基于分时电价和多目标优化的家庭电能管理系统的设计与实现

现如今，分时电价的盛行改变了原来的用电模式，不仅要保证用电费用降低，更要保证用户用电的舒适度。因此提出了基于分时电价和多目标优化的家庭电能管理系统。首先，将常用的家用设备按照家用设备的用电特性进行了分类。然后，家庭电能管理系统优化调度模型的建立以家庭用电成本的优化、用户舒适度的提升和碳排放量的追踪为目标。最后，对家庭电能管理系统模型使用改进的粒子群算法进行优化验证。通过使用Matlab进行仿真实验，验证该方法在降低用电成本、提高用户舒适度和追踪碳排放量方面有显著的优化效果。     

基于电气介数的电网故障无功规划选址与定容

无功补偿对电网减少线路损耗、提高故障应对能力和稳定性等有着显著作用.为此,基于电网拓扑电气介数模型,针对有限经济约束的电网输电线路N-1故障,考虑电网系统无功补偿的经济性约束和潮流方程电气约束,建立包括最小发电费用、最大网损降幅和最优裕度提升为目标函数的优化模型,求解输电线路N-1故障情况下的无功补偿策略的最优选址定容.最后,考虑到构建的混合整数非线性模型的复杂性与难以凸化松弛,通过改进的精英策略的自适应遗传算法求解最优潮流问题,并通过IEEE-14节点标准测试系统进行算法验证,从而表明所提出算法的有效性以及策略的可行性.     

集群服务器在混合请求下的负载均衡优化算法

针对Web集群服务器在处理分配混合页面访问任务时开环负载均衡算法导致局部服务器负载偏高而降低系统效率的问题,建立了一类基于动态调节的闭环负载分配策略。根据服务器处理Web访问页面类型的特点,建立静态页面与动态页面混合处理的负载量模型,形成处理不同服务请求与负载均衡的内在动态映射关系,以此优化静态页面缓存与调用方式,并基于服务器负载率动态预测和均衡指标,采用负载率偏差最小的任务权重最优分配模型,确定服务器集群的最优任务权重,实现了服务器集群处理混合页面访问的负载均衡分配策略和算法。仿真结果验证了负载均衡算法的正确性和有效性,可以达到更好的负载均衡效果。     

一种费用敏感型家电能量优化动态调度算法

本文提出了一种应用于家庭能量管理系统（Home Energy Management System,简称HEMS）中的能量优化动态调度算法。该算法在满足用户满意度的情况下,最大可能的节省用户用电费用。本文提出的费用敏感家庭能量动态管理（expense-aware Home Energy Dynamic Management, eHEDM）算法,选择家庭中耗能较大的四种电器,综合考虑用户用电的满意度和用电费用节省,以及家庭中总能量消耗的限制来调度家用电器。仿真结果验证了本文提出的eHEDM算法可以在满足用户满意度的条件下,节省较多的用电费用。     

可控家用电器负荷优化模型及用电策略研究

在家庭智能用电系统下,以经济性和舒适性为目标,构建了电动汽车、空调、热水器的优化用电模型。并使用基于Q学习的粒子群算法求解优化模型,阐述家用电器的智能用电策略。以空调负荷为例,采用优化模型和算法后,经仿真实验,满足温度控制要求,且费用最少,收敛速度快,有效减少了空调负荷的用电量,削减电费的同时又保证用户的舒适度。     

家庭能量管理系统多目标能量调度优化策略

为实现家庭能量管理系统的经济运行、提高用户能量收益和减少用户用能成本,提出了一种基于实时电价和NSGA-Ⅱ算法的家庭能量管理系统多目标能量调度优化策略。设计了家庭能量管理系统的基本架构,构建了家庭用电负荷模型、光伏发电模型、蓄电池模型和两阶段家庭能量调度优化模型,确定了优化决策变量、约束条件以及用户能量收益、光伏消纳率和用户满意度等优化目标,并采用所提调度优化策略对所构建两阶段能量调度优化模型进行了求解。基于MATLAB平台的仿真结果表明,所提出的调度优化策略能完成家庭能量管理系统能量的多目标优化,实现了系统中源、储、荷的协调配合和经济运行。     

一种基于实时电价的HEMS家电最优调度方法

提出了一种应用于家庭能量管理系统(HEMS)的基于实时电价的家电最优调度模型。该模型采用线性整数规划方法建模,分别以用电费用最省、用电费用和满意度兼顾、二氧化碳排放最少为优化目标,以用户指定的运行时间要求以及电力公司的需求响应为约束条件。该模型将家庭中可以进行调度的可中断运行电器和不可中断运行电器,以及不可调度而必须运行的电器统一考虑,同时考虑使用家庭光伏(photovoltaic,PV)发电系统并允许剩余电力上网出售。提出的调度模型不仅考虑了家庭用电的各种实际情况,而且考虑了将来电网中可再生新能源的使用以及需求响应等新技术的应用,具有重要的实际意义。最后通过仿真实验验证了提出方法的有效性。     

家庭智能用电管理系统用电优化算法研究

为降低居民用户用电费用和提高用电效率,考虑分时电价价格激励的作用,对家庭智能用电进行了研究。构建了由智能家用电器、传统家用电器、电动汽车及其充放电设备、智能开关/插座、智能交互终端、智能手机、分布式能源和智能电表等组成的家庭智能用电管理系统。以一般家庭常用家用电器为优化对象,以用户用电费用最低为优化目标,采用遗传算法,根据用户设定的各家用电器的工作时间范围,提出了基于分时电价的家庭智能用电管理系统智能用电优化算法。以兼顾用户用电费用和负荷消耗功率均匀分布为优化目标,设定功率阈值,引入惩罚机制,提出了基于分时电价的改进智能用电优化算法。仿真结果表明,所提出的智能用电优化算法可为用户最大限度节省用电费用;所提出的改进智能用电优化算法可为用户适当节省用电费用,并可避免引入新的用电高峰。因此,该智能用电优化算法能实现既定优化目标,是有效、可行的。用户可根据实际需要确定优化目标,选用相应算法。     

考虑缺货的模糊随机缺陷生产系统

针对不可靠的生产过程,研究了生产故障时间为模糊随机变量且允许缺货的缺陷生产系统.建立含缺货费和模糊随机重修费的经济生产批量模型.基于可信性理论,建立其期望费用模型,揭示了费用函数的性质,并证明了使费用最小的最优生产时间的存在性和唯一性,从而确定了最优生产时间的上下界.基于此,设计了最优生产时间的二分法求解过程.最后通过算例验证了所提出模型的有效性,并分析了缺货费用、重修费用和缺陷产品比例对最优生产策略的影响.     

一种改进的HEMS家电最优调度方法

在现有的家庭能量管理系统(Home Energy Management System,简称HEMS)的基础上增加分布式储能模块组成新的HEMS,并在此基础上提出了一种改进的基于0-1线性整数规划方法的家电最优调度模型。通过此调度模型,用户可以根据各自需求分别实现用电费用最省、用电费用最省同时兼顾满意度,或者二氧化碳排放最小的目标。该调度模型无论是在目标函数还是在约束条件上都采用线性化表示的方法,在使用极短的调度时间的同时能够保证调度结果是最优结果。最后通过仿真实验验证了提出方法的有效性以及验证了所提方法能够很好的应对电力公司的削峰填谷要求,具有重要的实际应用价值。实验结果表明,所提方法能够比以往相关研究取得更好地节约费用、减少二氧化碳排放的效果。     

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power (CCHP) with storage systems. Initially, the initiative optimization operation strategy of CCHP system in the cooling season, the heating season and the transition season was formulated. The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency, minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy. Furthermore, the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm. Ultimately, the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution(TOPSIS) method. A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method. The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method. The CCHP system has achieved better energy efficiency, environmental protection and economic benefits.      

遗传算法的阶梯电价用电需求管理方案

用电需求管理是智能电网中的重要部分,能让消费者根据自己的用电量做出合适的决策,帮助供电者减少峰值负载,让负载的时空分布更为均衡,从而增加智能电网的可持续性,并减少运营成本和碳排放量。一种基于负载转移技术的用电需求管理方案可以满足对大量、多种设备的调节需求。用电需求管理的解决方案使用改进的遗传算法,并引入了一个新的算子,模拟测试的结果显示通过改进的算法获得的方案节省了可观的成本,并且在使用阶梯式电价的情况下,减少了智能电网的峰值负载。     

需求侧管理下智能家庭用电多目标优化控制

针对家庭内附加型负载进行需求侧管理,缓解高峰时刻电网压力,提出一种智能电网环境的家庭用电控制系统。设计了智能控制器,可以获取用户家庭负荷信息并为用户提供分时电价计量,同时便于供电侧直接进行需求侧控制。提出了多目标蜻蜓算法,针对降低负载功率和减少需求响应延时时间两个目标进行求解,其迭代速度快,满足即时响应的需求。500个家庭的实验结果显示,家庭用电控制系统合理,降低了用户用电费用;算法计算速度快,响应时间延时少,有效缓解了高峰时刻的电网负荷。     

考虑碳排放的物流网络优化模型研究

考虑碳排放的物流网络优化问题, 分两阶段建立考虑碳排放的配送中心选择与需求匹配模型和碳排放成本最小的配送路径优化模型, 通过两阶段模型的配合使用, 解决了物流网络中配送中心选址、不同物流节点间的需求匹配以及配送车辆路径优化等多种决策问题。对“考虑碳排放”与“不考虑碳排放”情况进行Lingo软件的数值分析, 结果表明：考虑碳排放的物流网络综合物流成本明显降低, 比不考虑碳排放的传统方法更具现实意义。     

Application of Hyper-Spherical Search algorithm for optimal energy resources dispatch in residential microgrids

This paper presents a residential hybrid thermal/electrical grid-connected home energy system (HES), including a fuel-cell with combined heat and power and a battery-based energy storage system. The minimum operation cost of this integrated energy system is achieved by proper scheduling of different energy resources, found by applying a new powerful optimization algorithm, the Hyper-Spherical Search (HSS) algorithm, to the system's scheduling problem. This is the first time that HSS is used to face the energy resource dispatch problem. The HSS has been only tested in mathematical problems in the previous study. The optimization procedure generates an efficient look-up table in which the powers generated by different energy resources are determined for all time intervals. The effect of different electricity tariffs for purchasing electricity from the main grid on the operation costs of the system is investigated. Moreover, a battery is properly dispatched in the energy system to decrease the operation costs. A real load demand is used in the simulation. The results of HSS are compared with the harmony search algorithm and the colonial competitive algorithm to show the power and effectiveness of HSS to find the optimal dispatch strategy of energy resources for the first time. This is the first time that HSS is compared with CCA. The results of this paper are expected to contribute to home energy systems and real projects.     

基于DRL的综合能源系统优化运行研究

发展以电网为核心,电、热、气多能互补、协同供能的综合能源系统是落实“双碳”的重要手段,但是电-热-气联合运行的综合能源系统存在的经济性问题和稳定性问题有待解决。本文致力于采用机器学习算法在兼顾运行稳定性的情况下解决电-热-气联合运行系统的经济性问题。首先,本文对包含储能和电转气装置的综合能源系统进行建模,结合优化运行问题优化目标-约束条件的一般框架,在约束条件中考虑功率平衡、各机组出力限制、爬坡率限制和容量限制因素;然后,本文设计了基于DRL的电-热-气联合系统优化运行问题求解策略,算法结合了强化学习策略选择的优势和深度学习环境模拟的优势,在算法设计中详细考虑动作空间、回报函数、状态空间、DRL算法、DRL网络五大模块;最后,本文设计了4个算例,结合电-热-气联合系统典型日运行条件,验证了采用电-热-气联合运行供能模式可以有效实现多能互补降低用能成本,并且本文设计的DRL方法可以有效求解电-热-气联合系统的优化运行问题。     

A two-time-scale load balancing framework for minimizing electricity bills of Internet Data Centers

Internet Data Center (IDC) is one of important emerging cyber-physical systems. To guarantee the quality of service for their worldwide users, large Internet service providers usually operate multiple geographically distributed IDCs. The enormous power consumption of these data centers may lead to both huge electricity bills and considerable carbon emissions. To mitigate these problems, on-site renewable energy plants are emerging in recent years. Since the renewable energy is intermittent, greening geographical load balancing (GGLB for short) has been proposed to reduce both the electricity bills and carbon emissions by following the renewables. However, GGLB is not able to well deal with the wildly fluctuating wind power when applied into IDCs with on-site wind energy plants. It may either fail to minimize the total electricity bills or incur the costly frequent on–off switching of servers. In order to minimize the total electricity bills of geographically distributed IDCs with on-site wind energy plants, we formulate the total electricity bills minimization problem and propose a novel two-time-scale load balancing framework TLB to solve it. First, TLB models the runtime cooling efficiency for each IDC. Then it predicts the future fine-grained (e.g., 10-min) on-site wind power output at the beginning of each scheduling period (e.g., an hour). After that, TLB transforms the primal optimization problem into a typical mixed-integer linear programming problem and solves it to finally obtain the optimal scheduling policy including the open server number as well as the request routing policy. It is worth noting that the open server number of each IDC will remain the same during each scheduling period. As an application instance of TLB, we also design a two-time-scale load balancing algorithm TLB-ARMA for our experimental scenario. Evaluation results based on real-life traces show that TLB-ARMA is able to reduce the total electricity bills by as much as 12.58 % compared with the hourly executed GGLB without incurring the costly repeated on–off switching of servers.     

一种智慧能源及碳排放监测管理云平台系统方案研究与应用

基于新一代信息技术研究智慧城市系统中的实际应用,设计了一种智慧能源及碳排放监测管理云平台系统方案并实施一期工程,深入分析了政策层面的相关要求和智慧能源领域的应用需求,并分析了方案的技术架构和建设内容,实现了系统与第三方产品数据的无缝连接,提供符合信息化发展的各项数据服务,基本建成区一级统一的能耗监测与管理中心,实现对能耗企事业单位的用电量等能耗进行监测,通过用能支路进行计量,实现对能耗的在线监测和动态分析。通过对接全局信息化优化决策体系,进一步提升了运行管理的信息化水平,实现优化集约的运行管理及设备管理,系统的实时响应速度、抗干扰能力、适用环境、利用率、安全性等均达到领先水平,充分展示了所取得的阶段性成果和社会效益。     

实时电价下用户侧电力需求响应模型优化策略及数字仿真谈竹奎1,汪元芹2,赵菁2,刘斌1,刘敏2

为解决智能电网发展中用户参与电力市场运营的响应积极性以及用户收益最大化问题,本文在经济学原理基础上,引用需求价格弹性系数表征用户的用电量随电价的变化情况,建立实时电价下的用户负荷调节能力模型,根据该模型,进一步研究了基于实时电价的用户侧电力需求响应模型优化策略,考虑用户在不同响应场景和不同负荷调节潜力下的需求响应。解决供电与用电间的电力供需不平衡问题,实现用户积极响应及其利益最大化,并提高系统稳定性与安全性。以某地需求响应系统为例,对进入现货市场交易的用户进行数字仿真,通过算例分析表明该模型能有效改善用电负荷曲线,减小用户购电成本,验证了基于实时电价下的电力需求响应优化策略的优化效果。     

Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming

The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming (ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First, the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions. Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.