郭戈(1972-), 男, 教授, 博士生导师, 从事网联车辆协同控制、智能交通系统、共享出行系统优化与控制等研究, E-mail:
徐涛(1996-), 男, 硕士生, 从事智能出行系统的研究, E-mail:
韩英华(1979-), 女, 教授, 博士, 从事智能电网优化调度等研究, E-mail:
赵强(1981-), 男, 讲师, 博士, 从事交通网-电网协同优化的研究, E-mail:
科研团队简介
电动汽车的出现正在引领交通电气化的变革, 电动汽车随机的运输与充电行为将促进交通网与电网的深度耦合. 对此, 结合大数据分析、电车-路网、电车-电网等领域的最新成果, 系统地论述交通电气化进程的现状与进展. 首先, 总结耦合系统的数据预测方法, 归纳各自的基本特征、优势与局限性; 其次, 探讨交通网络中电动汽车的调度问题, 兼顾电动汽车的续航安全与运输服务; 然后, 围绕充电站选址、充/放电负荷管理等方面分析了电气化交通下电网的负载平衡; 最后, 对交通电气化进程中存在的问题与挑战进行总结, 并对其未来发展指明了方向.
The popularity of electric vehicles (EVs) is leading to a revolution in transportation electrification, and the random transport and charging behaviour of electric vehicles will promote the deep coupling between traffic network and power grid. Based on the new achievements such as big data analysis, EV-Road network and EV-Power grid, the progress of traffic electrification is systematically summarized. In this review, the data prediction methods of traffic-power system are summarized firstly, along with their basic features, advantages and limitations. Secondly, the scheduling problem of electric vehicles in traffic network is discussed, giving consideration to the endurance safety and travel service. Then, the grid load balance under electrification traffic is analysed based on the charging station location and charging/discharging load management. Finally, the existing problems and challenges in the process of traffic electrification are summarized, showing the promising development directions for future research.
为了缓解日益严峻的能源和环境问题, 发展电动汽车(electric vehicle, EV)已成为全球共识. 2018年全球电动汽车保有量已达到512万辆, 预计到2030年, 中国将以57%的市场份额稳居世界第一[
与轨道交通及公共交通不同, 电动汽车具有更强的灵活性, 其运行决策不仅受城市复杂交通网络结构和道路车流量等路网信息的约束和影响, 其充电需求与续航能力使电力系统也参与耦合, 庞大的充电需求及不协调的充电行为等恶劣的充电场景将严重冲击电网稳定性. 交通网、电网的响应与反馈进一步加深了与电动汽车的互动[
电网与交通网通过移动的电动汽车实现耦合, 电动汽车的交通属性往往也是研究其充放电行为和电网间相互影响的立足点[
2009年我国正式提出智能电网建设目标, 提高电网与发电侧及需求侧的交互响应能力, 通过电网规划、电网运行调度及用户用电行为分析, 为电动汽车在交通系统中的渗透与充放电管理奠定了基础[
本文旨在综述交通电气化进程中电力系统与交通系统的发展现状及最新成果, 分析交通网-电网耦合系统面临的问题与挑战, 并对其未来发展趋势及研究方向进行展望. 文章结构如下: 第1节介绍与交通网、电网研究领域相关的预测模型及技术; 第2节从电动汽车(下文简称电车)运输调度、电车运输与充电联合调度及路由导航3个方面论述电车-路网整合的成果及问题; 第3节围绕充电设施选址及G2V与V2G三个层面系统综述电车-电网整合过程所涉及的技术与挑战; 第4节为总结与展望. 电网交通网协同优化框架如
电网-交通网协同优化框架
互联网技术以及数据收集、处理及预测技术的发展正在引领大数据时代的到来. 对电网或路网状态的精准把握成为耦合系统优化部署及运行的基础. 对海量数据的理解、对不同预测模型差异的分析、对其适用性及局限性的把握, 是追求预测精度过程中值得探讨的问题.
充电负荷预测是研究电动汽车对电力系统影响的基础. 目前已经提出了许多预测负荷的方法, 根据时间范围可划分为长期负荷预测[
评估方法方面, 排队论[
预测方法主要可分为概率方法和基于人工智能的方法. 概率方法中, 马尔可夫链是一种满足马尔可夫属性的顺序而形成的随机方法[
电动汽车的出现一方面导致了驾驶员行为的变化和交通流的重新分布; 另一方面, 充电站故障扰动在电网-交通网间的传播使得电动汽车行为特性与交通网路况发生突变. 交通流、道路容量及拥塞状况等信息充分反映了交通网在电气化交通中的影响. 电网-交通网的协同优化往往立足于交通网的运行情况[
交通预测的基本模型主要包括自回归模型和时间序列等, 文献[
交通预测技术日益成熟, 但两网耦合系统与单一的交通网络相比更加复杂. 除原有的特征外, 还要充分考虑充电站负载能力及电价等电网侧特征, 深度挖掘用户社群的交通出行与用能行为规律等, 以更贴合耦合系统中交通预测的实际需要. 对于数据质量难以保证及特征数量过多等问题, 缺失数据填充、数据降维以及特征筛选等横向方案有待开展.
电价预测在电力市场调控中发挥着重要作用. 对于供电方或监管者, 电价预测有利于准确把握电价差以获得最大利润或引导充电行为; 对于用电方, 电价预测使动态成本控制成为可能.
电价预测中, 统计方法在规模较小或相对稳定的电力市场中表现良好. 其中, 文献[
同样地, 针对单一模型可能无法有效提取原始非线性、非平稳电价的复杂特征, 混合模型的思想被引入电价预测中, 以在预测前通过数据分解对非线性和非平稳的电价数据进行预处理. 例如: 文献[
交通电气化有望解决交通领域的资源和环境问题, 但提高出行效率、缓解交通拥堵仍有赖于出行模式的变革及高效的车辆调度策略, 尤其在电动汽车背景下车辆的续航能力及充电问题不容忽视, 这为交通网-电网耦合系统的动态任务分配及优化调度带来了新的挑战.
电动汽车、共享出行等新兴技术与理念为解决城市交通难题, 确保城市流动性的可持续发展提供了新的思路. 使用电动汽车提供出行服务的可行性已通过MATSim交通仿真平台[
单向车辆共享在给乘客提供便利的同时也引发了一些问题: 由于出行需求分配不均, 共享车辆不可避免地在某些站点堆积而某些站点无车可用, 需要精准高效的车辆调度方案重新平衡供求关系. 其中一类是基于用户的调度方案[
优化问题立足于系统的静态平衡, 即每个车站的车辆总入站率等于总出站率, 如下式所示:
而排队论模型则分别将车站和道路抽象为单服务器节点和无限服务器节点. 抽象排队网络中车辆的迁移规律和节点的服务时间分别为
与流体模型中乘客排队等待车辆的思想相反, 排队网络则是车辆排队等待乘客的到来, 队列的服务率即为乘客入站率. 然而孤立的车辆共享系统可能会引发需求转移效应, 蚕食其他交通工具并诱导交通拥堵. 文献[
实际上, 车辆的运输需求和充电需求是相互交织的. 文献[
文献[
且决策变量包含车辆载客与再平衡两方面内容, 如下所示:
由式(7)可以看出, 决策变量个数不仅与站点个数有关, 而且与车队规模有关.
文献[
真实的交通网络更为复杂, 相对站点-站点的调度方案, 具体的路径导航往往更符合实际需要, 文献[
电车联网及车辆路由导航
能量最优路径
上述EVRP问题的研究都基于静态的交通网络模型, 忽略了充电价格是电力市场环境中的时变因素, 也忽略了道路拥堵等动态的交通特性. 文献[
目前, 可再生能源发电已成为智能电网重要的组成部分, 但因其间歇性并不能被充分整合利用, 这一难题有望通过实时的电动汽车充电决策得以解决. 假设可再生能源的时变供给规律是已知的, 便可以结合电网潮流及电动汽车行驶特性等约束优化充放电决策, 保证能源利用与出行需求的双赢. 在路径规划过程中, 将V2G服务与可再生能源相结合, 还能保证经济效益最大化[
1) 简单地假设可续航里程与剩余电量线性相关, 而忽略了电车的行驶特性. 简化的线性模型与实际交通网络之间的偏差可能会导致电池过充或半路断电, 因此, 搭建能耗模型对准确评估电车的续航能力至关重要. 车辆能耗不仅与出行距离相关, 而且交通状态及驾驶习惯都将影响电动汽车续航里程. 结合历史出行数据及实时交通状态分析估计出行里程与车辆能耗间的关系不失为一种好方法. 另一方面, 围绕车辆物理特性, 结合车辆速度、功率及载荷等信息搭建能耗模型可以对续航能力进行严格计算. 能耗与各参数间的关系可参见文献[
2) 简单地假设充电设施足够且电车充电无需等待, 然而, 目前的充电站并不足以处理大量的充电需求. EVRP问题通常把车辆当作一个孤立的“点”进行分配而不关注车辆间的相互作用, 比如道路拥塞或充电排队, 因此, 该问题中排队论不再适用于描述车辆随机的充电延时. 可考虑采用马尔可夫决策过程对充电延时进行预测并作为时间窗约束应用于路径优化中. 实际上, 实时感知交通信息是寻找最优路线最为直接的方式, 也是物联网与智慧城市的大势所趋, 这就有赖于连接电动汽车与基础设施组件通信. 文献[
电动汽车巨大的充电需求将导致电网负荷变大, 影响配电网的能源消耗、峰值负荷及电能质量, 考虑交通电气化背景下电网侧的部署与管理对于引导充电负荷、确保电网稳定至关重要. 此外, V2G技术支持电车作为储能设备向电网传输电能, 形成了电车-电网间的双向能源传输结构, 推动电车成为智能电网不可分割的一部分.
续航能力以及司机的里程焦虑是限制电动汽车进一步普及的因素之一, 准确、高效地在交通网络中部署充电站以平衡供需是亟待解决的问题. 目前, 国内外对充电站布局的研究大多基于设施选址问题的优化模型. 目标通常是经济成本、时间成本或充电站的空间覆盖; 侯选位置通常是网络中的交叉路口或现有的基础设施(停车场或加油站等)[
基于节点的方法满足用“点”表示的需求, 电车前往最近的充电点寻求服务. 代表性算法之一是
基于路径的方法满足用“OD对”代表的需求, 假设司机可在行驶路线上的任何站点补充能源而不考虑距离. 流捕获位置算法(FCLM)[
上述研究在捕获交通流时通常没有考虑交通拥塞问题, 文献[
将慢速充电站与快速充电站选址算法进行比较, 前者往往基于区域(多边形), 而后者通常基于交通网络(链路). 充电设施的大规模部署, 尤其是快速充电站对电网容量提出了更高的要求, 需要将交通网络与配电网络相结合来对充电站进行更为可靠的规划[
充电站选址模型对比
literature | demand model | objective | demand representation | station type |
[ |
traffic assignment | maximize profit | network | fast |
[ |
traffic assignment | maximize costs and maximize profit | network | fast |
[ |
traffic assignment | maximize flow captured | network | alternative-fuel |
[ |
traffic assignment | minimize total travel distance and time | network | fast |
[ |
Ad hoc | maximize social welfare | network | fast |
[ |
Ad hoc | minimize total cost | point | slow |
[ |
Ad hoc | minimize charging cost | point | slow |
[ |
regression | maximize charging post usage | point | slow |
[ |
regression | maximize covered demand | point | slow |
确保电力系统负载平衡是电车充电调度及电网能源管理不可忽视的因素. 无充电负荷曲线(无电动汽车情况下的总电力需求)[
时间负荷转移即在高峰时段提高电价, 从而将部分充电负荷转移到非高峰时段, 该策略的一个典型应用场景是避免晚高峰时段电车充电负荷与住宅负荷间的重叠. 通过电价来控制电动汽车的有效性取决于常规充电与高峰电价之间的价格差异, 这种价格敏感性已在激励用户从无控制转向控制充电[
当某一充电站同时服务过多车辆时将极大增加该站点的电力需求, 影响服务效率并对电网组件造成不利影响. 在综合考虑区域电网容量的基础上, 通过控制不同充电站的电价, 可以激励充电负荷均匀分布至不同的区域从而提高充电效率. 文献[
电价的变化将改变司机对充电站点的选择以及充放电策略的规划, 最近从时间和空间的双重角度重塑充电需求的时空负荷转移算法[
值得注意的是, 分时/实时电价的制定除了要关注用户的价格响应外, 运营商买卖电力的电价差也是不可忽略的因素, 这就有赖于多个部门间的互动或精准的电价预测以把握电力市场动向.
V2G是一个与储能技术相关的概念, 即允许车载电池与电网之间进行双向电力流动, 如果管理得当, 电动汽车有望成为未来智能电网的重要组成部分. 文献[
在V2G构架方面, 现有的工作可分为两类: 单聚合器构架[
电车并网的树状层次构架
虽然V2G在实践上是可行的、在经济上是有利的, 但由于供电侧和负载侧都存在不确定问题, 充分发挥V2G的潜力并不容易. 鲁棒优化作为处理不确定问题的有效手段, 可将其引入V2G系统的能源管理中, 将能源及电动汽车荷电状态的随机性建模为不确定预测集, 有序引导电动汽车充放电削峰填谷. 此外, 由运营商统一监管与控制的共享车辆系统为V2G服务的发展提供了一个新的契机.
现阶段, 电网侧及路网侧均围绕电车的集成优化开展了大量研究, 并逐渐形成一种交通网-电网耦合的新趋势. 为了深入探讨交通电气化进程所需的准备工作、研究现状及相关技术, 本文对交通网-电网耦合系统的框架进行了梳理总结, 并沿着耦合系统状态预测、电车-路网优化运行、电车-电网优化运行这一主线, 结合混合预测模型、数据驱动、运输-充电联合调度、分层构架等新兴方法或技术综述了交通网-电网耦合系统的最新研究成果. 作为一门快速发展的、跨学科的研究领域, 大规模电动汽车在耦合系统中的集成还需投入更多的研究与实践, 特别是在数据处理技术、通信技术及更高效的计算求解技术等方面.
1) 电动汽车大规模并入交通网络与电力网络, 将大幅度加深交通流预测及充电负荷预测、电动汽车运输调度及充电调度、G2V及V2G等各个研究领域间的相互耦合, 使各个学科融合为一个有机整体. 同时, 考虑随机充电行为及不确定充电需求, 出行与充放电之间的权衡等波动因素, 开发一种综合的优化框架将成为交通网-电网耦合系统中亟待解决的重要课题.
2) 交通网-电网耦合系统的建模和预测十分困难, 已有的预测方法大多以静态的数据为基础, 采用单一模型或模型的简单组合来提高预测精度, 而忽视了对问题机理的认识和研究. 尽管混合算法具有非平稳性、随机性、非线性等特点, 但目前的混合预测模型主要集中在分解算法、优化算法和预测引擎的简单杂交上, 而忽略了改进独立算法和引入其他新技术的重要性. 特别是在数据预处理、人工智能优化、特征选择等方面仍有需要改进的地方.
3) 随着交通网-电网耦合系统规模的不断增大, 集中式的优化求解架构必然会面临巨大的计算负担, 同时电动汽车数据信息的集中处理也忽略了用户对隐私性方面的需求. 考虑用户侧移动计算能力逐渐增强, 引入边缘计算的思想, 将传统集中式云端优化架构转型为分布式边-云协同优化架构, 能够有效地利用云端和移动侧的存储计算能力, 保护电车用户数据隐私的同时合理迁移计算负担.
4) 私家电动汽车与共享出行系统的优化调度因电车是否可统一管控而存在本质不同. 价格驱动作为一种间接充电控制策略在私家车需求侧管理中发挥了重要作用, 但价格设置往往依赖于对用户价格敏感性的准确把握. 现有的工作通常基于简化的数学函数, 人类行为的复杂性导致缺乏合适的数学模型. 所以, 研究基于人们出行规律的交通建模与规划、车辆部署与调度具有重要意义.
5) 当前用于选址优化的数据集往往来源于静态人口普查或者计算机模拟, 不能准确地建模和表示充电需求空间分布的真实情况, 而且利用动态实时数据代替传统统计数据进行选址优化更加科学合理. 此外, 个人水平的轨迹分析在一定程度上改善了数据集的可靠性, 但有限的小规模私人和公共交通出行数据仍不足以代表整体需求. 随着无线网络以及移动跟踪设备的广泛使用, 采集大规模轨迹数据, 开发一种数据驱动的选址优化方法对提高城市规划的效率是必要且可行的.
6) 拥塞问题是交通网络不可避免的难题. 单纯以最小化调度、能源、时间等成本为目标的优化策略并不能解决拥堵问题, 甚至诱导交通拥堵的产生. 已有的成果利用交通流模型等对外源性拥堵展开了研究, 但通常忽视了调度策略自身导致的内源性拥堵问题及其反馈. 考虑道路容量及网络均衡, 研究动态的任务分配及路径规划方法非常具有挑战性, 也是未来重要的研究课题.
7) 当前交通网-电网耦合系统的研究仍局限于交通网络与电网的独立建模, 侧重于电动汽车行为的优化调控. 至于交通流与电网潮流间的交互在未来还有待进一步探讨, 而且相关研究成果的实际应用有赖于交通管理、能源供应各个部门, 社会企业以及电动汽车用户的积极参与、信息互动与战略协调.
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