A multi-objective optimization problem for allocating parking lots in a distribution network

This paper presents a multi-objective approach to determine optimal site and size of parking lots, which provide vehicle to grid (V2G) power in distribution system as new type of distributed generations (DGs). In this approach, the reliability of distribution system and power losses along with investment cost are considered in optimization problem. This optimization problem is solved using genetic algorithm (GA) method. Simulation study is carried out on a nine bus test system. The results of simulations show that the economic issue of parking lots installation depends on many factors such as availability of electric vehicles (EVs) as well as the electricity price. Also, it is shown that by taking enough incentive for EVs owners, optimal sitting and sizing of parking lots has economical benefit for distribution system companies. Also, optimal allocation of parking lots can improve the distribution system voltage profile.     

计及电动汽车无功支撑能力的分布式电源与智能停车场联合规划方法

随着大量分布式电源(DG)及电动汽车(EV)接入配电网,为保证电网的高效、清洁、经济和安全运行,必须对二者进行合理的规划。为此,提出一种计及EV无功支撑能力的DG及智能停车场(IPL)联合规划方法。首先,基于有源配电网的基本物理结构,对EV动力电池的无功可调范围进行了推导。进一步考虑风电等间歇性DG出力、常规用电以及EV充电负荷时空分布的不确定性,通过构建发电—负荷场景以综合计及上述不确定性因素的影响。在此基础上,分别以系统投资和运行成本最小作为目标函数,构建了综合考虑DG和IPL选址定容的两阶段优化模型。根据模型特点,采用经典的遗传算法实现问题求解。以33节点配网系统为例,对所提模型的有效性进行验证。仿真结果表明,在配电网投资规划中充分考虑规模化入网EV的无功支撑能力,能够有效改善系统的电能质量,促进可再生能源高效利用,从而带来更好的经济效益。     

智能电网环境下家庭能源管理系统优化调度算法

在智能电网环境下,提出了一种家庭能源管理系统框架和优化调度算法。根据室外温度预测值、可再生能源功率输出预测值、日前电价信号和用户偏好,算法对可调度用电负载、电动汽车、储能系统的运行进行优化调度从而最小化用户用电费用。算法考虑了电动汽车在高电价时段通过V2H(vehicle to home, V2H)功能向负载供电的情形,采用情景分析法处理室外温度和可再生能源功率输出预测的不确定性。通过仿真实验验证了算法性能,结果表明与只对负载或家庭能源管理系统部分组成部件进行优化调度的算法相比,所提算法显著降低了用电费用。     

基于GRASP-PR混合算法的电动汽车有序充电优化

大规模电动汽车(Electric Vehicle,EV)并网充电会对配电网造成过负荷、网损增大、电压越界等影响,为了减小EV充电对电网的冲击,文中构建一种多目标EV充电优化模型,并提出GRASP-PR混合算法进行求解。算例仿真采用IEEE 33节点配电网系统,对比研究所提算法、传统GRASP算法和无序充电的仿真结果,证明基于该混合算法有序充电能达到最小化配电网运营成本、负荷"移峰填谷"和升高节点电压水平的目标,有利于解决EV有序充电问题。     

A combinatorial artificial intelligence real-time solution to the unit commitment problem incorporating V2G

Plug-in hybrid electric vehicles (PHEVs) have been the center of attention in recent years as they can be utilized to set up a bidirectional connection to a power grid for ancillary services procurement. By incorporating Vehicle to Grid (V2G), this paper proposes a real-time solution to a non-convex constrained unit commitment (UC) optimization problem considering V2G parking lots as dispersed generation units. V2G parking lots can be considered as virtual power plants that my decrease dependency to small expensive units in a UC problem. In this paper, firstly a probabilistic attendance model of PHEVs in a parking lot is investigated, while expected number of PHEVs as well as the equivalent generation capacity of the parking lot is obtained using a radial basis neural network. Secondly, a particular UC problem considering V2G parking lot is solved using GA-ANN as a hybrid heuristic method. A real-time estimation of PHEVs number in the V2G parking lot and real-time solution to UC–V2G problem associated with load variation makes this work distinguished, while the proposed method is applied to a standard IEEE 10-unit test system with promising results.     

Analysis of Energy Saving and Environmental Characteristics of Electric Vehicles in Regionally Disaggregated World Energy Model

This paper investigates the impact of an extensive introduction of electric vehicles (EV) and plug‐in hybrid vehicles (PHEV) into the global energy system by 2050. The significant growth of automobile ownership in emerging countries is likely to increase world oil demand and the associated carbon dioxide emissions. In order to address these energy, security, and environmental concerns, the deployment of clean energy vehicles, such as EV and PHEV, is expected to play a crucial role due to their high fuel efficiency. Consequently, we develop both a global energy system model and a world vehicle penetration model, which can explicitly analyze the impact of EV introduction into the seasonal daily electric load curve, with consideration of the specific electricity charging profile through 2050. The simulation results confirm that EV deployment contributes to energy conservation, because oil demand reduction outstrips the growth in electricity demand and the associated fuel input into the power generation mix. Concerning carbon dioxide abatement, the magnitude of the impact relies on the carbon intensity of the power generation mix. If the intensity is low enough to guarantee a carbon mitigation effect due to EV fuel saving, emissions reduction is well assured. It should be noted, however, that in regions with high carbon intensity in the power generation mix, carbon emissions per mileage of EVs is almost equivalent to that of efficient gasoline vehicles such as hybrid vehicles, and the figure for PHEV is slightly higher than for hybrid vehicles. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(4): 20–36, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22373     

Coordinated Electric Vehicle Charging for Commercial Parking Lot with Renewable Energy Sources

Electric vehicles (EVs) are increasing in popularity because of their low operating cost and environmental friendly operation. The anticipated rise in EV usage, along with the increased use of renewable energy sources and smart storage devices for EV charging, presents opportunities as well as challenges. Time-varying electricity pricing and day-ahead power commitment add another dimension to this problem. In this research, we develop coordinated EV charging strategies for renewable energy powered charging stations in parking lots. This work maximizes the profits for parking lot operators by taking advantage of time-varying electricity pricing while satisfying system constraints. We propose a linear programming-based strategy for EV charging, and we specifically derive a centralized linear program that minimizes the charging cost for parking lot operator while satisfying customers’ demand in available time.     

计及生物质能的热电联供系统经济运行优化策略

为提升我国农村地区生物质能利用效率,借助热电联供(CHP)系统高用能效率优势,给出一种计及生物质能的CHP系统经济运行优化策略。在考虑了运行功率及容量约束下,对生物质能燃气发电、光伏发电以及储能电池等的运行成本进行建模分析,将CHP系统运行经济性优化转化为求解最小系统运行成本问题。在优化问题求解过程中,为提升传统粒子群算法求解效率,采用灰狼-粒子群算法降低优化过程陷入局部最优概率,提升优化算法运行速度。仿真结果表明,灰狼-粒子群算法具有更快的算法收敛速度,能够改善计及生物质能的CHP系统用能经济性,有效降低农村地区用能费用,促进生物质能推广应用。     

Improved meta-heuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks

The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts.     

基于微网的电动汽车与电网互动技术

随着电动汽车的渗透率越来越高,其无序充放电行为会给电网的正常运行带来负面影响。电动汽车与电网互动(V2G)是一个很好的解决办法。基于微网的V2G模式被用于电动汽车与电网之间的能量交换,并建立了以微网负荷波动最小、可再生能源利用率最大及车主获得的收益最大为优化目标的多目标优化模型。针对该优化模型的求解,首先提出了变阈值优化算法,并在此基础上进行了改进,提出了充放电速率可调优化算法。优化结果证明了这两种算法能够通过合理的调度来提高可再生能源利用率,改善微网内部电量供应和需求不平衡问题以及增加电动汽车车主的收益。     

铁路综合交通枢纽电动汽车充电桩系统设计

在综合交通枢纽的快速发展和新能源汽车迅速增长的背景下,系统地分析了已建成的综合交通枢纽的功能定位及空间布局,提出综合交通枢纽的新能源电动汽车的充电方案及配电系统计算。从铁路综合枢纽内的公车枢纽、社会公共停车场、出租车停车场等重点区域停放电动汽车的充电需求入手,结合每个停车区域的停车数量或规划的公交路线、停运时间、最大充电时间,针对性地分析了公交车辆、私家车、出租车、社会车辆的充电方式,计算出充电桩数量,提出充电计费方式和配电系统设计方案。研究结果表明,该方案指导了铁路综合交通枢纽内的电动汽车充电桩的系统设计,优化了充电系统供电方案,控制了综合枢纽的建设投资和运营成本。     

基于二进制量子粒子群的含电动汽车主动配电网多目标重构

电动汽车(EV)在配网中的渗透率不断增加,影响到电网的经济性和稳定性。提出了适用于主动配电网(ADN)多目标重构的二进制量子粒子群算法(BQPSO),建立了有功网损、电压偏移指标(VSI)和开关操作次数的多目标优化数学模型,以确定接入分布式电源(DG)和电动汽车(EV)后系统的最佳重构方案,并在修改后的IEEE33节点配电系统中进行计算,通过与不同的算法进行对比,验证了本文方法的实用性和有效性。     

Optimal design of hybrid power generation system and its integration in the distribution network

The inability of conventional energy sources to fully meet the rapidly increasing energy demands in today’s world has led to the growing importance of hybrid power generation systems that incorporate renewable energy sources. This work proposes an optimally designed multi-source standalone hybrid generation system comprising of photovoltaic panels, wind turbine generators, batteries and diesel generator. This design aims at minimizing emissions and cost, expressed in the form of the Net Present Value (NPV) of the system, while simultaneously maximizing its Energy Index of Reliability (EIR). The designed hybrid power generation system is further integrated into the distribution system as a Distributed Generation (DG); this is to optimally improve the performance of the distribution system by minimizing the total losses and the total voltage deviation of the distribution system. The combined cost and emissions incurred due to the energy purchased from the grid and the energy generated by DG are also reduced. For this purpose an improvised Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed taking care of contradicting objectives. The proposed optimization algorithms are implemented using MATLAB for a standard IEEE 69-bus distribution system, using an hour-wise annual data of Spain. The location and size of DGs and the type and number of each generating source of the hybrid system are considered as decision variables. The effectiveness of the proposed optimal design using the improvised MOPSO algorithm is established in comparison with Improved Hybrid Optimization by Genetic Algorithm (i-HOGA) results.     

基于计算几何方法的电动汽车充电站规划

综合分析了影响电动汽车充电站规划的若干因素,建立了电动汽车充电站规划的最大收益模型。根据电动汽车充电特性和出行特征,计算电动汽车充电功率需求期望值,从而得出规划区充电站的容量需求。根据电动汽车的分布特点,通过调节加权伏罗诺伊图的权重,使得服务区域划分更合理,同时保持各充电站负载率的均衡。利用粒子群优化算法的全局寻优能力,结合加权伏罗诺伊图,对充电站进行选址定容和服务区域划分的优化规划。算例分别针对不同电动汽车数量和不同分布方式的情况进行计算,结果验证了所述模型和方法的有效性和可行性。     

考虑电动汽车停泊特性的充电站-电网互动策略研究

研究充电站与电网之间的互动,对改善电动汽车的无序充电行为具有十分重要的意义。提出了一种考虑电动汽车停泊特性的充电站-电网互动策略。首先,基于历史数据,配电网运营商优化分时电价,以减少负荷波动。其次,考虑到电动汽车的停泊特性,充电站根据配电网运营商发布的充放电价格控制电动汽车的充放电功率。最后,配电网运营商通过动态重构策略改变配电网的潮流分布,以提高电压质量并降低线路损耗。基于IEEE-33节点配电网系统进行了仿真,仿真结果表明所提的互动策略可以提高充电站的利益,并能降低配电网的网损。     

Plug-In并联式混合动力汽车实时优化能量管理策略

能量管理策略是混合动力汽车的核心技术之一,其品质直接影响车辆的动力性、经济性和排放性能。首先制定了基于确定性规则的Plug-In并联式混合动力汽车能量管理策略;然后,为了提高车辆的燃油经济性,设计了电池能量观测单元,并对等效燃油消耗最小策略进行改进,提出了适用于Plug-In混合动力汽车的实时优化能量管理策略。研究结果表明,该能量管理策略显著提高了Plug-In并联式混合动力汽车的燃油经济性。     

Multiobjective electric distribution system expansion planning using hybrid energy hub concept

This paper presents a novel approach for optimal electric distribution system expansion planning (OEDSEP) using a hybrid energy hub concept. The proposed method uses an energy hub model to explore the impacts of energy carrier systems on OEDSEP procedure. This algorithm decomposes the OEDSEP problem into three subproblems to achieve an optimal expansion planning of a system in which the investment and operational costs are minimized, while the reliability of the system is maximized. The algorithm was successfully tested in the present research for an urban distribution system.     

插电式混合动力汽车双模糊控制策略及其优化

提出一种免疫遗传算法优化的插电式混合动力汽车双模糊控制策略。采用模糊控制方法,分别以发动机工作区间最优和制动能量回收最大为原则,建立了能量管理驱动控制策略和制动控制策略,并根据两者的耦合关系将它们组合成整体控制策略。通过将免疫算法的多样性选择算子、疫苗注射算子、免疫检测算子引入遗传算法,并对疫苗的选择和注射方式予以改进,构成新的免疫遗传算法,应用该算法对控制策略进行了兼顾油耗和排放的多目标优化。仿真结果表明:所提出的双模糊控制策略能实现汽车能量的合理分配,优化后的油耗和三种排放物的排放分别降低了14.01%、12.27%、11.81%和20.34%,且优化结果明显好于遗传算法的结果。     

V2G模式下基于SaDE-BBO算法的有源配电网优化

为了解决大规模电动汽车入网难以实现个体调度以及集群调度存在“维数灾”的问题，建立基于车辆到电网(vehicle-to-grid，V2G)模式的有源配电网分层分区优化运行模型。其中，上层优化模型对电动汽车集控中心(electric vehicle agent,EVA)进行调度，优化各区域EVA的充放电功率并作为下层优化模型的输入；下层优化模型调整各调压方式。在优化算法方面，提出一种自适应差分进化-生物地理学优化(self-adaptive differential evolution-biogeography-based optimization,SaDE-BBO)算法，并在改进的IEEE 33节点配电系统中进行仿真分析。结果表明：在不同充电控制策略下，V2G模式与各调压方式的协调互动在降低各区域EVA运营成本、平抑负荷波动以及保证有源配电网的安全和经济运行方面优势显著，与其他优化算法相比，SaDE-BBO算法具有更优质的解和更好的收敛性。     

基于广义纳什议价的V2V能量交易研究

为解决大量电动汽车(electric vehicle, EV)接入电网或微电网产生额外负荷冲击的问题，提出了一种基于广义纳什议价(generalized Nash Bargaining, GNB)理论的电动汽车交易模型。在市场中选择将配电网络(distribution network, DN)运营商作为代理，并在配电网络中安装变压器和并联电容器。DN运营商可通过变压器和电容器上的有载分接开关来管理网络上的电压和无功功率(voltage and reactive power, Volt -VAR)。同时，市场中允许两种交易方式，一种是EV直接与DN进行交易，另一种是电动汽车之间以点对点(peer to peer, P2P)的方式进行能源交易。最后，将GNB问题分为两个子问题，即社会福利最大化问题(P1)和能源交易问题(P2)，通过调用遗传算法对P1和P2进行求解。结果表明，加入Volt-VAR后，社会福利得到了增加，参与市场的各个代理也获得更公平的利润分配。所提出的模型促进了电动汽车之间的能量交易，降低了电动汽车无序充电对电网的冲击。