Smart Meters Research Articles

A Brief Review on Ancillary Services from Advanced Metering Infrastructure (ASAMI) for Distributed Renewable Energy Network

Advanced metering infrastructure (AMI) is an integrated system of smart meters, communications networks, and data management systems that enable the secure, effective, and dependable distribution of power while also delivering enhanced capabilities to energy consumers. The system also can measure power usage, connect, and disconnect service, detect tampering, identify and isolate outages, and monitor voltage automatically and remotely, which were previously unavailable or required user intervention. This article focuses on AMI and effectively integrating renewable energy sources (RES). However, the study also recommends smart metering for renewables such as solar photovoltaic (PV), hydropower, anaerobic digestion (ad) metering, and renewable energy storage, in which AIM thoroughly supervises the energy utilized by users' appliances. With the prediction of new ancillary services connected with contestability, related regulation, the sufficiency of consumer protection, and safety issues, the magnitude of renewable energy sources in the AMI is an almost unprecedented problem for consumers. The present energy management problems include reducing the power supply-demand gap and boosting power supply dependability. Implementing AMI with distributed renewable energy resources might be a viable strategy for lowering power consumption, improving power supply management, and maximizing management resource use.

Novel cyber-physical architecture for optimal operation of renewable-based smart city considering false data injection attacks: Digital twin technologies for smart city infrastructure management

Smart grid is considered a cyber-physical system, which is a combination of physical devices and computational processes. Since there are lots of interactions between the cyber layer and the physical layer, the operation, management, and security of the entire system are crucial topics that must be taken into account. In this regard, this paper tackles two problems in AC MGs, as a special case of smart cities. Firstly, a framework is proposed to solve the optimal scheduling problem in these systems. In this stage, the optimal management and scheduling of the system are structured and modeled as an optimization problem. The dragonfly is utilized as a powerful optimization technique to solve this optimization problem. It is worth noting that in this paper different renewable energy sources, as well as batteries are considered. Since cyber-attacks are among the greatest threats to the system and can cause disruption and outage in smart grids, in this paper a deep-learning-based method called long short-term memory along with the concept of prediction interval is utilized to develop a cyber-attack detection model for false data injection attacks on smart meters. The proposed cyber-attack detection model is first trained using historical data and then is used in real-time for detection. In order to investigate the effectiveness of the proposed optimal scheduling scheme, the modified IEEE 33-bus test system is utilized. Also, for testing the proposed cyber-attack detection model a real-world dataset is used. The simulation results show the great performance and effectiveness of both proposed methodologies. It should be noted that all model are simulated in digital twin simulator. The novel LSTM-based cyber-attack detection model enhances security in electric grids, demonstrating remarkable accuracy.

Quantum-resistance blockchain-assisted certificateless data authentication and key exchange scheme for the smart grid metering infrastructure

In the contemporary landscape of energy infrastructure, the “smart-grid metering infrastructure (SGMI)” emerges as a pivotal entity for efficiently monitoring and regulating electricity generation in response to client behavior. Within this context, SGMI addresses a spectrum of pertinent security and privacy concerns. This study systematically addresses the inherent research problems associated with SGMI and introduces a lattice-based blockchain-assisted certificateless data authentication and key exchange scheme. The primary aim of this scheme is to establish quantum resistance, conditional anonymity, dynamic participation, and the capacity for key updates and revocations, all of which are imperative facets for the robust implementation of mutual authentication within SGMI. Our scheme harnesses blockchain technology to mitigate the vulnerabilities associated with centralized administrative control, thus eliminating the risk of a single-point failure and distributed denial-of-service attacks. Furthermore, our proposed scheme is meticulously designed to accommodate the resource constraints of smart meters, characterized by lightweight operations. Rigorous formal security analysis is conducted within the framework of the quantum-accessible random oracle model, fortified by ’history-free reduction,’ substantiating its security credentials. Complementing this, simulation orchestration serves to underscore its superiority over existing methodologies, particularly in the realms of energy efficiency, data computation, communication, and the costs associated with private key storage.

Structure Evolution and Performance of Fe-Si Core Used in Power Inductors of Smart Meters

Structure Evolution and Performance of Fe-Si Core Used in Power Inductors of Smart Meters

Smartgrid-based hybrid digital twins framework for demand side recommendation service provision in distributed power systems

Electricity consumers face challenges in selecting an optimal energy-saving plan, and this is a sustainability problem. To set consumers focus on sustainable energy management, developments around ”Industry 4.0” are needed to achieve an optimal balance between cost and energy consumption with a focus on cutting-edge machine-learning models and smart services introduction. Energy modelling is crucial for confronting the challenges introduced by the energy transition. This research is to promote digital twin (DT) technology in smart energy grids. The hybrid digital twin model is developed based on smart grid end-users’ electrical components. This approach emulates the component specification of the reference smart grid system and encourages a reduction in net energy consumption. Additionally, the application of AI connects smart meters, IoT devices, and the assets of the smart grids to the DT recreation of demand-side end-users for energy efficiency recommendation provision; this setup improves energy management, energy efficiency, and the usage of renewable energy resources. The novelty of this study is that the recent scope of demand-side recommendation schemes has been extended in the direction of cutting-edge Industry 4.0 hybrid DTs. Additionally, a prototype system and its functional characteristics have been proposed with the potential to pave the path for utilization in microgrid environments. Simulation results show a unique level of parallelism between the reference system and the developed model, along with a 36.8% reduction in net energy consumption following implementation of the recommendation advice.

Efficient Usage of Energy Infrastructure in Smart City Using Machine Learning

The concept of smart cities revolves around utilizing modern technologies to manage and optimize city operations, including energy infrastructure. One of the biggest problems that smart cities have to deal with is ensuring the efficient usage of energy infrastructure to reduce energy consumption, cost, and environmental impact. Machine learning is a powerful tool that can be utilized to optimize energy usage in smart cities. This paper proposes a framework for efficient usage of energy machine learning for city infrastructure in smart cities. The proposed framework includes three main components: data collection, machine learning model development, and energy infrastructure optimization. The data collection component involves collecting energy consumption data from various sources, such as smart meters, sensors, and other IoT devices. The collected data is then pre-processed and cleaned to remove any inconsistencies or errors. The machine learning model development component involves developing machine learning models to predict energy consumption and optimize energy usage. The models can be developed using various techniques such as regression, classification, clustering, and deep learning. These models can predict energy consumption patterns based on historical data, weather conditions, time of day, and other factors. The energy infrastructure optimization component involves utilizing the machine learning models to optimize energy usage. The optimization process involves adjusting energy supply and demand to reduce energy consumption and cost. The optimization process can be automated, and SVM based machine learning models can continuously enhance their precision over time by studying the data. The proposed framework has several benefits, including reducing energy consumption, cost, and environmental impact. It can also improve the reliability and stability of energy infrastructure, reduce the risk of blackouts, and improve the overall quality of life in highly developed urban areas. Last but not least, the projected framework for efficient usage of energy machine learning for city infrastructure in smart cities is a promising solution to optimize energy usage and reduce energy consumption and cost. The framework can be implemented in various smart city applications, including buildings, transportation, and industrial processes.

A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm, leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10% of the dataset, exhibit significant internal dissimilarity. As a result, these clusters have been excluded from consideration for DR programs. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted DR programs.

Monitoring Energy and Power Quality of the Loads in a Microgrid Laboratory Using Smart Meters

Microgrids are local energy production and distribution networks that can operate independently when disconnected from the main power grid thanks to the integration of power generation systems, energy storage units and intelligent control systems. However, despite their advantages, the optimal energy management of real microgrids remains a subject that requires further investigation. Specifically, an effective management of microgrids requires managing a large number of electrical variables related to the power generated by the microgrid’s power supplies, the power consumed by the loads and the aspects of power quality. This study analyzes how we can monitor different variables, such as the active power, reactive power, power factor, total harmonic distortion and frequency in the loads of a microgrid, using high-precision power meters. Our empirical study, conducted using a functional microgrid comprising a hybrid wind–solar power system and several household appliances, demonstrates the feasibility of using low-cost and high-performance power meters with IoT functionality to collect valuable power quality and energy consumption data that can be used to control the microgrid operation.

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

Research on Blockchain-Enabled Smart Grid for Anti-Theft Electricity Securing Peer-to-Peer Transactions in Modern Grids.

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain's core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower-upper decomposition (LUD), lower-upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users' "honesty coefficients," providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods.

Comparative Analysis of TCP, UDP and SCTP in Smart Meter Data Transfer

Comparative Analysis of TCP, UDP and SCTP in Smart Meter Data Transfer

Detection and location of high impedance faults in delta 13.8kV distribution networks

This article presents the main results obtained in the project “Automated System of Classification, Detection, and Location of High Impedance Faults in Distribution Networks”, carried out for Brazilian utility COPEL, between 2019 and 2021, as part of ANEEL's R&D program. The project developed an automated system with efficient and robust algorithms for the real-time detection, classification, and location of high impedance faults in the distribution networks. The system consisted of computational tools to automatic high impedance faults detection and location in 13.8 kV delta-connected grid. Field tests carried out in a real feeder turned possible the development of a simple and reliable algorithm, related to the amplitude and phase shift of the zero-sequence current monitored in the substation. The fault location was determined by consulting undervoltage at automatic reclosers, and smart energy meters installed along the feeder. A computational tool was developed to provide relevant information to the utility Operation Center to initiate an emergency maintenance.

Energy Management of a Low-Cost Power Meter using ESP8266 and PZEM-016


 
 
 The burgeoning significance of the Internet of Things (IoT) lies in its capacity to configure interconnected environments and facilitate human-object interactions through collaborative services. This study proposes an efficient energy management approach leveraging cost-effective technologies like the ESP8266 microcontroller and the PZEM-016 Modbus RTU energy monitoring module. Tailored towards wireless connectivity, this solution is purposefully crafted for diverse sectors operating within constrained budgets, obviating the need for intricate infrastructure. A systematic deployment of the forward engineering research methodology is undertaken to discern the requisites and hurdles inherent in energy management. The amalgamation of ESP8266, PZEM-016, and the MQTT protocol, with RabbitMQ serving as a message broker, forges an efficacious framework for inter-device information exchange. The solution's instantiation entails the interconnection of power meter devices using the MQTT protocol, transmitting data in JSON format. The PZEM-016 sensor constitutes the crux, adeptly measuring voltage, current, frequency, and power with precision. Furthermore, the solution encompasses a prototype Smart Meter fortified with Wi-Fi connectivity to the internet, thus extending network coverage ubiquitously. Economic scrutiny reveals that the resultant power meter device costs less than 100 USD, competitively positioning it against analogous market offerings. This economically optimized design advocates for widespread adoption across multifarious sectors constrained by budgetary limitations, assuaging the complexities inherent in energy management through a trifecta of efficiency, reliability, and affordability.
 
 

Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

Energy-Efficient Dynamic MAC Protocol for M2M Communication

In the Internet of Things, which is vital for smart city applications, a vast network of battery-operated Machine Type Communication Devices (MTCDs) requires Machine Type Communication. These MTCDs connect directly to a network with a base station, facilitating a wide range of smart city functionalities. The development of energy-efficient communication protocols is essential to support the deployment of energy-constrained battery-operated devices. These protocols aim to minimize energy consumption, thereby enhancing network lifetime. Additionally, they must address scalability, reduce collisions and delays, and ensure a high Quality of Service (QoS) to effectively manage the vast number of devices involved. Clustering is a viable strategy to enhance scalability and energy efficiency in network communications. However, within this framework, particularly in the proposed Power Efficient Dynamic MAC protocol for M2M Communication (PMAC), there's still a need for energy-efficient communication between the Machine Type Communication Devices (MTCDs). This requirement is crucial to optimize the overall performance and sustainability of the network. In this paper, we analyses the Energy Efficient CSMA/CA (E-CSMA/CA) protocol, specifically developed for facilitating communication between Machine Type Communication Devices (MTCDs) in short-range communications. This analysis is set within the context of smart city applications, with a particular focus on smart metering. This approach underscores the importance of efficient, reliable communication protocols in modern urban infrastructure, especially for applications like smart metering that are integral to the smart city ecosystem.

User’s perspective in smart meter research: State-of-the-art and future trends

Over the past decade, there has been extensive research on smart meters’ rollouts worldwide. Many studies have explored smart meters from the perspective of residential users. However, there is a lack of a bibliographic analysis synthesizing the knowledge into an intellectual framework. This work aimed to comprehensively analyze the available literature, covering 1555 articles. A bibliographic analysis was carried out using bibliographic coupling, co-citation, and co-word analysis, thus providing a comprehensive quantitative and qualitative analysis. The result is an intellectual framework of this literature, outlining the studied themes (smart energy meter feedback, smart meter and the impact of renewable sources, smart meter’s contribution to water and energy management, effect of the smart meter on consumption reduction, and consumer in the context of smart grids) and twenty different approaches used by researchers. Past findings and future research trends of each theme are also discussed.

Activity and resolution aware privacy protection for smart water meter databases

Identifying water end-uses from household meter readings yields valuable commercial and environmental insights for both customers and water providers. However, smart water meter databases may expose sensitive information about the activities of metered households, thus raising privacy concerns that limit the possibility of making smart meter data available for research and planning. This paper considers the case where a water provider wishes to publish a database of household water meter traces to be used by water analysts, who are often external entities. This scenario presents privacy risks should an adversary gain access to this database, with the threat of uniquely identifying a household of interest and exposing its water use activities. To mitigate such risks, this paper proposes a process for activity-aware privacy protection and evaluates its effectiveness using real-world and synthetic databases. Our experimental analysis shows that water meter privacy protection is strongly dependent on the type of activity, temporal resolution, and population size. For example, in the case of a high-resolution database, we found that a large base population, small published sample, and 30-second resolution provided optimal trade-offs between privacy and information value. For a low-resolution database with a population of over 3500 households, 1-hour resolution provided strong information value and customisable privacy guarantees depending on the published sample size.

Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

Metering anomalies not only mean huge economic losses but also indicate the faults of equipment and power lines, especially within the substation. As a result, metering anomaly diagnosis is becoming one of the most important missions in smart grids. However, due to the insufficient and imbalanced anomaly cases, identifying the anomalies in smart meter data accurately and efficiently remains challenging. Existing methods usually employ few-shot learning models in computer vision directly, which requires the rich experience of human experts and sufficient abnormal cases for training. It blocks model generalizing to various application scenarios. To address these shortcomings, we propose a novel framework for metering anomaly diagnosis based on few-shot learning, named FSMAD. Firstly, we design a fault data injection model to emulate anomalies, so that no abnormal samples are required in the training phase. Secondly, we provide a learnable variable transformation to reveal inherent relationships among various smart meter data and help FSMAD extract more efficient features. Finally, the deeper metric network is equipped to support FSMAD in obtaining powerful comparison capability. Extensive experiments on a real-world dataset demonstrate the advantages of our FSMAD over state-of-the-art methods.

Detection of False-Reading in Smart Grid Net-Metering System

Malicious consumers may hack their smart meters (SMs) in the smart grid to report fake readings in order to make unlawful financial profits. Since the reported readings are used for energy management, the utility suffers significant financial losses as a result, and grid performance may suffer as a result. The net metering system, in which one SM is used to report the difference between the power consumed and the power generated, is the subject of this research, which is the first study to look into the issue. First, we process real power consumption and generation information to create a benign dataset for the net-metering system. The data was then examined, and time correlations between the net meter readings and correlations between the measurements and pertinent information from reliable sources, such as temperature, were discovered. Based on the data analysis, we suggest a single-pole double-throw detector to detect erroneous readings. In addition to data from reliable sources, our detector is trained on net meter readings from every customer in order to improve its performance by discovering correlations between them. We also create a relationship between the customer and the grid. Since the only billing system available nowadays is online, this project includes an app that may be used to solve a variety of problems, including electrical outages. Overall, with this, we improve customer and grid communication and simplify their lives.

A Hybrid Real-Time Electricity Pricing Strategy with Carbon Capture, Storage and Trading

During the power system low carbonization process, various policies and technologies for low-carbon have been rapidly developed. Real-time demand side management by energy suppliers based on real-time pricing (RTP) has become a reality via smart meter technology. Motivated by this, an RTP mechanism is used to guide users’ demand response, and the carbon trading regulation (CTR) and carbon capture, storage (CCS) technology are used on the supply side. Through these means, we can reduce carbon emissions from the power system and achieve the goal of carbon neutrality. Furthermore, a game model is used to construct the relationship between the energy supplier and users. Since the energy supplier acts first and then the user make their decision, the leader is the energy supplier and the followers are users. Further, to protect the information privacy of them and independently solve the goals of them, a distributed solution approach combining a differential evolutionary (DE) algorithm and Gurobi solver is developed for finding the equilibrium solution. The proposed model’s economic and environmental benefits are verified and the effectiveness of CCS technology and CTR is accessed via the numerical analysis, what means the proposed strategy can serve as a guide for the system’s low-carbon management.