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A smart meter is an automation technology that sends real-time power consumption of electric appliances to the outsourced cloud through the aggregator node. An outsourced cloud is used by the Utility providers to release computation and storage overhead. The real-time smart meter data helps in the management of demand and supply in the smart grid. However, the real-time smart meter data exposes the privacy of smart meter customers and inefficient aggregated smart meter data results in unbalanced power management decisions in the smart grid. Therefore, a smart meter data storage (SMDS) model is proposed that aggregates the encrypted smart meter data at the fog node with the property of homomorphic encryption and stores it on the outsourced cloud. Two clouds are used to process the smart meter data and only the utility provider is able to retrieve the actual power consumption of the smart meter. Additionally, a secure query processing model is designed to retrieve the smart meter data on the outsourced cloud. Experimental results show the effectiveness of the proposed work and the feature comparison demonstrates the superiority of the proposed over the existing works. [ABSTRACT FROM AUTHOR]

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device's continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%. [ABSTRACT FROM AUTHOR]

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The transactive energy market (TEM) is a recent development in energy management that enables prosumers to trade directly, promising many environmental and economic benefits. Prosumer trading necessitates sharing information to facilitate transactions. Additionally, many TEMs propose using blockchains to manage auctions and store transactions. These facts introduce privacy concerns: consumption data, trading history, and other identifying information pose risks to users if leaked. Anonymity by trading under a pseudonym is commonly presented as a solution; however, this creates risks for market participants: scammed users will not have recourse, and users with innocent malfunctions may be banned from trading. We propose the Individually Linkable Pseudonymous Trading Scheme (ILPTS), which enables users to trade under a pseudonym, protecting their identity, while a smart contract monitors reputations and can temporarily deanonymize a user, ensuring market integrity. ILPTS was developed in stages. Examination of existing TEM literature was performed to identify desirable features. Analysis of cryptography literature was performed to identify techniques that may confer certain features. It was found through formal analysis that ILPTS adheres to identified design goals, improves upon existing solutions, and resists common attacks against TEMs. Future work includes software simulation and on-device implementation to further verify security and feasibility. [ABSTRACT FROM AUTHOR]

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature. [ABSTRACT FROM AUTHOR]

District heating companies utilize smart meters that generate alarms that indicate faults in their sensors and installations. If these alarms are not tended to, the data cannot be trusted, and the applications that utilize them will not perform properly. Currently, smart meter data are mostly used for billing, and the district heating company is obligated to ensure the data quality. Here, retrospective correction of data is possible using the alarms; however, identification of sensor problems earlier can help improve the data quality. This paper is undertaken in collaboration with a district heating company in which not all of these alarms are tended to. This is due to various barriers and misconceptions. A shift in perspective must happen, both to utilize the current alarms more efficiently and to permit the incorporation of predictive capabilities of alarms to enable smart solutions in the future and improve data quality now. This paper proposes a prediction framework for one of the alarms in the customer installation. The framework can predict sensor faults to a high degree with a precision of 88% and a true positive rate of 79% over a prediction horizon of 24 h. The framework uses a modified definition of an alarm and was tested using a selection of machine learning methods with the optimization of hyperparameters and an investigation into prediction horizons. To the best of our knowledge, this is the first instance of such a methodology.

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized. [ABSTRACT FROM AUTHOR]

The exploitation of green hydrogen blending with natural gas (NG) is expected to contribute in lowering greenhouse gas emissions and fostering the share of renewable energy sources (RES). The NG infrastructure, in fact, can act as a storage facility for green hydrogen produced from excess RES, also providing flexibility to the electric system. However, the unbalance of the networks and the raise of protection issues for consumers may occur as a consequence of the potential decay of the metrological performance of gas meters operating with natural gas and hydrogen blends. In this paper, the authors present the results of an experimental campaign aimed at analysing the effect of hydrogen blending on the accuracy of smart domestic gas meters. Experimental tests were performed in the laboratory using air, natural gas and gas-hydrogen blends up to 23%vol. The results show no criticalities occur for diaphragm and ultrasonic gas meters, regardless of the hydrogen content. On the other hand, thermal mass gas meters show good reliability when hydrogen blends are within 2%vol, whereas for higher hydrogen contents, reliability is demonstrated only when they are equipped with a specific new design routine capable to address the gas quality changes. • Hydrogen blending with natural gas is becoming a spread practice for green energy storage and use. • Issues related to the readiness of the gas infrastructure to distribute hydrogen blends and appliances still have to be investigated. • Consumer protection and fairness of the billing is a crucial issue for domestic gas meters. • Accuracy tests of domestic gas meters of different measuring principles with hydrogen admixture up to 23% are presented. [ABSTRACT FROM AUTHOR]

Engagement with households to fully realize the potential of demand-side solutions has attracted policy attention. The potential of feedback has been understudied, especially regarding who engages more in electricity conservation. Furthermore, most studies have been limited to the Western context, with only a few that explore Asia. This study fills these gaps by investigating changes in household hourly electricity consumption patterns after its members receive high-resolution feedback. After data balancing, we partitioned 63 households into distinct groups using K-means clustering and investigated consumption changes after the provision of high-resolution electricity feedback through a mobile application. The results indicate mixed effectiveness of feedback: some households reduced consumption by about 13 %, while others increased it between 7 % and 20 %. In addition, statistical analysis using survey responses revealed that households with greater awareness of electricity costs and a stronger interest in climate change were more receptive to feedback. Demographic and housing attributes such as age, building type, and floor count also influenced the feedback effect. The findings recommend enhancing awareness of electricity costs and climate change and developing a better understanding of individuals’ challenges with changing conservation behaviors based on their demographic and housing characteristics.

Electricity theft detection is very important for the economic benefits of power companies and the effectiveness of safe operation of power systems. At present, the traditional power theft detection method can only identify whether the user has power theft, but cannot perform rapid and accurate inspections for various types of power theft users. Aiming at the characteristics of medium-voltage users with large power consumption and regular power consumption, this paper proposed a method for detecting the type of power theft in medium-voltage distribution lines based on robust regression and convolutional neural network. Firstly, considering the existence of abnormal data due to factors such as communication delay interruption, a robust regression algorithm is used to reduce its impact and improve the accuracy of regression analysis. Secondly, the correction coefficient and error term of each user obtained by regression are taken as the characteristics of user stealing electricity, and input into the convolutional neural network model for training to complete the identification of stealing electricity type. Finally, the method is verified by simulation and measured data. The results show that under different disturbance conditions, the proposed method can accurately identify different types of power stealing behaviors, which can better assist on-site investigation, narrow the investigation scope and improve the verification rate.

A load identification approach for residential intelligent meters using a random forest (RF) algorithm is employed to guarantee the secure and cost-effective functioning of the electricity grid. In this study, the load data from a smart meter in a home was pre-processed to remove any gaps, noise, or inconsistencies before making any predictions by using the random forest method. The power quality (PQ) features, current features, and Voltage-Current (V-I features), as well as the forecast findings and mathematical tools were used to recognise the load. Using these tools, the household intelligent meters utilising the random forest algorithm, features, harmonic characteristics, and instantaneous characteristics were extracted to form the load characteristics, and the objective function of load identification was generated based on a set of features. The findings of this comparative study demonstrate that employing this technique can reduce identification errors and boost productivity by a full two seconds. The proposed approach, based on a random forest technique, improved home power savings rate by 99.2% and the load management efficiency by 98.6%.

The success of the energy transition relies on effectively utilizing flexibility in the power system. Dynamic tariffs are a highly discussed and promising innovation for incentivizing the use of residential flexibility. However, their full potential can only be realized if households achieve significant benefits. This paper specifically addresses this topic. We examine the leverage of household flexibility and the financial benefits of using dynamic tariffs, considering household heterogeneity, the costs of home energy management systems and smart meters, the impact of higher electricity prices and price spreads and the differences between types of prosumers. To comprehensively address this topic, we use the EVaTar-building model, a simulation framework that includes embedded optimization designed to simulate household electricity consumption patterns under the influence of a home energy management system or in response to dynamic tariffs. The study's main finding is that households can achieve significant cost savings and increase flexibility utilization by using a home energy management system and dynamic electricity tariffs, provided that electricity prices and price spreads reach higher levels. When comparing price levels in a low and high electricity price scenario, with an increase of the average electricity price by 15.2 €ct/kWh (67 % higher than the average for the year 2019) and an increase of the price spread by 8.9 €ct/kWh (494 % higher), the percentage of households achieving cost savings increases from 3.9 % to 62.5 %. Households with both an electric vehicle and a heat pump observed the highest cost benefits. Sufficiently high price incentives or sufficiently low costs for home energy management systems and metering point operation are required to enable households to mitigate rising electricity costs and ensure residential flexibility for the energy system through electric vehicles and heat pumps.

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R.

The Smart Grid, a fusion of digital technologies and advanced communication methods, enables the transformation of power distribution, transmission, and generation by responding to fluctuations in electricity consumption. Conventional electrical grids often leave consumers unaware of their energy usage patterns, resulting in both energy wastage and financial loss. To enhance energy efficiency, the consumption patterns of consumers need regulation. Smart Grids efficiently employs Demand-Side Management (DSM) strategies, including peak clipping, load shifting, and consumer awareness campaigns, to optimize energy consumption and achieve energy savings. DSM’s synergy with smart meters and smart energy management systems (SEMS) emerges as a powerful trio in comprehensive energy conservation and optimization. The integration of SEMS with the Internet of Things (IoT), augmented by the advancements in 5G technology, emerges as a transformative paradigm. SEMS, operating within the IoT ecosystem bolstered by 5G connectivity, facilitates the instantaneous and efficient integration of IoT in SEMS, enabling real-time data collection, in-depth analysis, and data-driven decisions for optimal energy management. This empowers users with the ability to make data-driven decisions, yielding tangible outcomes in the form of efficacy, cost reductions, and fortified sustainability initiatives. We present a SEMS that amalgamates three microcontroller units to create a smart meter that is further integrated with a cloud-based middleware module and consumer API. Furthermore, it collects vital power metrics such as voltage, current, kWh, kW, and PF. Consumers can conveniently access this data in real-time via an API. Energy service companies can leverage this data for incentive programs and to motivate customers to optimize their energy consumption. These datasets serve as a foundational element for the development of diverse DSM strategies. This research work presents the deployment, and performance evaluation of an IoT-based SEMS that is implemented in diverse settings, including industrial, commercial, building, and warehouse setups. Moreover, as a test case, our evaluation extends to the specific conservation of energy within air-conditioning systems. The SEMS achieves significant energy conservation, with calculated savings ranging from 5% to 53%, showcasing its effectiveness in targeted energy management. The real-time data and case studies further demonstrate the efficiency of the presented work.

Broadband over Power Line (BPL) technologies are a successful solution for setting up communication networks in domestic environments. Several standards such as ITU-T G.hn and IEEE 1901 have evolved over the years, and commercial solutions based on them are currently available. However, most existing BPL technologies have not been designed to perform in Low Voltage (LV) access power distribution networks, so there is a need to adapt to the specificities of the communications channel found there. To accommodate the attainable performances of BPL technologies to the access segment of the grid, a communications prototype has been developed that injects custom BPL waveforms with different physical layer configurations and then measures performance. This prototype is an instrumental part of a process to define the most suitable physical layer parameters of Smart Grid BPL devices (namely, next generation BPL smart meters). In the absence of such a prototype, it is not feasible to obtain a proper characterization of the LV grid, as devices capable of producing OFDM (Orthogonal Frequency Division Multiplexing) modulations (typical of BPL technologies) with a varied set of adjustable features, and prepared to connect to the LV access grid, are not available in the market. The prototype is being used to carry out a measurement campaign in Iberdrola’s LV grid in Spain. This paper shows how the prototype has been field validated and derives initial conclusions to drive the future of BPL in LV access grids for Smart Metering and other Smart Grids applications.

Static flow sensors (e.g., thermal gas micro electro-mechanical sensors—MEMS—and ultrasonic time of flight) are becoming the prevailing technology for domestic gas metering and billing since they show advantages in respect to the traditional volumetric ones. However, they are expected to be influenced in-service by changes in gas composition, which in the future could be more frequent due to the spread of hydrogen admixtures in gas networks. In this paper, the authors present the results of an experimental campaign aimed at analyzing the in-service reliability of both static and volumetric gas meters with different hydrogen admixtures. The results show that the accuracy of volumetric and ultrasonic meters is always within the admitted limits for subsequent verification and even within those narrower of the initial verification. On the other hand, the accuracy of the first generation of thermal mass gas flow sensors is within the limits of the verification only when the hydrogen admixture is below 2%vol. At higher hydrogen content, in fact, the absolute weighted mean error ranges between 3.5% (with 5%vol of hydrogen) and 15.8% (with 10%vol of hydrogen). [ABSTRACT FROM AUTHOR]

Due to recent technical breakthroughs in wireless communication and the Internet of Things (IoT), the smart grid (SG) has been recognized as a next-generation network for intelligent and efficient electric power transmission. Electric vehicle charging is becoming one of the most popular SG application. However, in SG environment the communication between a vehicle user and smart meter is mostly performed using insecure channel for managing demand response during peak hours. This raises serious security and privacy issues. Motivated from the aforementioned challenges, this paper presents a secure and privacy-preserving framework for IoT-enabled SG environment. The proposed framework first uses a secure mutual authentication scheme to register and exchange session key among SG participants. Second, a deep learning method that uses a stacked sparse denoising autoencoder to convert data into a new encoded format is suggested. The attention-based truncated long short-term memory uses this modified data to identify intrusions. The proposed blockchain architecture uses the proof of authentication consensus mechanism to propagate regular transactions in order to validate data integrity and prevent data poisoning attacks. The suggested framework outperforms several current state-of-the-art solutions in terms of security and numerical discoveries. [ABSTRACT FROM AUTHOR]

The Internet of Things is an ecosystem that connects billions of smart devices, meters, and sensors. These devices and sensors collect and share data for use and evaluation by organizations in different industry sectors. Humans may use the IoT to live and work more intelligently and gain total control over their lives. Consequently, IoT can be used to connect devices and integrate them with new digital technologies for customers. On the other hand, smart utility companies in the electric, gas, and water sectors need to deliver services more efficiently and analyze their operations in a way that can help optimize performance, detect growing problems in real time, and initiate fixes to avoid unplanned service interruptions. Building actual smart metering networks is costly and time-consuming. Therefore, in this paper, a new Smart Utilities Traffic Scheduling Algorithm (SUTSA) is proposed. To minimize the system complexity, the model is based on narrowband power line communication, in which a wired hidden network sends data across power lines. A simulation is performed using OPNET Modeler 14.5 to evaluate the proposed model. The results proved that the proposed model is highly scalable and achieves full network-bandwidth utilization in different situations based on different application requirements. [ABSTRACT FROM AUTHOR]

Home area network (HAN) devices send the electricity consumption and other important data to the smart meter that must remain confidential from other devices. This paper proposes a novel one-dimensional hybrid chaotic map. The proposed map shows excellent chaotic properties when analyzed by bifurcation diagram, Lyapunov exponent & Shannon entropy. We further design an encryption strategy for data transfers between the smart meter and HAN devices. The proposed encryption scheme uses the existing lightweight key management in advanced metering infrastructure (LKM-AMI) architecture for data transfers, in which the encrypted data is transferred through an insecure channel and private keys are provided by trusted third party (TTP) through secure channels. The 2-way communication between HAN devices and the smart meter sends messages that are encrypted by using the proposed novel hybrid one-dimensional chaotic map. The encryption strategy mainly consists of three steps. In the first step, the seed and the control parameters are initialized. The second phase generates two intermediate keys using the proposed hybrid chaotic map. In the last phase, we encrypt the message by applying permutation followed by diffusion using intermediate keys. The proposed encryption strategy is resistant to various attacks. [ABSTRACT FROM AUTHOR]

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R. [ABSTRACT FROM AUTHOR]

The incorporation of renewable energy sources necessitates the upgrade of the electrical grid to a smart grid, which involves the implementation of smart meters. Although smart meters provide benefits to users, many smart meter implementation projects have failed to be accepted by users. This article assesses the factors influencing the acceptance of household smart meters in Joinville, a city in the south of Brazil. Based on the Unified and Extended Theory of Acceptance and Use of Technology (UTAUT2), a structural equation model was estimated using data from a sample of 136 respondents in the city of Joinville. The results indicate that Performance Expectancy, Hedonic Motivation, and Social Influence constructs have a more substantial effect on the Intention to Use smart meters. The results provide evidence for planning the upgrade of the electrical grid by implementing smart meters in southern Brazil. [ABSTRACT FROM AUTHOR]

Urbanization increases electricity demand due to population growth and economic activity. To meet consumer’s demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imbalance by accurately predicting the energy consumption. This paper presents a comprehensive literature review of forecasting methodologies used by researchers for energy consumption in smart buildings to meet future energy requirements. Different forecasting methods are being explored in both residential and non-residential buildings. The literature is further analyzed based on the dataset, types of load, prediction accuracy, and the evaluation metrics used. This work also focuses on the main challenges in energy forecasting due to load fluctuation, variability in weather, occupant behavior, and grid planning. The identified research gaps and the suitable methodology for prediction addressing the current issues are presented with reference to the available literature. The multivariate analysis in the suggested hybrid model ensures the learning of repeating patterns and features in the data to enhance the prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes three methods of the optimal smart meter selection for acting as a data concentrator in the automatic meter reading last mile network. The study explains the reasons why the selected smart meter should also act as a data concentrator, in addition to its basic role. To select the smart meter, either the reliability of communication or the speed of the automatic meter reading process was considered. Graph theory is employed to analyse the last mile network, described as sets of nodes and unreliable links. The frame error ratio was used to assess the unreliability whilst the number of hops was used to describe the speed of the reading process. The input data for the analysis are qualitative parameters determined based on observations in the real, operated last mile networks as well as their typical topological arrangements. The results of the research can be useful in the last mile network migration process, which uses concentrators to the networks without them, or during the process of newer last mile network implementation, where data concentrators are no longer applicable. The efficiency of the proposed methods is assessed measurably. [ABSTRACT FROM AUTHOR]

A smart grid is an electricity network that uses advanced technologies to facilitate the exchange of information and electricity between utility companies and customers. Although most of the technologies involved in such grids have reached maturity, smart meters—as connected devices—introduce new security challenges. To overcome this significant obstacle to grid modernization, safeguarding privacy has emerged as a paramount concern. In this paper, we begin by evaluating the security levels of recently proposed authentication methods for smart meters. Subsequently, we introduce an enhanced protocol named PPSG, designed for smart grids, which incorporates physical unclonable functions (PUF) and an elliptic curve cryptography (ECC) module to address the vulnerabilities identified in previous approaches. Our security analysis, utilizing a real-or-random (RoR) model, demonstrates that PPSG effectively mitigates the weaknesses found in prior methods. To assess the practicality of PPSG, we conduct simulations using an Arduino UNO board, measuring computation, communication, and energy costs. Our results, including a processing time of 153 ms, a communication cost of 1376 bits, and an energy consumption of 13.468 mJ, align with the requirements of resource-constrained devices within smart grids. [ABSTRACT FROM AUTHOR]

Smart grid technology's boundary of impact and security risk ranges from utility services, energy management services, manufacturers, law enforcement agencies, and consumers. As smart technology is deployed at a faster pace than regulation can keep up with as well as identifying vulnerabilities within smart technology; it becomes necessary to better understand what smart technology is currently available. To assist everyday consumers in better understanding how their data is collected, used, analyzed, and the potential implications stemming from the use of their data, we discuss multiple state and international privacy laws and examples of misuse cases. [ABSTRACT FROM AUTHOR]

Not only can theft of energy result in monetary losses, but it also results in expenses that are not technical for energy providers and even for the power infrastructure. Theft of energy has a detrimental effect on both the financial viability and the quality of the electricity. Through the integration of information and energy flows, smart grids have the potential to avoid power theft. The analysis of data from smart grids makes it easier to identify instances of power theft. In contrast, previous systems fared badly when it came to detecting instances of energy theft. To help and assess energy supply businesses in decreasing the barriers of low energy, unexpected power use, and poor power management, we presented in this study a method to detect electricity theft based on consumption data from smart meters. This was done in order to assist and evaluate energy supply businesses. More specifically, the Deep CNN model is able to successfully accomplish two tasks: it differentiates between periodic and non-periodic energy while maintaining the overall features of the power consumption dataset. When it comes to detecting instances of energy theft, the results of the tests indicate that the deep CNN model displays the highest level of accuracy and exceeds prior versions.

This paper proposes a new data-driven method for power outage detection. By capturing the changes in data distribution of smart meters (SM), it can detect power outages in partially visible distributed systems. First, a mechanism based on breadth-first search (BFS) is proposed, which decomposes the network into a set of regions to find the location information where power outages are most likely to occur. Then, the SM data for each region, generating a generative adversarial network (GAN), is used in order to extract unsupervised manner implicit temporal behavior under normal conditions. After network training, anomaly scoring technology is used to determine whether the real-time measurement data is the data of a power outage event. Finally, in order to infer the location of a power outage in a multi-area network, a regional coordination process with interdependence be-tween cross-regions is used. At the same time, the concept of entropy is used to provide performance analysis for the algorithm in this paper. This method has been verified on the distribution feeder model with actual SM data. Experimental results show that the algorithm is effective and feasible.

Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature.

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.

This study addresses the challenges posed by conventional energy meters, which rely on manual readings, leading to human errors and inefficiencies. In response to this, a battery-powered smart meter was developed utilizing an STM32 microcontroller, ADE7758 and STPM32 metering integrated circuits (ICs), SIM and ESP32 communication modules, along with a MYSQL database. Real-time energy data from both single and three-phase appliances were collected, and their energy consumption, errors, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were quantified. The model demonstrated an acceptable accuracy level, with an estimated MAE of approximately 2.912 units and an estimated RMSE of around 4.048 units, particularly in predicting motor power consumption. Additionally, ARIMA forecasting was applied to a three-phase asynchronous motor, revealing an average active motor power of 250.95 watts, indicating precise results over time.

Urban energy modeling is essential in planning electricity generation and efficiently managing electric power systems. Various urban energy models were developed for several energy-driven applications, including emission reduction, retrofit analysis, and forecasting. Electricity load forecasts help to estimate the load demand and effectively aid in power system operation and balancing. The accuracy of load forecasts at high temporal and spatial resolution can impact system planning and operation. Therefore, it is essential to know the factors that affect the accuracy of these forecasts and how they can be improved regarding the current state of the art. This article reviews the recent literature on data-driven electricity load forecasts in three steps. First, different phases of the review process are explained to select and analyze recent literature on machine learning-based short-term load forecasts. Then various aspects of load forecasting techniques have been reviewed, addressing their advantages, disadvantages, temporal resolution, and performance. Finally, the review covers the current challenges in load forecasting and describes the reasons for performance degradation and lower accuracy. Based on the reviewed literature, it was found that temperature, user load profiles, and proper management of input data highly affect load forecast accuracy. In addition, shortcomings of existing performance evaluation metrics make the applicability of those techniques questionable. Finally, we conclude the review by highlighting the necessary actions to improve load forecast accuracy that are relatively unexplored and can be used as a reference for future research on accurate load forecasts.

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device’s continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%.

The transactive energy market (TEM) is a recent development in energy management that enables prosumers to trade directly, promising many environmental and economic benefits. Prosumer trading necessitates sharing information to facilitate transactions. Additionally, many TEMs propose using blockchains to manage auctions and store transactions. These facts introduce privacy concerns: consumption data, trading history, and other identifying information pose risks to users if leaked. Anonymity by trading under a pseudonym is commonly presented as a solution; however, this creates risks for market participants: scammed users will not have recourse, and users with innocent malfunctions may be banned from trading. We propose the Individually Linkable Pseudonymous Trading Scheme (ILPTS), which enables users to trade under a pseudonym, protecting their identity, while a smart contract monitors reputations and can temporarily deanonymize a user, ensuring market integrity. ILPTS was developed in stages. Examination of existing TEM literature was performed to identify desirable features. Analysis of cryptography literature was performed to identify techniques that may confer certain features. It was found through formal analysis that ILPTS adheres to identified design goals, improves upon existing solutions, and resists common attacks against TEMs. Future work includes software simulation and on-device implementation to further verify security and feasibility.

Water utilities in Japan face a number of challenges, including declining water demand due to a shrinking population, shrinking workforce, and aging water supply facilities. Widespread use of smart water meters is crucial for solving these problems. The widespread use of smart water meters is expected to bring many benefits such as reduced labor by automating meter reading, early identification of leaks, and visualization of pipeline data to strengthen the infrastructure of water services, business continuity, and customer service, as detailed data can be obtained using wireless communication. Demonstration tests are actively conducted in Japan; however, many problems have been reported with cast iron meter boxes blocking radio waves. To address the issue, a low-cost slit structure for cast iron meter boxes is investigated in this study. The results confirm that the L-shaped tapered slit array structure with a cavity, which can be fabricated in a cast iron integral structure, satisfies the design loads required for road installation. The proposed slit structure achieved gain characteristics from −3.32 to more than 9.54 dBi in the 800 to 920 MHz band. The gain characteristics of conventional cast iron meter boxes range from −15 to −20 dBi, and the gain has been significantly improved. Antennas with a gain of −2.0 to +1.5 dB (0.8 to 2.5 GHz) were used for the transmitter antenna, which was found to have a higher gain than the transmit antenna in the 800 to 880 MHz frequency band. In the 1.5 to 2.0 GHz band, a high peak gain of 4.25 dBi was achieved at 1660 MHz, with no null and the lowest gain confirmed that this is an improvement of more than 10 dBi over conventional products. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption. [ABSTRACT FROM AUTHOR]

This article summarizes and organizes recent research findings in information and communication technology security developments integrated with smart grids. A vital component of a smart grid is a smart meter. It is relevant because it can collect, process, and transport customer's data over the Internet. Whereas developments in smart grid and smart meter technologies have given new productivity gains, they have also presented new security concerns. Security is essential in defending both the smart grid and smart meter from cyber-attack. Guaranteeing safety is one of the most challenging aspects of designing and deploying a smart metering infrastructure. This study presents a thorough investigation of the integrity of smart metering technologies from multiple different viewpoints. It focuses on threats, countermeasures, and estimations. This article makes four contributions: First, all potential vulnerabilities in smart metering components are described and examined. Second, it assesses the impact of attacks that use these weaknesses to boost the performance of each part and the whole smart meter structure. Thirdly, potential countermeasures to defend smart meters are discussed. lastly, it discusses the unresolved issues surrounding smart meter security and future research areas. This evaluation is distinctive due to its exhaustive treatment of security weaknesses and attacks on smart meter components. In conclusion, the future vision is described. [ABSTRACT FROM AUTHOR]

Various water distribution leak detection and localization methods have been proposed for supervisory control and data acquisition (SCADA) data collection systems. However, because their available numbers of measurements are limited, the SCADA systems are often insufficient to identify realistic sized leaks. A clear next step is to develop detection and localization methods for smart systems that collect advanced metering infrastructure (AMI) data (i.e., AMI systems); however, only the authors have proposed tools for the AMI data collection systems. To encourage the usage of AMI data, this study tested five types of data collection systems for leak detection and localization that measure (1) only source flows, (2) source flows and a few nodal pressures, (3) source flows and AMI demands, (4) AMI demands and a few nodal pressures, and (5) AMI pressures and AMI demands. An appropriate leak detection and localization tool for each data collection system is applied and tested for two water distribution networks: one located in Austin, TX, and the other in Tucson, AZ. Each system's performance was evaluated using metrics of detection probability, false alarm rate, time to detect, and localization pipe distance. Overall, based on the obtained results, the SCADA systems were poor in detecting realistic-sized leaks, while the AMI systems successfully identified those small failures. Thus, the AMI systems were required to improve detection, and a pressure-supplemented AMI system was necessary to obtain high localization performance, particularly for a large network, such as Austin, Texas. [ABSTRACT FROM AUTHOR]

State estimation for distribution systems faces the challenge of dealing with limited real-time measurements and historical data. This work describes a Bayesian state estimation approach tailored for practical implementation in different data availability scenarios, especially when both real-time and historical data are scarce. The approach leverages statistical correlations of the state variables from a twofold origin: (1) from the physical coupling through the grid and (2) from similar behavioral patterns of customers. We show how these correlations can be parameterized, especially when no historical time series data are available, and that accounting for these correlations yields substantial accuracy gains for state estimation and for the recognition of critical system states, i.e., states with voltage or current limit violations. In a case study, the approach is tested in a realistic European-type, medium-voltage grid. The method accurately recognizes critical system states with an aggregated true positive rate of 98%. Compared to widely used approaches that do not consider these correlations, the number of undetected true critical cases can be reduced by a factor of up to 9. Particularly in the case where no historical smart meter time series data is available, the recognition accuracy of critical system states is nearly as high as with full smart meter coverage. [ABSTRACT FROM AUTHOR]

Smart microgrids are being increasingly deployed within the Department of Defense. The microgrid at Marine Corps Air Station (MCAS) Miramar is one such deployment that has fostered the integration of different technologies, including 5G and Advanced Metering Infrastructure (AMI). The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and identify specific cyber-attacks against this infrastructure. Both network traffic in the form of packet captures (PCAP) and time series data (from the AMI sensors) are considered. We train the autoencoder model on three traffic data sets: (1) Modbus TCP/IP PCAP data from the hardwired network apparatus of the smart microgrid, (2) experimentally generated 5G PCAP data that mimic traffic on the smart microgrid and (3) AMI smart meter sensor data provided by the Naval Facilities (NAVFAC) Engineering Systems Command. Distributed denial-of-service (DDoS) and false data injection attacks (FDIA) are synthetically generated. We show the effectiveness of the autoencoder on detecting and classifying these types of attacks in terms of accuracy, precision, recall, and F-scores. [ABSTRACT FROM AUTHOR]

Power utilities worldwide are facing enormous challenges when it comes to the distribution of electricity. With these challenges, electricity theft is regarded as the most common challenge in the electrical distribution system. Electricity theft can be meter tampering done in consumer houses and illegal connections done using hook-ups from the distribution pole grids. These electricity theft challenges have caused power utilities to reconsider customer engagements focusing on feedback, putting loss detection systems in their distribution system networks, using artificial intelligence to schedule maintenance and other asset management activities, etc. The main focus of this paper is to design a real-time power theft monitoring and detection system that is able to detect power theft in distribution systems. This proposed system utilizes smart meters consisting of an Arduino ATMega328P microcontrollers with GSM modules (Global System for Mobile Communication) used for system communication. Cloud storage is created to store the smart meter data. Simulations of the proposed system were done using Proteus Design Suite v.8.10 SP3 software. The proposed system is practically constructed for prototype measurement results. Should power imbalances be measured by the system, the authority office will receive an SMS notification as an alert for power theft detected by a specific smart metering system. The authority office will analyse the power measurements sent to the cloud storage (MATLAB Online, ThingSpeak IoT channels display), and further action will be taken. [ABSTRACT FROM AUTHOR]