电动汽车对交互式智能电网负荷曲线的影响

针对用户通过电网与电动汽车的互动(V2G,vehicle-to-grid)影响电网负荷的问题,建立双向拍卖机制下需求侧响应(Demand Response,DR)模型。通过考虑电动汽车是否参与需求响应进行算例分析,通过对比,最后结果表明,在电动汽车的参与下,通过需求侧响应可以提高电力系统运行的经济性,在一定程度上有利于用电峰值的降低。     

考虑配电网弹性负荷的错峰限电辅助决策方法

针对错峰限电过程的干扰因素较多，提高用电质量的同时节约电能，设计一种考虑配电网弹性负荷的错峰限电辅助决策方法。通过隶属度函数得到一个相对清晰的决策指标，由专家组共同决定错峰限电权重，建立限电分配指标体系，按照各个指标对配电网弹性负荷影响程度推算出各个干扰因素对错峰限电决策的影响程度，动态预测配电网线路负荷，通过弹性建模以及后续的电网优化，建立错峰控制限电的决策模型，将复杂的多目标问题转化成简单的单一目标模型，实现了错峰限电的辅助决策方法设计。实例分析结果表明，所提出的方法有效减少了限电损失，并降低了用户用电峰谷差，完成高峰期负荷向低峰期的转移，实际应用效果明显，满足错峰限电辅助决策方法的设计需求。     

面向智能电网双向互动信息服务的通信组网方案设计

以建设智能电网对于智能用电的需求为出发点,研究了智能用电的关键技术以及智能用电新型服务,针对不同的用户设计了面向智能电网的双向互动信息服务系统以及通信组网方案。采用先进的双向互动通信技术、智能交互终端技术以及智能表计技术,为电网和用户之间的互动提供可靠的信息通道和网络支撑,并对应用层信息互动平台提出建设方案,构建了电网...     

浅谈用电信息采集异常数据消缺管理

用电信息采集系统是智能电网的重要组成部分,是实现与电力用户双向互动、提升用户服务能力、建立智能用电服务体系的技术基础.用电信息采集系统能够为"SG186工程"营销业务提供电力用户实时的用电信息数据,有利于推进公司营销管控标准化建设和公司信息化建设,也为提升电力需求侧管理水平、全面预付费的营销业务策略的实施提供技术支撑,符合国际电网技术发展的方向.     

配电网规划中负荷预测实际问题及措施

随着智能电网和大数据技术的不断发展,传统的配电网负荷预测分析模式已无法满足需求.针对负荷预测基础数据准确性、电力用户侧数据有效性等方面的实际问题,结合云计算技术设计出基于大数据系统的负荷预测技术模型,用随机森林分析方法设计负荷预测实验,确保规划实施后配电网的安全、节能与可靠运行,从而最大限度地解决农村地区电网规划负荷预测难题.     

基于智能电网调度支持的居民用电侧自动需求响应系统

为了优化居民用电侧管理和电力系统总的发电容量,构建基于智能电网调度支持的居民用电侧自动需求响应系统。该方法以先进的智能电网技术为依托,依靠智能电网调度支持平台构建需求响应系统框架,采用价格需求响应方式和激励需求响应方式,实现尖峰电价、尖峰折扣、分时电价、实时电价的控制和市场激励、计划激励。其应用分布式在线学习算法能在用户满意度较低的条件下,降低用电成本,提高用电效益。通过综合分析可知,基于智能电网调度支持的居民用电侧自动需求响应系统可较好地优化发电容量并满足居民用电侧用电需求。     

基于分时电价和用户侧储能的配电网调度优化策略

针对分布式电源接入及负荷波动引起配电网线路损耗增加的问题,文中提出将分时电价机制协同用户侧储能参与配电网的优化运行模型。基于分时电价建立用户负荷响应模型,构建以用户日用电成本、配电网网损最小为目标函数的配电网运行优化模型。采用评价函数法将多目标转化成单目标,并在传统遗传算法中引入模拟退火Metropolis准则对用户侧储能充放电策略寻优。仿真结果证明了文中所提策略能有效降低配电网网损及用户用电成本,所提算法寻优速度较快,收敛性能较好。     

解析配电网调度运行操作管理规范

作为一种综合性的电力系统,配电网调度涉及到多重方面,其中包括:设备选型、城市建设及电网规划。配电网的发展,带动了经济的发展,为广大用电用户带来便捷,根据各地实际情况,不仅满足了广大用电用户的需求,还保障了供电质量,优化了电网运行管理,更加有效地实现了配电网计算机的管理,并达到了最有效的经济效益。     

家庭能效远程监控系统的设计与实现

家庭智能用电是智能电网建设中的热点之一,用户用电从被动参与向主动参与的转变,是电网发展的客观规律,本文充分考虑用户需求,设计了一套基于WEB的家庭能耗管理系统,从通信协议、通信网络、硬件、软件4个方面介绍了家庭能耗管理系统的工作原理和功能实现.家庭能耗管理系统能够实现家电远程控制、家庭内用电信息浏览的功能,系统能满足用户在便捷生活、智能生活和节能生活方面的需求.     

基于微网并网的智能电网用户用电福利研究

考虑需求侧响应的微网并网运行机制能够引导用户合理用电,对研究智能电网用户用电福利具有重要意义。综合光伏、风能发电的不稳定性,提出微网并网运行机制下需求侧响应策略,以微网运行成本最小和需求侧用电效用最大为目标,建立了考虑需求侧响应的微网并网运行福利模型。仿真结果表明所建立的模型可达到用户最大用电福利,且有利于削峰填谷,降低供电压力,提升用户参与建设微网的积极性。     

Adaptive algorithm for optimal real‐time pricing in cognitive radio enabled smart grid network

Integration of multiple communication technologies in a smart grid (SG) enables employing cognitive radio (CR) technology for improving reliability and security with low latency by adaptively and effectively allocating spectral resources. The versatile features of the CR enable the smart meter to select either the unlicensed or the licensed band for transmitting data to the utility company, thus reducing communication outage. Demand response management is regarded as the control unit of the SG that balances the load by regulating the real‐time price that benefits both the utility company and consumers. In this study, joint allocation of the transmission power to the smart meter and consumer's demand is formulated as a two stage multi‐armed bandit game in which the players select their optimal strategies noncooperatively without having any prior information about the media. Furthermore, based on historical rewards of the player, a real‐time pricing adaptation method is proposed. The latter is validated through numerical results.     

论智能电网配用电通信研究

在智能电网体系中,对配电通信网络的研究是重要组成部分.本文正是在国内外最新的研究成果,分析配点用电通信网络的现状、发展前景和未来方向.本文首先对智能电网的配点用电业务进行分析,提到了各自特点提出了智能配电用电业务模型构建出对各种业务的网络模型,详细介绍了高级配电自动化系统、用电负荷管理系统、又能服务网络、检测网络.     

An Optimal Power Scheduling Method Applied in Home Energy Management System Based on Demand Response

In this paper, we first introduce a general architecture of an energy management system in a home area network based on a smart grid. Then, we propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real‐time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost‐effective way possible. In our research, to avoid the high peak‐to‐average ratio (PAR) of power, we combine the real‐time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.     

Bargaining‐Based Smart Grid Pricing Model for Demand Side Management Scheduling

A smart grid is a modernized electrical grid that uses information about the behaviors of suppliers and consumers in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity. In the operation of a smart grid, demand side management (DSM) plays an important role in allowing customers to make informed decisions regarding their energy consumption. In addition, it helps energy providers reduce peak load demand and reshapes the load profile. In this paper, we propose a new DSM scheduling scheme that makes use of the day‐ahead pricing strategy. Based on the Rubinstein–Stahl bargaining model, our pricing strategy allows consumers to make informed decisions regarding their power consumption, while reducing the peak‐to‐average ratio. With a simulation study, it is demonstrated that the proposed scheme can increase the sustainability of a smart grid and reduce overall operational costs.     

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.     

基于云计算的电网预测模型与自适应调度研究

光伏发电系统被视为面向偏远地区与小型孤立社区供电的优秀解决方案,但也存在电力供应不稳定的问题。针对光伏发电的能源管理问题,提出一种基于云计算的智能电网管理方案,在对电力供需两端做出预测的基础上,确定最优的电量调度策略。该方案使用小波递归神经网络模型(Wavelet Recurrent Neural Network,WRNN),使得可以高效且准确地同时对电力供需两端进行预测。此外,云计算体系使得可以在基于大量数据的电力供需预测任务上进行快速和分布式的计算能力支持。     

用于电力数据管理分析的负荷预测与异常检测

构建了一个电力数据管理分析系统,并设计了电力负荷预测算法和异常数据检测算法问题.首先,针对BP神经网络在预测电力负荷存在的因初始权值与阈值设置影响估计精度的问题,提出利用粒子群优化BP神经网络网络参数,提高了预测算法的收敛速度与预测精度;然后,针对电力数据异常检测算法效率较低的问题,提出了基于改进谱聚类的异常数据检测算...     

基于营配调贯通的用电需求侧分析

国网公司已全面实施营配调数据贯通,以此促进公司专业管理和服务水平的全面提升。本文主要叙述实现营销SG186系统、电网GIS平台之间数据贯通的需求侧用电需求研究和优质服务分析,并就重庆公司营配调贯通方式进行了分析和阐述,同时提出了用电侧需求分析的自动化算法,并结合当前信息化建设的实际提出了结合需求侧分析和优质服务的客户定制服务软件,从供电企业的需求侧管理分析和客户关注的用电优质服务两方面提出了未来供电企业的发展方向。     

An Overview to the Concept of Smart Coupling and Battery Management for Grid Connected Photovoltaic Battery System

The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system (RES). Grid integrated photovoltaic (PV) battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems (INES), Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system (PEMS).     

Density Based Fuzzy C Means Clustering to prolong Network Lifetime in Smart Grids

Wireless sensor networks (WSN's) are preferred for industrial applications due to progressive increase of sensor electronics. One such application is deployment of WSN's in smart grids. Smart Grid integrates information and communication techniques with electricity network. Smart grids utilize sophisticated control and monitoring devices for improving the efficiency of the grid. For energy efficient, low cost monitoring and control in smart grid WSN's is treated as a promising technology. Advanced Metering Infrastructure (AMI) is the key technology in the distribution networks of Smart Grid. The AMI is composed of various sensors for metering purpose. The meter data is also useful for the distribution operators to manage the demand response. The network involves smart meters, smart electric gas and water meters along with digital network management appliances for optimizing the electric network with real time data management. The smart sensors are limited in terms of battery, operational power and memory. These sensors communicate with the base station in restricted range. The communication between smart grid nodes and base station (sink) is multi-hop in nature. The communication takes place within limited range of communication so the security concerns that are involved in the network are to be handled by the routing protocols. So as to make the bidirectional communication efficient between the smart sensors and utility an effective routing scheme is required for these energy limited devices to handle the heavy network traffic in smart grids. Here energy efficient routing for WSN's in NAN networks to attain load balancing is proposed through density based Fuzzy C means clustering (DFCM). The obtained simulation results show that DFCM can provide a satisfactory performance for enhancing the network life span.