Advanced Metering Infrastructure Research Articles

An evaluation method for integrating EVs in distribution networks with clustering algorithms

Introduction: With the installation of advanced metering infrastructures, the operation data of EVs in the distribution networks can be obtained with time intervals of seconds and minutes. Based on these operation data, the impacts of integrating EVs into the distribution networks can be calculated and discussed.Methods: In this paper, an improved clustering algorithm with a new distance index for the daily curves of different types of EVs was proposed. The different types of EVs can be classified into several typical groups and the required number of operation scenarios can be reduced. After reducing the large-scale database to typical clusters, research can be conducted on the characteristics of EVs specific to certain scenarios.Results and discussion: In this way, the capability of integrating different types of EVs into the distribution network, such as fast EV charging stations, slow EV charging stations, and EV bus charging stations, is assessed from the perspective of load capacity size. The proposed clustering algorithm was verified with practical operation data.

A Lightweight_PAEKS-based energy scheduling model considering priority in MicroGrid

Priority-based energy scheduling is proposed, whereby the data utility of measured user information in smart meters, including the priority and power demand of power consumers and the maximum power supply of power suppliers, is leveraged to ensure that in the case of limited power resources within islanded microgrids, energy is allocated to power consumers in the order of high to low priority. Nonetheless, in terms of data utility, most existing strategies only consider high-priority power consumers, neglecting the priority of power suppliers and low-priority power consumers, thus damaging the fairness of low-priority consumers and the interests of power suppliers, to the detriment of these groups. Moreover, smart meters are vulnerable to data integrity attacks, which may result in the manipulation of measured user information during the forwarding process, thereby disrupting normal priority-based energy scheduling. Therefore, focusing on data utility without considering data security is insufficient. Researchers have extensively investigated how to secure smart meters within advanced metering infrastructure (AMI), primarily employing cryptography-based methods to encrypt user information in measured meters and decrypt it at the microgrid central controller (MGCC), thereby protecting the data confidentiality of user information during the forwarding process and preventing data tampering. However, the prevailing cryptographic methods used to secure smart meters are extremely complex, and make smart meters and local controllers on the forwarding path unable to obtain any user information in which embedded consumers’ priority and cannot leverage the data utility of priority to forward information accordingly. In other words, existing models cannot simultaneously achieve data confidentiality and data utility during the forwarding process. To solve these issues, this study investigates a Lightweight_PAEKS-based energy scheduling model considering priority in microgrid (LPESCP). As a lightweight solution, the LPESCP comprehensively considers data confidentiality and data utility, as well as the priority and fairness of all users, to ensure the security of smart meters and optimize energy scheduling. In particular, the data utility problem is solved by using an optimization model that aims to maximize the global satisfaction degree of all users in terms of priority and fairness. The Lightweight_PAEKS and Paillier encryption scheme are used so that nodes on the forwarding path can successfully match priority-related keyword and forward information in order of the keyword corresponding emergency coefficient indicating the urgency levels of information, ensuring data utility, while do not know the specific keyword, emergency coefficient and other information, ensuring data confidentiality. To verify the effectiveness of the LPESCP, experiments are conducted for three cases generated based on random numbers. The results show that the LPESCP model can effectively ensure energy supply to consumers, comprehensively considering data confidentiality and utility as well as priority and fairness of all users. In addition, the global satisfaction degree and time overhead are introduced as metrics to verify that the LPESCP strategy is more effective in providing greater global satisfaction degree and lower time overhead than existing priority-based energy scheduling models.

Detection of Electricity Theft based on Minimal Gated Memory network combined Adaptive Synthesis sampling and Decision Tree

Advanced metering infrastructure is the foundation for recording and analyzing the massive, high-frequency electricity consumption data. With the increasing number of electricity theft issues caused by tampering with smart meters, it is necessary to detect electricity theft anomalies. For effective electricity theft inspection, an electricity theft detection method based on Minimal Gated Memory（MGM） network combined Adaptive Synthesis (ADASYN) sampling and Decision Tree (DT) is proposed in this paper. The proposed method considers that the number of electricity theft consumers is far less than that of honest consumers, and the electricity theft detection is a problem of data imbalance. From the perspective of data, ADASYN sampling method is selected to process the sample data of electricity theft consumers. In order to detect electricity theft effectively, the DT is further introduced for feature selection, and combined with the newly proposed MGM network to distinguish honest consumers and electricity theft consumers. Six forms of electricity theft attacks are introduced in this research. Based on real data from smart meters in Ireland, two experiments are designed to demonstrate the effectiveness of the proposed method. The results show that our proposed method perform the best in Precision, Recall, F1-score and AUC when detecting electricity theft. The method proposed in this research can provide a new idea and approach for the detection of electricity theft and maintain the electricity supply quality and safety of smart grid.

A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework

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.

Correlation-driven multi-level learning for anomaly detection on multiple energy sources

Advanced metering infrastructure (AMI) has been widely used as an intelligent energy consumption measurement system. Electric power was the representative energy source collected by AMI; most existing studies to detect abnormal energy consumption have focused on a single energy source, i.e., power. Recently, other energy sources such as water, gas, and heating have also been actively collected. As a result, it is necessary to develop a unified methodology for anomaly detection across multiple energy sources; however, research efforts have rarely been made to tackle this issue. In this study, we propose a new correlation-driven multi-level learning model for anomaly detection on multiple energy sources. The distinguishing property of the model incorporates multiple energy sources in multi-levels based on the strength of the correlations between them. Our model is scalable to integrate arbitrary new energy sources, with further performance improvement, considering both correlated and non-correlated sources. Through extensive experiments on real-world datasets consisting of three to five energy sources, we demonstrate that the proposed model clearly outperforms the existing multimodal learning and recent time-series anomaly detection models and makes further performance improvement as more energy sources are integrated, showing the scalability of the proposed model.

Incorporating quantum key distribution and reinforcement learning for secure and efficient smart grid advanced metering infrastructure

Incorporating quantum key distribution and reinforcement learning for secure and efficient smart grid advanced metering infrastructure

Novel AMI in Zigbee Satellite Network Based on Heterogeneous Wireless Sensor Network for Global Machine-to-Machine Connectivity

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project’s scheduling aligns with the enterprise’s sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses.

Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure.

DESIGN OF SECLUDED ENERGY METER FOR TAMPERING AND UNAUTHORIZED ACCESS DETECTION IN SMART GRID

This paper deals with an energy metering system for electricity monitoring for each and every household that is different from the conventional energy meter. This proposed design is meant to be a better alternative for an efficient and more secure line for energy usage by the consumer without any illegal access to their connections so that they don’t have to pay for something they aren’t accountable for in the first place and protect the supplier/provider, which is the government, from incurring any losses from the theft as well as from customers who tamper with the meter values. These things are vital in today’s world. So,we are taking this into account and designing a smart meter with the IoT technology for monitoring data values at the tip of our fingers, readily available at any moment. It serves many purposes, from the monitoring of energy consumption to easily using mobile phones to work on managing the energy usage according to their budget. It is also easy for the supplier to monitor without manually inspecting the location. The WiFi module ESP8266 is used for the purpose of transmitting the energy data to the server. This paper also deals with a smart grid system in which energy meters are used as a two-way communication system as opposed to conventional meters with one-way communication, which will be a more efficient system. This IoT is good in terms of its development towards an easy and advanced metering infrastructure, which also needs secure communication between the supplier and consumer to protect that data.

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.

Optimization of low voltage distribution network configuration using forecasts based on Advanced Metering Infrastructure data

Optimization of low voltage distribution network configuration using forecasts based on Advanced Metering Infrastructure data

Electricity Theft Detection Using Rule-Based Machine Leaning (rML) Approach

Since electricity theft affects non-technical losses (NTLs) in power distribution systems, power companies are genuinely quite concerned about it. Power companies can use the information gathered by Advanced Metering Infrastructure (AMI) to create data-driven, machine learning-based approaches for Electricity Theft Detection (ETD) in order to solve this problem. The majority of data-driven methods for detecting power theft do take usage trends into account while doing their analyses. Even though consumption-based models have been applied extensively to the detection of power theft, it can be difficult to reliably identify theft instances based only on patterns of usage. In this paper, a novel rule-based combined machine learning (rML) technique is developed for power theft detection to address the drawbacks of systems that rely just on consumption patterns. This approach makes use of the load profiles of energy users to establish rules, identify the rule or rules that apply to certain situations, and classify the cases as either legitimate or fraudulent. The UEDAS smart business power consumption dataset's real-world data is used to assess the performance of the suggested technique. Our technique is an innovation in theft detection that combines years of intensive theft tracking with the use of rule-based systems as feature spaces for traditional machine learning models. With an astounding 93% recall rate for the rule-based feature space combination of the random forest classifier, this novel approach has produced outstanding results. The acquired results show a noteworthy accomplishment in the field of fraud detection, successfully detecting fraudulent consumers 77% of the time during on-site examination.

Detection and confirmation of electricity thefts in Advanced Metering Infrastructure by Long Short-Term Memory and fuzzy inference system models

The successful implementation of Smart Grids heavily relies on energy efficiency, particularly through the Advanced Metering Infrastructure (AMI) and Smart Electricity Meters (SEM). However, cyber-attacks pose a threat to SEM, with electricity theft being a primary motivation. Despite the valuable data provided by SEM for analytical purposes, existing methods to identify theft involve cumbersome and costly on-site inspections. This research proposes an electricity theft detection model using the Long Short-Term Memory (LSTM) network. The model employs a collective anomaly approach, defining prediction errors through a threshold and forecast horizon. Suspicious consumption profiles are analysed, and a fuzzy inference system (FIS) implemented in MATLAB 2021b is used to model security risks based on these profiles. The study utilizes energy consumption data from four diverse consumer profiles (consumers 1, 2, 3, and 4) to develop consumer-specific LSTM models for detection and an FIS model for confirmation. Tampered consumer data is identified and confirmed based on selected AMI parameters. While all consumers exhibit suspicious profiles at times, only consumers 2 and 3 are confirmed as engaging in electricity theft. This research provides a robust approach to detecting and verifying fraudulent consumption profiles within the context of AMI, offering a more reliable dimension to theft detection and confirmation.

A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure

In the advanced metering infrastructure (AMI), impersonation, eavesdropping, man-in-the-middle and other attacks occur in the process of communication between entities through public channels, which will lead to the leakage of user privacy or the incorrect issuance of control instructions, resulting in economic losses and even power system operation failures. In view of this situation, we design a lightweight key agreement scheme based on a chaotic map for the AMI. We use the chaotic map to replace the time-consuming bilinear pairing and elliptic curve method and establish a secure communication channel between legal entities. In addition, we also design a multicast key generation mechanism for message transmission in AMI. The security analysis proves the security of the proposed scheme in the random oracle model, which can meet the security characteristics of anonymity and forward secrecy, and can effectively resist common attacks such as impersonation, replay and man-in-the-middle. The performance analysis results show that the proposed scheme requires lower computational and communication costs than related schemes, so it is more suitable for AMI scenarios with limited resources.

Smart Grid Monitoring: Enhancing Reliability and Efficiency in Energy Distribution

Smart grid has been replaced with traditional electrical power grid with its various technologies. In today’s world it has emerged in as solution of increasing demand. This technology has changed the way electricity is produced, transferred, and consumed. This technology benefits in advanced monitoring systems to improve the reliability and efficiency of energy distribution. This abstract provides an overview of smart grid monitoring, highlighting its key components, benefits, and challenges. Smart grid monitoring comprises of different technologies and techniques. Information communication technology helps the grid in collection of data from various consumers. These technologies involve advanced sensors, Advanced Metering Infrastructure (AMI), data analytics. It has multidimensional benefits. It enhances grid reliability by enabling rapid detection and response to the faults. It provides consumers with detailed information about their energy usage, adapting more awaking consumption habits. This contributes to the overall reduction of greenhouse gas emissions and environmental impact. In conclusion, it represents a crucial advancement in the energy sector, transforming the traditional grid into an intelligent grid. The continued development and deployment of smart grid monitoring technologies promise a brighter and more sustainable energy future.

A Levenberg–Marquardt algorithm‐based line parameters identification method for distribution network considering multisource measurement

AbstractThe uncertainty brought by the integration of distributed generations in distribution networks poses a higher demand for situation awareness in the distribution network. Accurate identification of distribution network line parameters is of great significance for the operation and control of the distribution network. This paper proposes a method for identifying distribution network line parameters considering multisource measurement. Firstly, the initial values of conductivity and susceptance are obtained through linear regression and converted into resistance and reactance, respectively. Then, based on the series parallel connection of the network end branches, a non‐linear function about resistance reactance is derived. By combining the measurement data of micro phasor measurement unit and advanced metering infrastructure at multiple times, the non‐linear measurement equation of the line is established, and the Levenberg–Marquardt algorithm is used to solve the non‐linear function, thus achieving the identification of distribution line parameters. The case study demonstrates the accuracy and effectiveness of the proposed method.

Review to Smart Grids

This paper is the result of an extensive study, comparison, and analysis of academic articles and research related to smart grids and the solutions they offer to enhance the efficiency, reliability, and sustainability of modern electrical power systems. It presents a comprehensive analysis of the progress made in recent years in smart grid technologies, including architecture, communication infrastructure, control strategies, and the integration of renewable energy sources. Additionally, the benefits and advantages of adopting smart grids are explored and presented, along with the challenges arising from their use. There is also a reference to the future prospects of using smart grids and how they can impact human life. Following this, there is an analysis of the two most critical elements of smart grids, namely Energy Management Systems (EMS) and Advanced Metering Infrastructure (AMI). Finally, some conclusions are drawn regarding the use of smart grid systems, and a personal opinion is expressed.

Optimize AMI With an Asset Management Program

The Town of Surf City, N.C., is located on Topsail Island, a barrier island situated south of the Outer Banks. Its clean and uncrowded public beaches, family‐friendly atmosphere, wide range of amenities and services, and mild climate make the town a wonderful place to live and a popular destination for vacationers. The town recently implemented advanced metering infrastructure (AMI) technology to modernize—and futureproof—its water system.

A fog-edge-enabled intrusion detection system for smart grids

The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant privacy risks and extends the time required for intrusion detection. Training a high-quality IDS using Artificial Intelligence (AI) technologies on a single entity becomes particularly challenging when dealing with vast amounts of distributed data across the network. To address these concerns, this paper presents a novel approach: a fog-edge-enabled Support Vector Machine (SVM)-based federated learning (FL) IDS for SGs. FL is an AI technique for training Edge devices. In this system, only learning parameters are shared with the global model, ensuring the utmost data privacy while enabling collaborative learning to develop a high-quality IDS model. The test and validation results obtained from this proposed model demonstrate its superiority over existing methods, achieving an impressive percentage improvement of 4.17% accuracy, 13.19% recall, 9.63% precision, 13.19% F1 score when evaluated using the NSL-KDD dataset. Furthermore, the model performed exceptionally well on the CICIDS2017 dataset, with improved accuracy, precision, recall, and F1 scores reaching 6.03%, 6.03%, 7.57%, and 7.08%, respectively. This novel approach enhances intrusion detection accuracy and safeguards user data and privacy in SG systems, making it a significant advancement in the field.

Classification of intrusion cyber‐attacks in smart power grids using deep ensemble learning with metaheuristic‐based optimization

AbstractThe most advanced power grid design, known as a ‘smart power grid’, integrates information and communication technology (ICT) with a conventional grid system to enable remote management of electricity distribution. The intelligent cyber‐physical architecture enables bidirectional, real‐time data sharing between electricity suppliers and consumers through smart meters and advanced metering infrastructure (AMI). Data protection issues, such as data tampering, firmware exploitation, and the leakage of sensitive information arise due to the smart power grid's substantial reliance on ICT. To maintain reliable and efficient power distribution, these issues must be identified and resolved quickly. Intrusion detection is essential for providing secure services and alerting system administrators in the case of adversary attacks. This paper proposes an intrusion classification scheme that identifies several types of cyber attacks on modern smart power grids. Grey‐Wolf metaheuristic optimization‐based feature selection is used to learn non‐linear, overlapping, and complex electrical grid properties. An extended deep‐stacked ensemble technique is advanced by putting predictions from weak learners (CNNs) into a meta‐learner (MLP). The outcomes of this approach are explained and confirmed using explainable AI (XAI). The publicly available dataset from Mississippi State University and Oak Ridge National Laboratory (MSU‐ORNL) is used to conduct experiments. The experimental results show that the proposed method achieved a peak accuracy of 96.6% while scrutinizing the original MSU‐ORNL data feature set and a maximum accuracy of 99% when analysing the selected feature set. Therefore, the proposed intrusion classification scheme may protect smart power grid systems against cyber security attacks.