Advanced Smart Metering Research Articles

Cosine Windows in Interpolated DFT-based Method for an Accurate High-Frequency Distortion Assessment in Power Systems

The transformation of electrical networks in the context of the new smart grid paradigm unavoidably involves new challenges regarding Power Quality (PQ) disturbances not only for customers but also for all the other involved stakeholders. Among PQ disturbances, waveform distortions have recently gained growing interest due to the massive presence of new technologies in distributed energy resources, in modern loads and in advanced smart metering systems. The presence of these devices determines arduous electromagnetic compatibility problems since the current and voltage waveform distortions in smart grids are characterized by spectral components above the traditional 2 kHz frequency limit, in a range extended up to 150 kHz. In this paper, an interpolated DFT-based (IDFT) method, recently proposed in the relevant literature in the field of signal processing, is properly extended for an accurate and fast assessment of power system waveform distortions in the frequency range from 2 to 150 kHz. Since DFT-based methods can suffer well-known spectral leakage problems, in this paper the IDFT is applied using cosine windows that minimize interference conditions among spectral components and maximise the estimation accuracy of the spectral component amplitude, phase angle and frequency. An optimal number of cosine window terms is also searched to improve the spectral analysis of high-frequency power system waveforms. Numerical applications on synthetic test signals and measured waveforms are carried out to quantify the accuracy and computational efforts of the proposed approach and to select the cosine window terms that better optimize the waveform distortion assessment.

Efficient Data Transfer Mechanism for DLMS/COSEM Enabled Smart Energy Metering Platform

We report our implementation of DLMS/COSEM (Device Language Message Specification/Companion Specification for Energy Metering) enabled advanced smart metering data collection using our in-house developed data-driven multivariate data pruning technique. Aiming at near real-time smart metering data collection, a working model of a smart energy meter with a data pruning subsystem is developed and incorporated with the data pruning and communication module. The implementation of DLMS/COSEM service layer enables standard pull-based data collection from multiple meters that are connected with a commercial head-end system. Our implementation enables to fetch smart meter readings from multiple meters with significantly reduced data footprint. Our empirical results demonstrate that, dynamic data pruning capability reduces the number of samples by nearly 85%. The bandwidth saving increases with the increasing batch size, by accounting for all protocol overheads the endto- end communication bandwidth saving is up to 85%. The DLMS overhead is negligible for larger batch size.

Evaluation of energy consumption and power qua798lity in oil mills using advanced smart meters

One of the main industries in Spain and other Mediterranean countries are oil mills. Although the specific energy consumption of oil mills depends on the capacity and other characteristics, some studies have highlighted the high energy consumption of these industries. In the case of olive mills, previous studies have shown that energy consumption is mainly supported by electricity, with low contribution from gas, diesel or biomass. Considering the importance of reducing the electricity consumption in oil mills, it is necessary to analyse the power consumption in order to take actions focused to reduce the contracted power and the energy required in its operation. Moreover, since these industries may require stable power supply, it is appropriate to analyse the power quality in order to detect possible adverse effects derived from a poor power quality. This paper presents the analysis of data collected by the Circutor MYeBOX 1500 portable meter installed in an olive oil mill. It is shown how monitoring these facilities during several weeks can provide relevant information regarding the energy assessment and policies.

Intelligent Intrusion Detection Scheme for Smart Power-Grid Using Optimized Ensemble Learning on Selected Features

The smart grid has gained a reputation as the advanced paradigm of the power grid. It is a complicated cyber-physical system that combines information and communication technology (ICT) with a traditional grid that can remotely control operations. It provides the medium for exchanging real-time data between the company and users through the advanced metering infrastructure (AMI) and smart meters. However, smart grids have many security and privacy concerns, such as intruding sensitive data, firmware hijacking, and modifying data due to the high reliance on ICT. To protect the power-grid system from these counteracts and for reliable and efficient power distribution, early and accurate identification of these issues needs to be addressed. The intrusion detection in a smart grid system plays an essential role in providing a secure service and transmitting the high priority alert message to the system admin about the detection of adversary attacks. This paper proposes an intelligent intrusion detection scheme to accurately classify various attacks on smart power grid systems. The proposed scheme used the binary grey wolf optimization-based feature selection. It optimized the ensemble classification approach to learn the non-linear, overlapping, and complex electrical grid features taken from publicly available Mississippi State University and Oak Ridge National Laboratory (MSU-ORNL) dataset. The experimental results using a 10-fold cross-validation setup and selected feature subset for two class and three class problems reveal the proposed method's promising performance. Further, the significantly superior performance compared to the existing benchmark methods justified the robustness of the proposed scheme.

Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder

Due to the climate crisis, energy-saving issues and carbon reduction have become the top priority for all countries. Owing to the increasing popularity of advanced metering infrastructure and smart meters, the cost of acquiring data on residential electricity consumption has substantially dropped. This change promotes the analysis of residential electricity consumption, which features both small and complicated consumption behaviors, using machine learning to become an important research topic among various energy saving and carbon reduction measures. The main subtopic of this subject is the identification of abnormal electricity consumption behaviors. At present, anomaly detection is typically realized using models based on low-level features directly collected by sensors and electricity meters. However, due to the significant number of dimensions and a large amount of redundant information in these low-level features, the training efficiency of the model is often low. To overcome this, this study adopts an autoencoder, which is a deep learning technology, to extract the high-level electricity consumption information of residential users to improve the anomaly detection performance of the model. Subsequently, this study trains one-class SVM models for anomaly detection by using the high-level features of five actual residential users to verify the benefits of high-level features.

A configuration method of computing resources for microservice-based edge computing apparatus in smart distribution transformer area

With the development of the Internet of Things technology in the smart grids, terminal devices such as advanced sensors and smart meters present a trend of massive access in distribution network, which poses great challenges to the information perception, analysis and processing ability of distribution automation system. The microservice-based edge computing apparatus is a novel technology in the smart distribution transformer area to improve the adaptability of the distribution automation system. Research on modeling methods and computing resource capacity planning methods for the microservice-based edge computing apparatus has become an urgent and significant research topic in the modern electrical power system. Therefore, this paper proposes a configuration method of computing resources for the microservice-based edge computing apparatus in the smart distribution transformer area. The suitability of the microservice technology and edge computing apparatus is analyzed, and a model of microservice-based edge computing apparatus is proposed. Inspired by power demand and supply in the power system, a computing resource demand and supply model is proposed to represent the computing resource demand of power Internet of Things services and the computing resource supply of the microservice-based edge computing apparatus. Furthermore, a sequential logic-based evolution algorithm is designed to determine the configured capacities of computing resources for the microservice-based edge computing apparatus considering multi-aggregated service flows. Finally, simulation results validate the effectiveness of the proposed method.

Spatial-Temporal Residential Short-Term Load Forecasting via Graph Neural Networks

Electric load forecasting, especially short-term load forecasting, is of significant importance for the safe and efficient operation of power grids. With the wide adoption of advanced smart meters, more attention has been paid to short-term residential load forecasting. Most of the existing load forecasting methods are mainly focused on using temporal information of historical loads, and information of neighboring houses are generally ignored. However, houses in the same or neighboring areas may show similar consumption patterns due to shared conditions such as temperature, holiday impacts. Such information can be very helpful for machine learning based forecasting methods. In this paper, we propose to tackle the short-term residential load forecasting including both the individual load and aggregated load with a graph neural network based forecasting framework. The proposed framework can capture the hidden spatial dependencies of different houses without even any prior knowledge requirement on the geographic information for these houses. The proposed framework is evaluated on data sets of different residential houses from several areas. The experimental results demonstrate that the proposed framework can improve the residential forecasting accuracy by a wide margin compared with the baselines.

Smart Metering and Scheduling of Electrical Loads of Buildings

The article analyzes global trends in the development of smart metering and energy planning in buildings, the development and implementation of intelligent energy monitoring to collect and analyze data on energy consumption for management to improve energy efficiency, it considers the implementation of infrastructure for advanced smart metering smart home using NILM-technology non-intrusive load monitoring. It is shown that equipping buildings of various purposes in a smart city with real-time energy accounting systems with smart meters generates a new approach to improving the energy efficiency of buildings and contributes to the successful implementation of energy service contracts and energy management systems in them.

Experimental Validation and Deployment of Observability Applications for Monitoring of Low-Voltage Distribution Grids.

Future distribution grids will be subjected to fluctuations in voltages and power flows due to the presence of renewable sources with intermittent power generation. The advanced smart metering infrastructure (AMI) enables the distribution system operators (DSOs) to measure and analyze electrical quantities such as voltages, currents and power at each customer connection point. Various smart grid applications can make use of the AMI data either in offline or close to real-time mode to assess the grid voltage conditions and estimate losses in the lines/cables. The outputs of these applications can enable DSOs to take corrective action and make a proper plan for grid upgrades. In this paper, the process of development and deployment of applications for improving the observability of distributions grids is described, which consists of the novel deployment framework that encompasses the proposition of data collection, communication to the servers, data storage, and data visualization. This paper discussed the development of two observability applications for grid monitoring and loss calculation, their validation in a laboratory setup, and their field deployment. A representative distribution grid in Denmark is chosen for the study using an OPAL-RT real-time simulator. The results of the experimental studies show that the proposed applications have high accuracy in estimating grid voltage magnitudes and active energy losses. Further, the field deployment of the applications prove that DSOs can gain insightful information about their grids and use them for planning purposes.

A New Advanced Method for an Accurate Assessment of Harmonic and Supraharmonic Distortion in Power System Waveforms

Waveforms distortion is a pressing concern in Smart Grids where a massive presence of new technologies in distributed energy resources and in advanced smart metering systems is expected. In this context, the increasing diffusion of high switching frequencies static converters and the growing usage of Power Line Communication push for research dealing with the assessment of waveforms with spectral components up to 150 kHz. The analysis of such waveforms is a challenge for researchers due to the contemporaneous presence of a high number of spectral components in the range of low- (up to 2 kHz) and high- (up to 150 kHz) frequencies, with their opposite needs in term of time window length (and frequency resolution). The main idea of this paper is to improve the performances of existing methods by using a joint method of analysis based on a profitable strategy of divide and conquer; the method guarantees the best compromise between accuracy and computational efforts. A Discrete Wavelet Transform initially divides the original waveform to obtain two frequency bands: the wavelet suitability for conducting multi-resolution time-frequency analysis on waveforms in different frequency bands with different frequency resolution is effectively exploited. Then, the sliding-window modified ESPRIT method and the sliding-window Discrete Fourier Transform which uses a Nuttal window are used for the analysis of the low- and high-frequency bands, respectively; the positive characteristics of each method are exploited, minimizing the drawbacks and integrating their behavior so that the whole joint method allows an accurate estimation of each low- and high-frequency spectral component with the required acceptable computational efforts. The proposed method is tested on synthetic and measured waveforms in terms of accuracy and computational efforts. The analysis of the numerical application results clearly reveals that the proposed method improves the performances of existing methods of analysis in the examined cases.

A novel optimization method for urban resilient and fair power distribution preventing critical network states

In decentralized and smart power systems with a high share of distributed renewable energy sources the reliability of power supply is particularly challenged. Therefore, known concepts on maintaining the security of supply have to be rethought and enhanced by the notion of resilience. Power shortages caused by dark doldrums or induced by component failures or by cyber-attacks on the one hand and new extreme power demands due to an increased usage of power consuming technologies on the other hand can lead to critical network states or even blackouts. The impact of failures of critical infrastructures on the urban security of supply in general is measureable by so-called supply indices. These supply indices in combination with efficiency and fairness metrics allow the definition of a composite resilience metric, which we utilize in our novel smart scheduling method. Assuming the existence of advanced metering infrastructures and smart meters, the presented method enables stable and urban resilient grid operation in the face of power scarcity or extreme power demands. Thereby, the new method goes far beyond demand side management, peak shaving techniques or rolling blackouts. Consequently, the proposed composite resilience metric enables the development of new power distribution policies and controls, which consider urban resilience with respect to critical services, power efficiency, and fairness. Based on an Evolutionary Algorithm and Optimal Power Flow, the new smart and urban resilient scheduling method is applied to an extension of the IEEE 33 case: simulation results are presented and it is shown that the new method outperforms known benchmark methods such as rolling blackouts.

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

Double Auction With Hidden User Information: Application to Energy Transaction in Microgrid

In this paper, a general-purpose double auction mechanism that is specifically relevant for energy transactions among competing buyers and sellers, i.e., agents, in a microgrid has been investigated. This auction algorithm is implemented by means of a microgrid controller (MC), and is individually rational, weakly budget balanced, and efficient (social welfare maximizing). Furthermore, the mechanism does not violate any physical grid constraints and does not require agents’ private information. These characteristics of the auction are accomplished by a suitable surrogate objective function which implicitly maximizes the social welfare while the agents place bids in a selfish manner and can sell or procure energy. The sellers of energy are modeled as prosumers that are equipped with photovoltaic panels as well as advanced smart meters and are willing to participate in selling their excess energy due to saturation in their energy consumption utility curves. Buyers of energy are assumed to be conventional consumers with smart meter capabilities to communicate with the MC and place bids to get optimal allocations. Buyers aim to maximize their energy usage utility plus their payment amount whereas sellers maximize their own energy consumption utility plus the reimbursement that they receive for selling their excess generation. Furthermore, the mechanism allows the possibility of sellers’ coalition where a fair redistribution of the energy supply is possible in a competitive market with less demand and more supply.

Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol

An incentive demand response program with two tools is proposed in this paper to help the customers to participate in the program. The first tool is the use of advanced metering infrastructure (AMI) with two-way communication system that carries significant implications for customer service, privacy and consumer protection policies. The proposed demand response program with AMI, based on real time signals, can be an effective method for utilities to manage system peaks by controlling customer loads with renewable sources. Widespread use of program with AMI will be supported by reliable and flexible ECHONET Lite Specification (ELS) communication protocol, as the second tool, that would encourage consumers to participate and reduce their energy use at peak times. ECHONET Lite, next-generation industrial and household level protocol system, is designed to integrate many devices and systems. Developing commercial energy management system (CEMS) based on advanced smart metering and ECHONET Lite protocol can help in getting the balance between the load and resources at the customers’ premises. CEMS can manage appliances and renewable resource devices at the consumers’ premises with the premise that the consumers should manage the power consumption by themselves to balance and also reduce the energy costs. The developed management system is based on a diffusion model for load balancing. The automated model of gaseous diffusion is applied as new load balancing model between the load and sources. The model helps getting an automatic balancing between the demand and resources at the customers’ premises. The proposed demand-response with real-time pricing and hourly pricing offers consumers choice and transparency, and promotes efficiency. The provided scheme provides tools that can assist the customers’ premises to participate in demand response programs.

Joint Load Scheduling and Voltage Regulation in the Distribution System With Renewable Generators

By equipping with the advanced smart meters and two-way communications infrastructure, smart grids, as a key component of future smart cities, are able to improve the energy efficiency and reduce the energy cost through real-time monitoring and customer load scheduling. However, the high penetration of intermittent renewable energy such as solar power may cause frequent overvoltage and undervoltage problems at certain buses, making the load scheduling face new challenges on voltage regulation. In this paper, we investigate the impact of voltage constraints on load scheduling by power flow analysis in a power distribution system with renewable generators. A voltage regulator (VR) is introduced to regulate the voltage of buses in the distribution system and assist load scheduling. To jointly minimize the cost and stabilize the voltages of the distribution system, we propose a grid-customer coordinated load scheduling strategy, which simultaneously determines the tap changes of the VR and scheduling of customer electricity loads in each time slot. Finally, we evaluate the performance of the proposed strategy based on realistic power demand and renewable energy generation datasets. Extensive numerical results demonstrate that the proposed strategy can remarkably reduce the energy cost and stabilize the voltage fluctuation of distribution systems.

Opportunities and challenges of demand response in active distribution networks

In power systems, the installed generation capacity must exceed the annual peak demand, even though some capacity is kept idle most of the time. However, if it is uneconomical or not feasible to augment a sufficient capacity, the demand might exceed the available capacity. This mandates the system operator to shed the load in order to maintain security of the system. With the advent of advanced smart metering infrastructure, communication between system operator and end‐use customers makes it possible to adjust/curtail/shift the demand with respect to the state of the system. The response of the demand commonly termed as demand response (DR) can be attained either by incentive‐based or price‐based. With the help of DR, the renewable energy generation capacity can be increased by tuning the demand to match the variable and unpredictable power from renewable generation. It can also bring other benefits such as peak shaving, hosting capacity enhancement, and generation cost reduction. Furthermore, electric vehicles, heat pumps, and electric water heater can also be used as distributed storage resources to contribute to ancillary services, such as frequency/voltage regulation, peak‐shaving power or help to integrate fluctuating renewable resources. All these DR modes of operation need conventional regulatory frameworks and market design for capitalizing the available resources. Therefore, the objective of the study is to discuss the DR classification and their control strategies, DR role in microgrids and integration of renewable energy resources. Also, highlighted the opportunities and challenges along with the insights for the research scope associated with DR. WIREs Energy Environ 2018, 7:e271. doi: 10.1002/wene.271This article is categorized under: Energy Infrastructure > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure Energy Policy and Planning > Systems and Infrastructure

Distribution automation analysis based on extended load data from AMI systems integration

One of the critical requirements of smart distribution grid is to incorporate self-healing mechanisms which help in effective operation and better management of distribution system. Distribution system has grown in a multi-fold fashion during recent times not only in terms of increased load but also with the addition of advanced infrastructure like communication, smart meters & sensors, automation and information technology. Digitization of distribution grid led to the development of new technologies and solutions like residential micro-grids, electric vehicle charging, demand response, net metering, demand-side management and outage management system for effective load management and control in Low Voltage (LV) distribution networks. At the same time, this has been posing tremendous challenges in effective operation of Medium Voltage (MV) distribution networks due to the lack of real-time and additional information from LV networks. Distribution Automation (DA) applications at MV level has limited access or visibility outside distribution substation and reaching down to the LV network level. In this paper we propose a method of distribution load modelling in DA system based on Advanced Metering Infrastructure (AMI) system feedback which helps in effective operation of DA system along with suitable case study analysis.

Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration

Abstract This paper presents a comprehensive review and assessment of the latest research and advancement of electric vehicles (EVs) interaction with smart grid portraying the future electric power system model. The concept goal of the smart grid along with the future deployment of the EVs puts forward various challenges in terms of electric grid infrastructure, communication and control. Following an intensive review on advanced smart metering and communication infrastructures, the strategy for integrating the EVs into the electric grid is presented. Various EV smart charging technologies are also extensively examined with the perspective of their potential, impacts and limitations under the vehicle-to-grid (V2G) phenomenon. Moreover, the high penetration of renewable energy sources (wind and photovoltaic solar) is soaring up into the power system. However, their intermittent power output poses different challenges on the planning, operation and control of the power system networks. On the other hand, the deployment of EVs in the energy market can compensate for the fluctuations of the electric grid. In this context, a literature review on the integration of the renewable energy and the latest feasible solution using EVs with the insight of the promising research gap to be covered up are investigated. Furthermore, the feasibility of the smart V2G system is thoroughly discussed. In this paper, the EVs interactions with the smart grid as the future energy system model are extensively discussed and research gap is revealed for the possible solutions.

Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy

Buildings are the dominant source of energy consumption and environmental emissions in urban areas. Therefore, the ability to forecast and characterize building energy consumption is vital to implementing urban energy management and efficiency initiatives required to curb emissions. Advances in smart metering technology have enabled researchers to develop “sensor based” approaches to forecast building energy consumption that necessitate less input data than traditional methods. Sensor-based forecasting utilizes machine learning techniques to infer the complex relationships between consumption and influencing variables (e.g., weather, time of day, previous consumption). While sensor-based forecasting has been studied extensively for commercial buildings, there is a paucity of research applying this data-driven approach to the multi-family residential sector. In this paper, we build a sensor-based forecasting model using Support Vector Regression (SVR), a commonly used machine learning technique, and apply it to an empirical data-set from a multi-family residential building in New York City. We expand our study to examine the impact of temporal (i.e., daily, hourly, 10min intervals) and spatial (i.e., whole building, by floor, by unit) granularity have on the predictive power of our single-step model. Results indicate that sensor based forecasting models can be extended to multi-family residential buildings and that the optimal monitoring granularity occurs at the by floor level in hourly intervals. In addition to implications for the development of residential energy forecasting models, our results have practical significance for the deployment and installation of advanced smart metering devices. Ultimately, accurate and cost effective wide-scale energy prediction is a vital step towards next-generation energy efficiency initiatives, which will require not only consideration of the methods, but the scales for which data can be distilled into meaningful information.

Residential Power Load Forecasting

The prepaid electric power metering market is being driven in large part by advancements in and the adoption of Smart Grid technology. Advanced smart meters facilitate the deployment of prepaid systems with smart prepaid meters. A successful program hinges on the ability to accurately predict the amount of energy consumed on a daily basis for each end user. This method of forecasting is called Residential Power Load Forecasting (RPLF). This paper describes the systems engineering (SE) processes and tools that were used to develop a recommended load prediction model for the project sponsor, SmartGridCIS. The basic concept is that power is treated similar to a prepaid telephone in a “pay as you go” fashion. Modeling techniques explored in the analysis of alternatives (AoA) include Fuzzy Logic, Time Series Moving Average, and Artificial Neural Networks (ANN). SE tools such as prioritization and Pugh matrices were used to choose the best-fit model, which ended up being the ANN. Cognitive systems engineering was used in conjunction with the task analysis. Requirements were developed using the commercial tool IBM Rational DOORS®.