计及多方利益的“车-路-网”三层充电优化方法

随着电动汽车（EVs）保有量逐年增加,充电需求日益增大,短时间内大规模EVs集中充电不仅会给电网稳定运行带来风险和负担,还会造成路网交通拥堵。为了缓解上述问题,提出一种兼顾配电网、路网和车主三方利益的“车-路-网”三层充电优化方法。通过三层优化模型的相互传递与配合,对配电网的等效负荷方差和有功网损、路网的通畅情况、车主的充电总成本进行协调优化。以杭州市某区域路网为例,结合IEEE-33节点配电网系统进行仿真。通过三层充电优化,系统等效负荷方差降低72.82%,有功网损降低83.41%,车主充电总成本减少8.89%。仿真结果表明,所提优化方法能够合理安排EVs充电,可以在平抑负荷波动和降低有功网损的同时,缓解路网拥堵和降低车主充电总成本。     

基于多目标粒子群的电动汽车优化充电策略

居民区家用电动汽车充电具有较强的集中性,大规模电动汽车充电负荷会对配电网系统造成负荷峰谷差过大等问题。文中提出一种基于多目标粒子群(Multi-Objctive Particle Swarm Optimization, MPSO)算法的用户充电选择控制策略,通过分析预测电动汽车充电负荷建立以系统总负荷方差和调度成本最小为目标函数的多目标优化模型,同时考虑了电动汽车电池及系统功率等约束条件,采用多目标粒子群优化算法求解电动汽车最优起始充电时刻。仿真结果表明,相比居民区内电动汽车无序充电,文中所提电动汽车充电策略能有效降低负荷峰值和调度成本。     

基于改进萤火虫算法的电动汽车有序充电研究

电动汽车的推广取得了明显效果,但其无序充电给电网带来了负面影响。文中建立了考虑时空分布的电动汽车充电模型,提出了电动汽车充电优化调度策略,并改进标准萤火虫算法,通过算例仿真研究了渗透率的影响,减小了负荷的峰谷差,并使求解效果更优。     

一种组网雷达波束资源优化分配模型

针对空面目标组网雷达协同调度的资源优化分配问题,通过分析不同目标的重要性、RCS和跟踪质量对调度资源的差异化需求,以组网系统探测效益为优化目标,构造了一种基于波位调度的动态规划模型。利用改进的拍卖算法对该模型求解各雷达最优的波位分配,在满足不同类型目标对跟踪资源需求的同时,降低了组网系统总体波束资源的消耗。该方法解决了多雷达目标调度中资源分配和冲突消解的协同问题,降低了计算量,提高了时间和能量两种资源的合理分配和有效利用率,实现了组网系统高效的波束资源管理。通过仿真验证,证明了所提模型的正确性和算法的有效性。     

基于需求响应的风电-抽水蓄能系统调度模型

针对大规模风电并网影响电网稳定运行的问题,基于日前用户侧电力负荷预测和实时电价预测,文中提出了一种电网吸纳风能的新模型。以最小化风电—抽水蓄能联合系统发电成本为目标,在目标求解过程中确定最佳自弹性系数和互弹性系数,运用基于实时电价的需求价格来弹性引导电力用户以实现负荷转移。考虑到用户侧电价弹性限制的约束,该方法采用遗传优化算法编程求解。仿真结果表明,需求侧资源主动参与优化调度可降低负荷峰谷差,利于系统稳定运行,并且可以减少发电成本,提高联合系统经济性。     

区域多任务安全隐患排除的机器人调度策略

针对灰狼优化算法易陷入局部最优且单一算法不易解决障碍物空间多机器人隐患搜排的调度问题,提出了一种分步引导式多机器人安全隐患协同排除调度策略。首先引入非线性收敛因子调整策略和静态加权平均权重策略改进灰狼优化算法以避免算法陷入局部最优;随后通过改进的灰狼优化算法先后两次求解遍历顺序,引导机器人规划搜索路径与排除隐患点路径;最后在领航者-跟随者模型的基础上多机器人编队与队形变换避障,逐一到达隐患点位置实现多机器人的调度策略。通过国际通用6个基准函数进行测试,改进的灰狼优化算法在收敛速度、搜索精度及稳定性上均有明显提高,验证了区域多任务安全隐患排除的分步引导式多机器人协同调度策略的有效性。     

基于改进鲸鱼算法虚拟电厂经济优化调度

针对分布式不可控电源接入系统引起的波动问题,文中基于虚拟电厂提出一种考虑市场电价的经济优化调度模型,根据负荷需求,对虚拟电厂中的各分布式发电的功率输出进行优化,以达到经济最优化。将鲸鱼算法应用于所建立的虚拟电厂经济优化调度模型,并对算例结果进行了分析,结果表明,该模型是合理有效的。针对鲸鱼算法在求解过程中存在的收敛速度慢和收敛速度低的问题,在收敛因子方面又对鲸鱼算法进行了进一步的改进,将改进的鲸鱼算法运用到已建好的虚拟电厂经济优化调度模型中,对比两种仿真结果可知,改进算法的收敛速度和精度都得到了提高。     

基于多代理的电动汽车充电站能量管理系统

针对电动汽车用户对充电、调节电网负荷以及最大化充电站运营收益的要求问题,采用UML统一建模语言对电动汽车充电站能量管理系统进行建模,从能量调度角度对电动汽车充电过程进行整体功能分析与策略整合,同时电动汽车充电站能量管理系统基于多代理技术,将充电过程中的各方作为具有适应性的代理,各代理通过交互通讯优化自身决策,简化复杂系统的控制问题,再通过UML面向对象的开发流程建立了一个具有拓展性和综合性的模型框架,为下一步实现系统的软件工程、硬件配置奠定了基础。     

改进协同量子粒子群优化算法在流水车间调度中的应用

作为一类典型的调度问题,流水车间调度是N-P难调度问题,因而引起了众多研究者的兴趣。本文提出了一种改进的协同量子粒子群优化算法,该方法中定义了量子角的表达式,所有的子种群采用优化协同模式以确保算法收敛,采用扰动机制避免算法陷入局部最优,运用了综合学习策略以提高种群的多样性。改进的协同量子粒子群算法应用于流水车间调度,仿真结果验证了改进算法能改善全局收敛能力。     

基于长短期记忆网络的新能源电网电量分时计度方法

针对新能源接入电量不稳定,电量分时计度的负荷曲线优化效果较差的问题,设计一种基于长短期记忆网络的新能源电网电量分时计度方法。设计新能源电网的短期电力负荷预测模型,实施短期电力负荷预测。结合接入新能源的特征与用户需求,利用模糊隶属度函数计算新能源接入下负荷曲线上的谷时段隶属度、峰时段隶属度,通过聚类分析的方式归类峰谷时段。基于长短期记忆网络探索新能源电网电量分时计度中的目标函数、约束条件,构建新能源电网电量分时计度模型,实现新能源电网的电量分时计度。在某地区新能源电网测试设计方法的性能,测试结果表明,该方法在较大需求价格弹性、较小单时段响应系数下能够实现更好的负荷曲线优化效果。     

Power Optimization Strategy Considering Electric Vehicle in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their household appliances (HA) for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system (EMS) and then propose a power optimization strategy based on household load model and electric vehicle (EV) model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price (TOUP), the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.     

Energy Management for a Residential Microgrid Using Wavelet Transform and Fuzzy Control Including a Vehicle-to-Grid System

This paper presents the design and implementation of an energy management system (EMS) with wavelet transform and fuzzy control for a residential micro-grid. The hybrid system in this paper consists of a wind turbine generator, photovoltaic (PV) panels, an electric vehicle (EV), and a super capacitor (SC), which is able to connect or disconnect to the main grid. The control strategy is responsible for compensating the difference between the generated power by the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into a smoothed component and a fast fluctuated component. The command approach used for fuzzy logic rules considers the state of charging (SOC) of EV, renewable production, and the load demand as parameters. Furthermore, the command rules are developed in order to ensure a reliable grid when taking into account the EV battery protection to decide the output power of the EV. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.     

Optimal Scheduling of Utility Electric Vehicle Fleet Offering Ancillary Services

Vehicle‐to‐grid presents a mechanism to meet the key requirements of an electric power system, using electric vehicles (EVs) when they are parked. The most economic ancillary service is that of frequency regulation, which imposes some constraints regarding the period and duration of time the vehicles have to be connected to the grid. The majority of research explores the profitability of the aggregator, while the perspective of the EV fleet owner, in terms of their need for usage of their fleet, remains neglected. In this paper, the optimal allocation of available vehicles on a day‐ahead basis using queuing theory and fuzzy multi‐criteria methodology has been determined. The proposed methodology is illustrated on the daily scheduling of EVs in an electricity distribution company.     

基于可再生能量协作的雾无线接入网络资源分配

针对雾无线接入网络(Fog Radio Access Network,F-RAN)中能耗开销巨大的问题,提出了一种基于能量收集(Energy Harvesting,EH)约束的资源分配算法,从联合模式选择与功率分配两个方面进行了研究.首先建立传输模型和能量采集模型,根据功率约束和电费支出约束建立最优化问题;再使用分枝定界法对通信模式进行选择,利用吞吐量注水法对不同传输模式下的发射功率进行分配.仿真结果表明,提出的基于可再生能量协作的F-RAN的吞吐量和电网能量效率均高于传统F-RAN,具有经济和环境双重效益.     

风电储能系统能量调度策略研究

为了改善风电场发电的稳定性,抑制风电引起的电压波动与闪变,提高含风电电力系统的稳定性问题成为重要的研究内容,本文在简要介绍风电的特点的基础上,针对风电并网带来的电能质量及稳定性等问题,阐述了基于能量调度技术的解决方案,详细介绍了基于模糊理论＂最大-最小＂算法的调度系统控制器和系统其它主要部分的模型及仿真结果。控制器根据负荷用电量预测信息控制储能系统的充放电,不仅能有效抑制并网后电网的电能波动也能优化风电的发电质量。MATLAB仿真结果表明,风电储能系统能量调度策略和控制器是有效的,该系统能够有效减小风电场并网功率的波动。     

混合能源的NOMA网络中最优时延均衡和功率控制算法

针对可再生能源和电能源融合的非正交多址接入(Non-orthogonal Multiple Access, NOMA)网络,考虑动态的业务到达、时变的信道衰落、振荡的能源采集和多种能源消耗,以最小化时间平均的电能源消耗为目标,同时满足有限排队时延、最大发射功率限制、能量守恒、有限能源存储容量等约束条件,构建时延均衡和功率控制的随机优化问题。基于李雅普诺夫(Lyapunov)优化方法,解析最优解的数学特征,提出了一种两用户下行链路中具有多项式级别复杂度的最优功率控制算法,通过队列稳定性来均衡可再生能源的动态采集、消耗和有限存储之间的关系。仿真结果表明,所提算法能够有效地降低电能源消耗和排队时延。     

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.     

大规模储能技术对风电规模化发展举足轻重

大规模储能与大容量风力发电系统的结合是可再生能源发展的必要趋势。借助储能装置来抑制风电系统固有的波动,使风电这种间歇性、波动性很强的可再生能源变得"可控、可调",使电网对这种最接近规模化发展的能源调度变为可能。文中囊括了液流电池储能、钠硫电池储能、锂离子电池储能等几种近期内最有可能规模化发展的储能方式,同时,对超级电容器、超导、压缩空气储能等技术也进行了对比分析。     

家庭微网储能分组能量管理优化策略

在家庭能量管理系统中,可再生能源的发电功率具有不确定性和间断性,成为影响家庭能量优化调度的因素。储能系统在优化过程中过多充放电次数也会增加储能折旧费用。针对上述问题,文中提出一种储能分组能量管理优化策略。根据可再生能源出力不确定部分和确定部分为储能系统配置充电部分和调度部分。首先建立风力发电系统、光伏发电系统和储能系统模型,然后在此基础上搭建以每日用电费用最小为目标的家庭能量管理优化调度模型。最后以上海市一住宅用电为例,通过改进遗传算法对模型求解。仿真算例分析表明所提策略降低用电费用的同时可以减小可再生能源发电不确定性对能量优化调度的影响,具有一定的有效性和参考价值。     

Renewable and Storage Energy Integration for Smart Grid Housing

This paper presents the design,implementation and testing of an embedded system that integrates solar and storage energy resources to smart homes within the smart microgrid.The proposed system provides the required home energy by installing renewable energy and storage devices.It also manages and schedules the power flow during peak and off-peak periods.In addition,a two-way communication protocol is developed to enable the home owners and the utility service provider to improve the energy flow and the consumption efficiency.The system can be an integral part for homes in a smart grid or smart microgrid power networks.A prototype for the proposed system was designed,implemented and tested by using a controlled load bank to simulate a scaled random real house consumption behavior.Three different scenarios were tested and the results and findings are reported.Moreover,data flow security among the home,home owners and utility server is developed to minimize cyber-attacks.