考虑交通流量和总成本的电动汽车充电站规划研究

针对目标区域充电桩规划需求问题,在对区域充电需求分析和充电站数量估算的基础上,利用以同心圆的圆周和圆心为站址的充电站选址方法,构建以俘获的车流量与充电站所有成本之比最大的数学规划模型.在此基础上,根据南昌市某区电动汽车数量,确定所需要的充电站数量为4~9个,并规划出六种方案,然后对每一种方案的规划目标值进行计算和比较,得出第三种方案即建设6个充电站为最优方案.研究结果有利于完善城市的交通网络,提升城市电动汽车的运营能力.     

电动汽车充电设施优化网络布局研究

电动汽车在解决环境污染和能源短缺上扮演着越来越重要的角色为了解决充电站选址定容问题,建立基于Voronoi图方法的充电站选址模型和基于排队论方法的充电桩定容模型,以满足规划区域内的所有充电需求;在此基础上,建立社会总成本最小的优化模型,得到人车桩网最优布局最后实例分析验证规划方法的可行性与合理性,结果表明:充电站为7座时,社会总成本最小,达到923.2万元;充电站位置靠近重心,布局合理,各充电站需配置充电桩数量依次为:14、18、10、19、13、13、13台研究结果有利于完善城市交通系统,为电动汽车管理和充电设施建设提供理论依据.     

基于遗传算法的快慢充充电站综合布局优化研究

随着能源和环境问题的日益加剧,电动汽车产业逐渐兴起.作为其运营所必须的基础配套服务设施,充电站的布局优化对于处在发展初期的电动汽车大规模推广应用具有重要意义.兼顾建设运营方和电动汽车用户方综合利益,以目标区域内建设快速充电站和慢速充电站综合费用最小化为目标,以满足电动汽车用户最大充电需求,保证用户充电便利性为约束条件,综合考虑地理位置、充电需求等因素建立了多等级电动汽车充电设施选址模型,并利用遗传算法求解模型.最后,算例表明所提出的方法和模型对目标区域电动汽车充电站的优化布局具有一定可行性和合理性.     

电动汽车未来发展相关问题研究

对电动汽车未来发展相关问题进行了研究.主要完成的工作有:建立了包括目的充电站和超级充电站分布的优化模型和演化发展模型,并以韩国为例研究充电站的分布及充电站网络的演化.建立电动汽车发展的微分方程模型,以美国、韩国为例研究一个国家电动汽车发展10%、30%、50%和100%的时间表.建立电动汽车和充电站发展模式的分类模型,并对不同国家电动汽车发展模式进行分类.     

电动汽车充电站选址问题研究

研究了电动汽车作为通勤工具情况下的充电站选址问题.首先根据城市通勤道路长度、各条道路上的通勤人员拟使用电动汽车的数量,以及电动汽车的最大续航里程等信息,构建了包含两种边的赋权网络图,进一步将电动汽车充电站选址问题转化为赋权网络图的最大覆盖问题,并建立了以极大化满足用户需求为目标的充电站选址问题整数非线性规划模型,设计了求解模型的启发式算法.最后,通过一个具体算例对模型及算法进行了验证,结果显示,模型和算法是解决电动汽车充电站选址问题有效方法.     

考虑充电设施重入的电动汽车旅行商问题研究

出于减少环境危害和响应相关法规的考虑,物流企业开始逐步将运输车辆转变为电动汽车;而由于电动汽车的续航里程有限,对电动汽车的路径优化也将涉及充电设施。充电设施的“重入”是指电动汽车返回之前已经通过的充电设施进行充能的现象,它的存在需要改变经典旅行商问题模型中的“子回路约束”。本文聚焦于充电设施的“重入”,构建了一个无需预估充电设施重入次数上限的电动汽车旅行商问题模型,并通过引入路径可行性判别方法和增加充电设施选择与重复策略,设计了一种改进蚁群算法对问题进行求解。结果表明:与未考虑重入的模型相比,本文提出的考虑充电设施重入的模型能在电动汽车电池容量较低的情况下求出最优解,同时也能够使充电设施承担多次充电任务,从而在较少充电设施情况下依然能够得到可行的最优路径。     

考虑引力因素与时间满意度的充电站竞争性选址问题研究

充电站有效合理的布局和规划对提高充电用户满意度以及电动汽车的推广都起着重要的作用.在考虑用户满意度的基础上,同时论证了充电站与充电需求之间的相互吸引关系,通过引入引力模型得到充电站与充电需求间的引力模型函数,构建了考虑时间满意度与引力因素的竞争性充电站选址模型.最后,利用MATLAB完成实例仿真的求解,结果表明选址模型与实际情况相符合,为充电站的竞争性选址问题提供了一些参考.     

考虑绕行特征的电动汽车快速充电站选址问题及自适应遗传算法

快速充电站选址是电动汽车运营的重要内容之一。本文考虑电动汽车用户会通过绕行一定距离对车辆进行充电这一特征,建立了一个以电动汽车快速充电站建站成本和旅客整体绕行成本之和最小的双层整数规划模型。本文首先给出了用于生成绕行路径集合的A^*算法,然后设计了一种包含局部迭代搜索的自适应遗传算法对该模型进行求解。为了测试算法性能,通过两个不同规模的算例图与已有求解FPLM问题的遗传算法进行了比较,数值试验部分证明了算法的正确性和有效性。最后引入浙江省的高速路网图,从建站成本和截流量两方面对电池续航里程带来的影响进行了相关的灵敏度分析。     

时间满意逐渐覆盖电动汽车充电站选址及算法

作为电动汽车配套的基础设施,电动汽车充电站的选址对电动汽车的推广有着十分重要的意义.针对电动汽车充电站选址问题,别入逐渐覆盖思想和时间满意度函数,从需求点效用最大化的角度出发,提出了基于时间满意逐渐覆盖电动汽车充电站选址模型,并运用蝙蝠算法通过MATLAB实现.实例的求解比较验证了该模型及算法在电动汽车充电站选址决策中的有效性.     

新能源移动充电车路径优化问题研究

在绿色城市背景下,新能源汽车的数量快速增长,现有公共充电设施的不完善使得移动充电服务应运而生。投入运营成本较高而利润低成为阻碍移动充电业务运营的瓶颈之一,如何通过科学合理的调度提高平台利润成为重要问题。本文研究了移动充电车队的调度和路径优化问题,以平台最大收益为目标,综合考虑顾客软时间窗、移动电池容量以及充电车续航里程等约束,建立数学规划模型;设计了一种最大最小蚁群算法,并通过数值实验验证了模型的合理性和算法的有效性,为移动充电企业运营提供决策参考。     

Locating replenishment stations for electric vehicles: application to Danish traffic data

Environment-friendly electric vehicles have gained popularity and increased attention in recent years. The deployment of a network of recharging stations is essential given their limited travel range. This paper considers the problem of locating electronic replenishment stations for electric vehicles on a traffic network with flow-based demand. The objective is to optimize the network performance, for example to maximize the flow covered by a prefixed number of stations or to minimize the number of stations needed to cover traffic flows. Two integer linear programming formulations are proposed to model the problem. These models are tested on real-life traffic data collected in Denmark.     

基于两阶段求解策略的动态电动车辆路径优化研究

由于政府对新能源汽车的补贴政策和市区对燃油车限行政策的实时,越来越多的物流公司在城市配送中广泛采用电动汽车。然而,电动车续航里程受限,需要在途充电或者换电,同时客户需求的动态性以及充/换电设施的排队等现实因素也应该被考虑。为此,提出了分阶段策略求解动态电动车辆路径优化问题,并建立了两阶段的EVRP模型。其中第一阶段针对静态客户建立了静态EVRP模型,第二阶段在设计了换电站及动态客户插入策略的基础上,建立了动态EVRP模型以路径更新策略。最后,设计改进的CW-TS混合启发式算法来求解静态模型,设计贪婪算法求解动态模型。实验结果表明,模型与算法具有较好的适用性和有效性。     

基于无线通信基站的室内三维定位问题

三维定位问题是现代商用通信网络中对于定位系统存在的一个真正具有技术难度的挑战.根据视距传播环境和非视距传播环境的到达时间的数据集,建立线性误差模型;对于无真实位置的竞赛数据集,定义竞赛数据定位误差评估模型;基于不同的空间场景,提出基于空间单元的定位算法;面对高度误差明显高于平面误差的问题,设计基于高斯加权的误差补偿模型;针对最优定位精度最少基站问题,提出基于贪心策略的基站选择算法;考虑轨迹连续性,设计轨迹准确性验证的10-fold交叉验证方法;基于测量距离有限的真实环境,分析平均"连接度数"与定位精度的关系.实验结果表明,提出的定位算法在有效基站数大于等于5时,能获得较好的定位精度.     

Integrating link-based discrete credit charging scheme into discrete network design problem

In this paper, we present an optimization model for integrating link-based discrete credit charging scheme into the discrete network design problem, to improve the transport performance from the perspectives of both transport network planning and travel demand management. The proposed model is a mixed-integer nonlinear bilevel programming problem, which includes an upper level problem for the transport authority and a lower level problem for the network users. The lower level sub-model is the traffic network user equilibrium (UE) formulation for a given network design strategy determined by the upper level problem. The network user at the lower level tries to minimize his/her own generalized travel cost (including both the travel time and the value of the credit charged for using the link) by choosing his/her route. While the transport authority at the upper level tries to find the optimal number of lanes and credit charging level with their locations to minimize the total system travel time (or maximize the transportation system performance). A genetic algorithm is used to solve the proposed mixed-integer nonlinear bilevel programming problem. Numerical experiments show the efficiency of the proposed model for traffic congestion mitigation, reveal that interaction effects across the tradable credit scheme and the discrete network design problem which amplify their individual effects. Moreover, the integrated model can achieve better performance than the sequential decision problems.     

计及多重阻塞的电动汽车移动储能特性建模

为实现城市交通电力耦合系统在城市道路、充电设施、输电线路阻塞环境下的优化运行,提出了计及多重阻塞的动态交通电力流联合优化方法。首先,基于时空网络模型,提出了计及电动汽车移动、静止、充电、排队模式的队列时空网络模型,构建了适用于电动汽车的车辆调度模型,进而形成动态交通分配模型,以减少交通出行损失。其次,通过优化发电机组、储能等的出力和备用计划,计及城市电网安全、备用约束,构建了安全约束动态经济调度模型,以降低碳排放及发电成本。随后,形成多目标动态优化模型,并将其转换为混合整数凸二次规划问题。最后,在耦合IEEE-30、Sioux Falls系统中验证了所提模型的有效性。     

Stop location design in public transportation networks: covering and accessibility objectives

We consider the location of new stops along the edges of an existing public transportation network. Examples of StopLoc include the location of bus stops along some given bus routes or of railway stations along the tracks in a railway system. In order to evaluate the decision assume that potential customers in given demand facilities are known. Two objectives are proposed. In the first one, we minimize the number of stations such that any of the demand facilities can reach a closest station within a given distance of r. In the second objective, we fix the number of new stations and minimize the sum of the distances between demand facilities and stations. The resulting two problems CovStopLoc and AccessStopLoc are solved by a reduction to a classical set covering and a restricted location problem, respectively. We implement the general ideas in two different environments, the plane, where demand facilities are represented by coordinates, and in networks, where they are nodes of a graph.     

Sparsity-promoting distributed charging control for plug-in electric vehicles over distribution networks

Uncoordinated charging of plug-in electric vehicles brings a new challenge on the operation of a power system as it causes power flow fluctuations and even unacceptable load peaks. To ensure the stability of power network, plug-in charging needs to be scheduled against the base load properly. In this paper, we propose a sparsity-promoting charging control model to address this issue. In the model, the satisfaction of customers is improved through sparsity-promoting charging where the numbers of charging time slots are optimized. Dynamic feeder overload constraints are imposed in the model to avoid any unacceptable load peaks, and thus ensure the network stability. Then, a distributed solution strategy is developed to solve the problem based on the alternating direction method of multipliers (ADMM) since most of power networks are managed typically in a distributed manner. During solving process, Lagrangian duality is used to transform the original problem into an equivalent dual problem, which can be decomposed into a set of homogeneous small-scaled sub-problems. Particularly, each sub-problem either has a closed-form solution or can be solved locally by an accelerated dual gradient method. The global convergence of the proposed algorithm is also established. Finally, numerical simulations are presented to illustrate our proposed method. In contrast to traditional charging models, our sparsity-promoting charging model not only ensures the stability of power network, but also improves the satisfaction of customers.     

Design of optimal utility of wireless rechargeable sensor networks via joint spatiotemporal scheduling

In wireless rechargeable sensor networks, how to optimize energy resources for maximizing the sensor data is a challenging problem. In this paper, mobile charging vehicle scheduling, sensor charging time splitting and rate control with battery capacity constraints are considered together to maximize network utility. However, they are considered independently in exist works even though these problems are interdependent. In order to improve network performance through collaborative optimization of three problems, a joint optimization problem is formulated firstly. Then, a multistage approach is developed to jointly optimize the three subproblems iteratively. Furthermore, an accelerated distributed algorithm is integrated to improve the convergence speed of rate control. The results of extended experiments demonstrate that proposed approach can obtain higher network utility and charging efficiency compared to other charging scheduling methods.