Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

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.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Implementation of Machine-to-Machine Solutions to Improve Quality of Service for Electrical Energy Distribution Networks in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of a GENEKO modem that exploits its digital inputs together with a full coverage of certain required auxiliary services so as to generate proper detection signals whenever failure currents occur;which allows incorporating the latest failure detection technology into the system.

基于PEF-PinSVM的含可再生能源并网电力系统电压暂态稳定裕度评估

为精准掌握含可再生能源并网电力系统电压暂态稳定性,提出一种改进弹球损失支持向量机的评估方法。首先,结合系统特征与投影能量函数指标相关理论建立电力系统暂态稳定裕度的原始特征集,随后借助最大相关最小冗余特征选择法,筛选出衡量并网电力系统电压暂态稳定裕度的特征子集,以表征可再生能源并网对电力系统电压暂态稳定裕度的影响;其次,采用分位数方法将系统划分为稳定类与不稳定类,并将电压暂态稳定裕度分类问题转换为在PEF-PinSVM中寻找最优分位数距离问题,减小可再生能源并网波动性与不确定性对系统电压暂态稳定裕度评估的影响;最后,以IEEE-39节点系统进行算例仿真,发现含可再生能源并网电力系统电压暂态稳定裕度评估准确率会伴随可再生能源渗透率的增加而提升。

The Development of Electricity Grid, Smart Grid and Renewable Energy in Taiwan

The grid has played a vital role in the evolution of the electricity market;from traditional to smart grids;from fossil fuel power generated electricity grid connections to the integration of other renewable energy forms such as solar and wind power;the grid has played a key role in each step in Taiwan’s move towards energy transition. This study includes Taiwan’s construction of its transmission and distribution network, the recently passed newly revised version of the Electricity Act with its revisions to its transmission and distribution related content, and policies promoting the smart grid as well as issues that the renewable energy grid has raised in both the technical and legal aspects. Taiwan’s electricity supply system is made up of the northern, central and southern systems. The Transmission and distribution grid have been defined as a common carrier, maintaining state-owned monopoly. The smart grid has 6 main facets to promote, including smart generation and dispatch, smart transmission, smart consumers, smart grid electricity grid industry and the establishment of a smart grid environment. Due to the possible effects of the integration of renewable energy generated electricity, there is a vital need for the regulation of the grid’s management and skills.

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 More Renewable-Friendly Electrical Grid: Thermal Storage Refrigeration for Demand Response in California and Denmark

Energy storage technologies, which enable demand response, are being explored throughout the world as a component of strategies for switching to renewable intermittent energy sources and reducing peak loads. This study examines thermal storage refrigeration (TSR) technology as a case study for the potential value of demand response in California and Denmark. Using technical specifications from a TSR prototype developed at UC Davis and market data from California and Denmark, the analysis examines possible business models for the TSR refrigerators and highlights market characteristics that are important to its adoption. Results suggest that the TSR technology is not a viable option in the current market environment in Denmark, but could payback in less than 6 years in California if a part of a demand response based virtual power plant. In a hypothetical future scenario involving real-time pricing in the retail market, a high degree of price volatility would be needed to make TSR technology appealing to residential consumers. Based on this analysis, an interesting area of future work would focus on the market potential of TSR technology for commercial and industrial applications.

Optimization of Thermal and Electrical Appliance Loads in Residential and Commercial Buildings with Demand Side Monitoring

Energy demand-supply mechanism with the load shape for both residential and commercial buildings in the Province of Ontario and Nigeria is studied with demand side monitoring of energy consumption. Thermal and electrical loads are characterized by certain predictor variables, including the consumers’ behavioural pattern, power ratings of energy appliances and weather conditions. The proposed bottom-up approach is capable of providing low-volume electricity and natural gas consumers, in a fully deregulated energy market, with competitive energy saving advantage, based on corrective monitoring of independent users’ demand loads. Special application of the bottom-up model-based facility characterization of demands for thermal comfort and indoor air qualityin a developing energy sector like Nigeria enables the development of planning tool for the proposed integration of renewable power systems. The developed DSMonitorTM app is capable of deploying an effective smart grid technology tool towards an improved building energy demand-supply balance at the individual end-user level.

Smart Energy Resource Grid in China:What's the Path?

Initiated and approved in 2009,the project of Development Pattern and Implementation Design of Smart Energy Resource Grid in China is now accomplished with the research achievement released to the public and highly valued by authorized organizations such as the Global Smart Grid Federation.In this paper,based on the description of the research achievement,the advantages of the smart energy resource grid in China and the consequential changes are analyzed and discussed,involving the industries of electric power,oil and gas,energy storage,water supply,architecture and transportations etc.

An Overview of the Smart Grid in Great Britain

This paper presents an overview of the current status of the development of the smart grid in Great Britain(GB).The definition,policy and technical drivers,incentive mechanisms,technological focus,and the industry's progress in developing the smart grid are described.In particular,the Low Carbon Networks Fund and Electricity Network Innovation Competition projects,together with the rollout of smart metering,are detailed.A more observable,controllable,automated,and integrated electricity network will be supported by these investments in conjunction with smart meter installation.It is found that the focus has mainly been on distribution networks as well as on real-time flows of information and interaction between suppliers and consumers facilitated by improved information and communications technology,active power flow management,demand management,and energy storage.The learning from the GB smart grid initiatives will provide valuable guidelines for future smart grid development in GB and other countries.

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.

Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

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.

A Prepaid Smart Metering Scheme Based on WiMAX Prepaid Accounting Model

Prepaid energy meters have been widely adopted by utilities in different countries across the world as an innovative solution to the problem of affordability and consumption management. However, the present smart card based systems have some inherent problems like added cost, low availability and lack of security. In the future Smart Grid paradigm, use of smart meters can completely overhaul these prepaid systems by introducing centralized accounting, monitoring and credit-control functions using state-of-the-art telecommunication technologies like WiMAX. In this paper we pro-pose a prepaid smart metering scheme for Smart Grid application based on centralized authentication and charging using the WiMAX prepaid accounting model. We then discuss its specific application to Demand Response and Roam-ing of Electrical Vehicles.

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 mierogrid. 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-attaeks.

An Approach for Cost-Efficient Grid Integration of Distributed Renewable Energy Sources

We describe a specific approach to capacity man a ge ment for distribution grids. Based on simulations, it has been found that by curtailing a maximum of 5% of the yearly energy production on a per-generator basis, distribution grid connection capacity can be doubled. We also present the setting and fi rst results of a fi eld test for validating the approach in a rural distribution grid in northern Germany.