校园信息网的设计和调节收费方案

本文主要利用排队论等方法建立了校园信息网的设计和调节收费问题的数学模型 ,从而给出了一种网络设计和调节收费方案     

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.     

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.     

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.     

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.     

出行量预测不确定下基于灵敏度分析的拥挤收费定价模型

拥挤收费是改善城市交通的有效手段.对拥挤收费定价的研究方法之一,就是针对固定的出行量进行收费设计.由于出行量预测不确定的因素,应针对长期出行量预测不确定的情况,利用灵敏度分析的方法,对拥挤收费定价的问题进行研究.首先研究了出行者出行的广义费用,然后考虑出行者的出行决策及出行量预测不确定的因素,给出一个拥挤收费定价模型,为交通管理者提供理论依据.     

Smart charging profiles for electric vehicles

Electric vehicles (EVs) can help decarbonise the transportation sector, which is responsible for a great share of greenhouse gas emissions. Although different measures have been introduced to foster the penetration of EVs in the society, they have not been deployed at a large scale yet. Electric companies are concerned about the effects of introducing EVs into the grid, especially with a large amount. The charging pattern of EVs is the main factor that determines these effects. Unregulated charging (probably when returning home) would have undesirable consequences (e.g. increase in variable costs, emissions, reduction of reliability) for the system, it is therefore necessary to develop an “intelligent” charging strategy. These characteristics justify the existence of different smart charging profiles. It is also important to assess the effect of using day-ahead management systems instead of pre-set profiles. This document compares different possible strategies for charging EVs and their consequences in the power system. The impact on variable costs, emissions and renewable energy sources integration will be obtained using an operation planning model. The Spanish power system for 2020 is analysed under different EV penetration levels and charging strategies. The results show the benefits of using smart charging profiles instead of an unregulated profile, obtaining large cost reductions and maintaining system reliability levels. Moreover, the benefits of using a day-ahead management system are also evaluated, resulting in a small reduction of system variable cost compared to the use of pre-defined charging profiles.     

Does battery management matter? Performance evaluation and operating policies in a self-climbing robotic warehouse

Our research is motivated by battery management in a new self-climbing robotic (SCR) system. The SCR system fully depends on battery-powered robots for tote movements. Therefore, battery management plays an important role and considerably impacts the system performance. This paper investigates the decision of battery charging technology (fast charging versus slow charging) taking into account the battery degradation, the battery charging policy (priority charging policy and dedicated charging policy), and the optimal number of chargers in the system. The paper also optimizes battery management in the SCR system by establishing semi-open queuing networks (SOQNs). The analytical models are solved by the approximate mean value analysis and are validated by simulation models. We find several interesting managerial insights: (1) In the operational policies, although fast charging can decrease the throughput time, we find a new condition when slow charging outperforms fast charging in robotic warehouses. (2) The priority charging policy is more cost-effective than the dedicated charging policy. (3) We also find a decision tool to determine the optimal number of chargers to satisfy the maximum allowed throughput time with the minimum cost.     

城市规模、充电设施建设与新能源汽车市场培育

作为互补品,充电设施建设是决定新能源汽车产业发展的重要因素。本文从城市规模的视角出发,在历史友好模型的理论框架下,构建厂商决策和消费者决策模型,利用统计数据和资料设置初始参数,模拟新能源汽车产业的演化过程,比较了不同规模城市内充电设施建设对新能源汽车市场培育的差异化作用。研究结果表明:在现有的市场环境下,新能源汽车的保有量呈现出逐年递增的趋势,但与传统能源汽车相比,市场占有率仍然较低,平均市场份额尚不足4%;不同规模城市充电站的利用率呈现梯度分布特征,规模越大的城市充电站的平均利用率越低;加大充电设施的建设力度有利于降低充电耗费的时间成本,提高潜在用户对新能源汽车的购买欲望,从不同规模城市的比较来看,三线城市的充电站建设对新能源汽车市场培育的带动作用更为明显。因此,未来充电设施政策制定可以给予小城市更多支持。     

基于路网的电动汽车快速充电站布局决策研究

大力发展和推广纯电动汽车已成为全世界众多国家的共同选择。但电动汽车有限的续航能力及公共快速充电设施的缺乏,制约着人们的使用和长途出行。本研究针对我国公共快速充电网络建设亟待完善的问题,基于我国主干高速公路网络,对服务途中充电需求的快速充电站科学分布问题建立分布决策模型。研究表明,对续航能力低于200公里的电动汽车,应用最优策略分布的快速充电站数量从50座增加到250座时,可将途中充电需求覆盖率从50%左右提高到90%以上,而对续航能力超过250公里的电动汽车,150座按最优策略分布的快速充电站即能覆盖至少96.49%的途中充电需求。通过对不同续航能力和不同充电站数量约束下共30种情形的分析,本研究不仅能为多情形下充电站分布问题提供最优选址和数量组合决策方案,也为我国充电基础设施的完善及电动汽车产业可持续发展提供有力的理论支持和政策建议。     

On-off scheduling schemes for power-constrained electric vehicle charging

In this paper, we study the problem of establishing a dynamic charging schedule of electric vehicles (EVs) at a charging station, assuming that limited power implies that only a limited number of EVs can charge simultaneously. The only control we assume to be available to the charging station is the ability to (at any given time) turn on or off the power supply to any EV, with this tool we want to develop a charging schedule that will satisfy the energy demands of the EVs in their intended deadlines. We propose two distinct approaches to this problem: a discretized time version, based on a greedy-like algorithm, and a continuous time version, based on linear programming. We compare these two approaches and numerically study the improvement they yield in the efficiency of the charging procedure, when applied to simulated data based on real parking data. Finally, we illustrate the flexibility of the models by sketching several possible extensions.     

Charging scheduling problem of an M-to-N electric vehicle charger

This study investigates a real case of charging scheduling of an electric vehicle charger with multiple ports called M-to-N charger. The charger is designed for a multi-unit dwelling facility and can charge N electric vehicles simultaneously despite the supplied charging capacity being limited to only M electric vehicles. The electric vehicles arrive at the charger randomly and stay for their desired length of time, during which they must be charged as much as possible with minimum electric cost. The scheduling problem considers four objectives: maximizing the total charging amount, minimizing the total charging cost, minimizing the charging completion time, and maximizing the charging balance among the electric vehicles. A mixed-integer linear programming model and a relaxation-based heuristic algorithm are developed. Computational experiment results show that the proposed heuristic algorithm can generate schedules within 8 s for this case study by using an open-source linear programming solver. Compared with the mixed-integer programming algorithm, the proposed heuristic algorithm can provide solutions with less than 7% charging amount gap and 4% price gap. The proposed heuristic algorithm is successfully implemented in a real M-to-N charger.     

Minimizing grid capacity in preemptive electric vehicle charging orchestration: Complexity,exact and heuristic approaches

Unlike refueling an internal combustion engine vehicle, charging electric vehicles is time-consuming and results in higher energy consumption. Hence, charging stations will face several challenges in providing high-quality charging services when the adoption of electric vehicles increases. These charging infrastructures must satisfy charging demands without overloading the power grid. In this work, we investigate the problem of scheduling the charging of electric vehicles to reduce the maximum peak power while satisfying all charging demands. We consider a charging station where the installed chargers deliver a preemptive constant charging power. These chargers can either be identical or non-identical. For both cases, we address two optimization problems. First, we study the problem of finding the minimum number of chargers needed to plug a set of electric vehicles giving different arrival and departure times and required energies. We prove that this problem belongs to the complexity class P, and we provide polynomial-time algorithms. Then, we study the problem of minimizing the power grid capacity. For identical chargers, we prove that the problem is polynomial, whereas it is NP-hard in the case of non-identical chargers. We formulate these problems as a mixed-integer linear programming model for both cases. To obtain near-optimal solutions for the NP-hard problem, we propose a heuristic and an iterated local search metaheuristic. Through computational results, we demonstrate the effectiveness of the proposed approaches in terms of reducing the grid capacity.     

电动公交枢纽直流快速充电模式及充电设施最优配置

发展电动公交能减少燃油消耗和城市污染物排放,对改善城市环境具有十分重要的意义.提出一种能显著减少电动公交运营成本的直流快速充电方式及相应的充电设施的建设模式.对公交枢纽直流快速充电模式下电动公交充电服务排队模型、高峰和平峰期充电设施最优配置模型进行了研究,并通过算例验证了这些模型的有效性.研究成果对我国电动公交的充电设施建设具有借鉴意义.     

充电系统可靠性分析

本文给出了充电系统一次及k次充电可靠性的计算表达式;并利用R软件模拟系统的实际充电过程,验证给出的计算表达式的准确性,为实际应用提供理论依据。     

Feynman-Kac Formula for Left Continuous Additive Functionals and Extended Kato Class Measures

The perturbation of the generator of a Borel right process by a signed measure is investigated, using probabilistic and analytic potential theoretical methods. We establish a Feynman-Kac formula associated with measures charging no polar set and belonging to an extended Kato class. A main tool of this approach is the validity of a Khas’minskii Lemma for Stieltjes exponentials of positive left continuous additive functionals.      

第三方数据平台精准营销收费模式研究

第三方数据平台通过大数据分析技术为商家提供精准营销服务,本文研究了两个竞争商家通过同一个数据平台进行精准营销时,平台收费模式(费率佣金与按销量收费)对商家竞争以及平台与商家利润的影响。研究发现:1)费率佣金模式的绩效依赖于费率参数,在精准度高的情况下,它不能最大化平台与商家的利润。2)与费率佣金相比,按销量收费模式显著提高了商家的利润,并且平台与商家的利润都随费用参数的增加而增加。3)从收费模式选择的角度,精准度越高,按销量收费模式的优势越明显;费率佣金模式适用于垄断情况,而按销量收费模式更适用于竞争情况。4)费率佣金与按销量收费的结合可以实现平台与商家的共赢。研究结论为大数据驱动下的精准营销收费模式选择,以及“大数据平台”的盈利模式创新提供了理论参考。     

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。     

基于排队模型的电动汽车充电桩数量优化设计

通过对电动汽车充电行为的分析,采用顾客带有不耐烦和止步行为的有限容量M/M/C/N排队模型对充电场所充电服务系统进行建模.通过求解模型的平衡方程,获得系统稳态下的队长分布及其它多项性能指标.以系统性能指标为基础,从经济效益与社会效益的角度提出了充电服务系统优化设计的目标函数,并通过数字例子对优化模型进行了说明.分析了模型参数对系统充电桩最优配置数的影响,从中可看出,系统容量、顾客的不耐烦率及顾客的止步概率等参数对充电桩数量的优化设计都会产生不可忽视的影响.     

基于基尼系数的停车换乘拥挤收费定价模型

许多大城市提倡停车换乘的组合出行方式,目的是减少出行车辆,缓解交通拥挤状况.应用双层规划模型对停车换乘拥挤收费进行研究.下层模型采用弹性需求SUE模型,上层模型考虑了交通公平因素.以各出行方式占用的道路资源为公平指标,应用基尼系数对传统上层模型进行改造,构建一个用户盈余尽可能大而基尼系数尽可能小的上层模型,并利用基尼系数控制参数,实现在不同公平要求下的拥挤收费设计.算例表明,基于基尼系数的停车换乘拥挤收费设计,能改善道路的拥挤状况,且兼顾了用户盈余与公平.