly concentrates on combining operation optimization with optimal investment strategies for BSS considering multiscenarios PV power generation and swapping demand.A stochastic programming model is developed by using state flow method to express different states of batteries and its objective is to maximize the station’s net profit.The model is formulated as a mixed-integer linear program to guarantee the efficiency and stability of the optimization.Case studies validate the effectiveness of the proposed approach and demonstrate that ignoring the uncertainties of PV generation and swapping demand may lead to an inappropriate batteries,chargers and swapping robots configuration for BSS.

政府规制下新能源车企基础设施建设与消费者行为演化博弈研究

针对新能源汽车消费者日益关心的高效补能问题,文中基于演化博弈理论,构建以消费者、新能源汽车企业、政府为主体的三方博弈矩阵,在政府积极规制和消极规制两种策略下,研究政府对新能源汽车企业的监管力度、惩罚力度和补贴力度等因素对三方策略选择的影响。通过MATLAB对模型结果进行仿真分析,研究表明:政府的监管力度和惩罚力度对消费者的决策影响较大,而新能源汽车企业的策略选择主要受政府的补贴力度和惩罚力度的影响;政府的策略以积极规制为主,适度的监管和惩罚会促进消费者购买车电分离的新能源汽车,也能让新能源汽车企业加快换电站的建设。

北京市出租汽车电气化综合效益比较分析等研究动态

选取来自国际学术期刊的论文,以概述形式对城市交通理论方法、实证分析等学术研究成果进行总结性介绍,旨在增强城市交通业界和学界对国际学术动向和研究热点的关注,促进学术交流。本期共选取3篇论文加以介绍,包含北京市出租汽车电气化综合效益比较分析、中国电动汽车碳排放省际转移研究、基于CGE分析需求侧政策对中国私人交通电气化和低碳化的影响等内容。