A new method of electric energy metering considering harmonic responsibility

Aiming at the shortcomings of the existing electric energy metering method,combining with the harmonic responsibility analysis model based on the reference impedance method and the idea of apparent power decomposition in IEEE Std 1459-2010 standard,two new metering indicators—billing active power and billing power factor are defined.A new electric energy metering method is proposed and its specific implementation steps are given.The simulation model is built in Matlab/Simulink,and three different examples are set up.Using the simulation data,the various metering indicators need to be examined by the existing electric energy metering method and the new electric energy metering method are calculated.The calculation results show that the new electric energy metering method not only overcomes the shortcomings of the existing electric energy metering method,but also is very easy to be popularized and applied.

A Novel Bidirectional Interaction Model and Electric Energy Measuring Scheme of EVs for V2G with Distorted Power Loads

With the increasing demand for petroleum resources and environmental issues,new energy electric vehicles are increasingly being used.However,the large number of electric vehicles connected to the grid has brought new challenges to the operation of the grid.Firstly,A novel bidirectional interaction model is established based on modulation theory with nonlinear loads.Then,the electric energy measuring scheme of EVs for V2G is derived under the conditions of distorted power loads.The scheme is composed of fundamental electric energy,fundamental-distorted electric energy,distorted-fundamental electric energy and distorted electric energy.And the characteristics of each electric energy are analyzed.Finally,the correctness of the model and energy measurement method is verified by three simulation cases:the impact signals,the fluctuating signals,and the harmonic signals.

Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

Research on the Recognition Method of Electric Energy Meter Lead Title based on the Fuzzy Image Processing

This article puts forward an automatic recognition algorithm of electric energy meter lead seals： firstly, the image will be histogram equalization, smoothing, binaryzation pretreatment, then according to the image characteristics of text changes, the system can quickly and accurately segment image from complex background, finally the system extract different dimension and the feature of English and Arabia using digital projection transform coefficient method and to identify the corresponding number by BP neural network, solves the problem of automatic recognition of electric energy meter lead sealing.

Technologies Behind the Smart Grid and Internet of Things: A System Survey

Electric smart grids enable a bidirectional flow of electricity and information among power system assets.For proper monitoring and con-trolling of power quality,reliability,scalability and flexibility,there is a need for an environmentally friendly system that is transparent,sustainable,cost-saving,energy-efficient,agile and secure.This paper provides an overview of the emerging technologies behind smart grids and the internet of things.The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply.In addition,the paper sorts out challenges in the traditional or legacy electricity grid,power generation,transmission,distribution,and revenue management challenges such as reduc-ing aggregate technical and commercial loss by reforming the existing manual or semi-automatic techniques to fully smart or automatic systems.This article represents a concise review of research works in creating components of the smart grid like smart metering infrastructure for postpaid as well as in prepaid mode,internal structure comparison of advanced metering methods in present scenarios,and communication systems.

Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.

An Application of Decision Trees Algorithm to Project Hourly Electricity Spot Price as Support for Decision Making on Electricity Trading in Brazil

Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.

Design of PLC Centralized Meter Reading System

With the increasing development of urban housing construction and the rapid expansion of the number of inhabitants and independent electric energy meter, a variety of tariff systems are launched, meter reading metering becomes increasingly complex, and the traditional manual meter reading is already hard to adapt to new changes. This thesis studies the design of a centralized meter reading system. This program applies PLC technology for information transfer, which guarantees the stability of system carrier communication. This system is composed of electric energy meter, collector, concentrator and other hardware, as well as communication and management software of collection copies system. It is characterized by functions of power data collection, centralization, remote transmission, distance control, electricity analysis, line loss calculation, integrated query, and time-sharing billing. Its promotion and application will necessarily provide important technical means for power supply, property and urban utilities to improve their modernized and automatic management level.

Research on Intelligent Power Management System for Student Dormitory based on CAN Bus

This paper analyzes the actual situation of the electricity management about student apartments, and design intelligent energy student housing management system based on CAN bus. The system uses the field level, the underlying management level and upper management level three management system. Field level with a dedicated energy metering chip AD7755 and STM32F103 microcontroller with A/D conversion function as the core, to achieve real-time power measurement; via CAN bus timing or random read live energy data for monitoring electricity consumption of the apartment, investigate abnormal electricity, thus effectively limiting the students to use electrical power to achieve the modernization and automation of power management solutions student apartments.

Profibus—Modbus总线桥在废水处理系统中的应用

本文介绍了Profibus-Modbus总线桥在废水处理上的应用方案、系统组成、网络结构和实现方法。系统通过Profibus-Modbus总线桥,实现了多节点的智能型电量表与远程监控计算机之间的通信,给类似废水监控系统的应用提供参考。

Energy flow problem solution based on state estimation approaches and smart meter data

Accurate electric energy(EE)measurements and billing estimations in a power system necessitate the development of an energy flow distribution model.This paper summarizes the results of investigations on a new problem related to the determination of EE flow in a power system over time intervals ranging from minutes to years.The problem is referred to as the energy flow problem(EFP).Generally,the grid state and topology may fluctuate over time.An attempt to use instantaneous(not integral)power values obtained from telemetry to solve classical electrical engineering equations leads to significant modeling errors,particularly with topology changes.A promoted EFP model may be suitable in the presence of such topological and state changes.Herein,EE flows are determined using state estimation approaches based on direct EE measurement data in Watt-hours(Volt-ampere reactive-hours)provided by electricity meters.The EFP solution is essential for a broad set of applications,including meter data validation,zero unbalance EE billing,and nontechnical EE loss check.

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

对有关电能计量若干问题的思考

文中基于论证和较全面分析,就不同场景下电能计量所关注的截然不同的问题、更好利用大数据去构建电能表检验新方法、动态电能计量研究面临新挑战、关注超谐波电能计量、不宜将电能计量装置与电能表简单划等号,以及测量准确度不同于测量精度等六个问题,阐述了作者的观点和认识,旨在对开展相关研究,以及更清楚理解相关测量方法及概念等起到促进作用。

欧洲汽车动力系统多元化的发展趋势和展望

当前,全球能源结构处在变革时期,低碳和零碳化是包括汽车行业在内的所有经济运行和规划活动中的重中之重。汽车动力系统的能源呈现多元化的发展趋势,始于轻型车的电动化已成为不可逆转的总体方向,而氢时代在到来之前面临各种技术和非技术类等诸多问题的挑战。电动化带来的一个更为广泛应用的新技术是人工智能(AI),从而使得汽车动力系统逐步突破传统设计方法的局限得以适应更加复杂的环境条件并获得高效率。该文介绍欧洲汽车动力系统电动化和多元化的发展趋势,针对欧盟正在考虑中的电力合成燃料等问题并结合动力系统智能化等发展趋势,对欧洲汽车技术领域在2035年前的研发动向作出相关思考以及展望。

基于节能降耗的飞轮储能电力计量技术

飞轮储能电力计量技术,是一种将飞轮储能与电力计量技术相结合的创新型电力管理技术。飞轮储能以其高功率密度、长寿命、快速响应和环保无污染等特点,在电力系统中展现出巨大的应用潜力。而电力计量技术则是电力系统中的重要组成部分,用于准确计量和监测电能的使用情况。将两者结合,飞轮储能电力计量技术旨在提高电力系统的运行效率,优化资源配置,并推动电力行业的可持续发展。本文从节能降耗的角度出发,分析飞轮储能电力计量技术的应用研究情况,并从多个角度阐述未来飞轮储能电力计量技术的应用研究方向,以实现对电力系统的高效、绿色管理。

基于混合铁心的新型电流互感器研究

传统电流互感器铁心饱和会导致其二次电流波形发生畸变,进而可能影响电能计量的精度或引起继电保护设备错误动作,威胁电网的安全稳定运行。针对此类问题,提出了基于混合铁心的新型电流互感器(CCCT)。CCCT主要由混合铁心、二次绕组、二次电阻、磁场传感器与信号处理电路组成,其中混合铁心包含完整的内铁心与带气隙的外铁心,磁场传感器放置在外铁心的气隙中,其输出信号用于补偿发生畸变的二次电流。为验证该结构的有效性,进行了有限元仿真,制作了CCCT样机并进行了正弦交流电流、正弦半波电流、短路电流和直流电流的测量实验。有限元仿真与实验结果表明,CCCT在额定电流下的复合误差小于0.2%,稳态对称短路电流下的复合误差为2.04%,暂态短路电流下的峰值瞬时误差为4.25%,直流条件下输出与输入的拟合优度为0.999 9,既基本保留了传统电流互感器的测量精度,也具有良好的暂态响应特性与抗直流特性,可同时满足相关标准对测量、保护用电流互感器的精度要求,具备在实际工程中应用的潜力。

基于区块链的校园智能停车能源交易平台的设计

提出一个基于区块链的智能校园停车场电动汽车能源交易平台。智能停车场是能够同时支持电动汽车停车和充电服务的智能场所,该系统架构由物理和网络基础设施层组成,区块链技术是促进参与者之间能源交易的有效途径。定义了参与者、资产、交易以及相互之间的智能链接,研究了两种场景,一个场景是电动汽车作为卖方和控制器作为买方之间的能源交易,第二个场景是电动汽车作为买方和控制器作为卖方之间的能源交易。这种平台既为参与者提供了利润,又平衡了大学校园高峰时期的能源负载需求。

基于智能电表的用电信息采集与能源管理研究

用电信息采集系统作为智能用电业务的数据基础和交互的核心枢纽,可实时监测和管理电能质量,自动采集电力用户的用电信息,并监控智能电表用电数据,为辨识故障提供依据。阐述智能电表的特性,分析智能电表用电信息采集的具体应用,通过智能电表能源管理系统的功能设计,量化智能电表计量数据中心需求响应潜力,为提高工作效率和降低电力能源消耗提供基础数据与依据。

考虑电力市场化交易的电碳表计量及实现方法研究

电力间接碳排放是电力用户碳排放的主要来源之一,碳排放流是计算电力间接碳排放的重要方法。目前,基于碳排放流的计量理论尚未考虑电力市场化交易的影响,缺乏相应工程应用的实现方法,参与电力市场化交易的用户难以准确获得其用电量所对应的间接碳排放量。为解决这一难题,该文研究碳排放流理论的分布式电能实现方法,考虑电力市场化交易对交易双方及相关用户碳排放因子的影响,提出含电力市场化交易的电力系统源、网、荷全过程电碳排放量计量方法;并研究存在绿电交易、储能等情况下的电碳排放因子特点及计算方法。基于上述理论,研制了电碳表及其相关系统,并利用电碳表系统对某地电网部分实际拓扑进行分析,验证了所提方法的有效性及电碳表的实用性。

量子功率标准综述:原理、现状及展望

随着国际单位制(International System of Units,SI)量子化变革的稳步推进和广域落地实施,以更加准确、稳定的量子计量标准替代实物计量标准,已成为计量学领域的重点攻关任务之一。量子功率标准对电能的精准计量将发挥关键作用,可有力保障电能贸易结算、电碳交易的公平和公正。首先,该文从交流功率标准和直流功率标准两方面,对当前国内外功率标准研究现状进行了综述。对于交流功率标准,分别阐述了工频交流功率标准和宽频交流功率标准的基本架构、测量原理,指出基于热电变换器的交流功率标准可实现工频和宽频(百kHz)交流功率的高准确度溯源,而国内外已有交流量子功率标准的工作频带一般不超过400Hz,需开展宽频交流量子功率标准研究;对于直流功率标准,国内外还处于初步研究阶段,由于当前尚缺乏对直流电压、电流信号的标准化定义,导致应以何种形式的直流功率信号作为标准被测对象尚不明确,无法为构建直流量子功率标准提供方向性指导。随后,该文对比分析了不同的功率标准,给出了对当前构建功率标准所存在问题的思考,并试对交、直流功率标准的未来发展做出展望:随着宽频量子电压标准技术的发展与进步,有望实现宽频量子功率标准;同时,开展宽频、宽量限电压、电流放大器和比例技术的研究,是实现高准确度交、直流量子功率标准的重要前提。