Smart Metering Systems Research Articles

A Survey on Key Management and Authentication Approaches in Smart Metering Systems

The implementation of the smart grid (SG) and cyber-physical systems (CPS) greatly enhances the safety, reliability, and efficiency of energy production and distribution. Smart grids rely on smart meters (SMs) in converting the power grids (PGs) in a smart and reliable way. However, the proper operation of these systems needs to protect them against attack attempts and unauthorized entities. In this regard, key-management and authentication mechanisms can play a significant role. In this paper, we shed light on the importance of these mechanisms, clarifying the main efforts presented in the context of the literature. First, we address the main intelligent attacks affecting the SGs. Secondly, the main terms of cryptography are addressed. Thirdly, we summarize the common proposed key-management techniques with a suitable critique showing their pros and cons. Fourth, we introduce the effective paradigms of authentication in the state of the art. Fifth, the common two tools for verifying the security and integrity of protocols are presented. Sixth, the relevant research challenges are addressed to achieve trusted smart grids and protect their SMs against attack manipulations and unauthorized entities with a future vision. Accordingly, this survey can facilitate the efforts exerted by interested researchers in this regard.

Energy Contour Forecasting Optimization with Smart Metering in Distribution Power Networks.

Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption metering, as is demonstrated in this work, the extended implementation of smart metering can be used to support many other important functions in the electricity distribution grid. The present paper proposes a new solution that uses a frequency feature-based method of data time-series provided by the smart metering system to estimate the energy contour at distribution level with the aim of improving the quality of the electricity supply service, of reducing the operational costs and improving the quality of electricity measurement and billing services. The main benefit of this approach is determining future energy demand for optimal energy flow in the utility grid, with the main aims of the best long term energy production and acquisition planning, which lead to lowering energy acquisition costs, optimal capacity planning and real-time adaptation to the unpredicted internal or external electricity distribution branch grid demand changes. Additionally, a contribution to better energy production planning, which is a must for future power networks that benefit from an important renewable energy contribution, is intended. The proposed methodology is validated through a case study based on data supplied by a real power grid from a medium sized populated European region that has both economic usage of electricity-industrial or commercial-and household consumption. The analysis performed in the proposed case study reveals the possibility of accurate energy contour forecasting with an acceptable maximum error. Commonly, an error of 1% was obtained and in the case of the exceptional events considered, a maximum 15% error resulted.

РЕШЕНИЯ НПО «МИР» В ОБЛАСТИ ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМ УЧЕТА 0,4 КВ

This article is about basic models of smart metering power systems for 0,4 kV consumers. The authors analyzed Federal Law’s № 522-FL influence on power metering systems structure and paid attention on main problems in system developing. There are typical solutions for smart metering systems for 0,4 kV consumers made by «Mir» Inc, based on three-level and two-level architecture. The authors identified benefits of twolevel smart power metering architecture. They presented the implementation results of pilot project based on GSM-gateway power meter in PAO «Rosseti».

Spectrum Optimization of Low Power Wide Area Network Utilization in Smart Cities

The discovery of this great technology known as Low Power Wide Area Networks (LPWANs) has significantly altered the way we live in a positive way. It is a technology with low power and long range capabilities with impacts that cannot be overemphasized. The impacts range from making cities better with quality living, the environment with quality air and humidity, efficient waste disposal systems, smart metering systems, and traffic free road management to more efficient transportation. Our research focused on the optimization of the unlicensed spectrum occupied by LPWANs using cognitive radio and its utilization in smart cities. In this research, we proposed the spectrum sharing model and carried out our simulations using MATLAB. We discovered that the spectrum sharing model brought about an increase in channel capacity and a decrease in latency, which are basic parameters that determine the performance of the spectrum. After the research, there was a significant improvement in spectrum performance leading to higher quality of service (QoS).

Using smart meters data to model customer water consumption and evaluate customer flow patterns

ABSTRACT The correct determination of water consumption is key to increasing hydraulic modeling reliability. In this context, Smart Metering (SM) is an important concept. The SM systems can provide consumption at a customer level and in small intervals of time. In this scenario, the work focused on a residential condominium, in Brazil, where an SM system was implemented. The work uses data obtained to model consumers’ water consumption and proposes a system to detect metering failures and internal water loss. Three models were proposed: one uses Autoregressive Integrated Moving Average (ARIMA) theory, another uses the Seasonal Autoregressive Integrated Moving Average (SARIMA), and the last uses multivariate linear regression. The average root mean square error of the first model was 14,73, of the second was 13,50, and of the third was 20,99. A metering and consumption system, able to detect internal leaks and others, was also developed.

SMART METERING SYSTEM USING AI TECHNIQUES

The economy of energy suppliers and countries may be significantly impacted by non-technical losses (NTLs) in the electrical distribution system, which mostly involve electrical theft. Non-technical losses include consumer dishonesty, unethical transmission line tapping, and hacking or tampering with energy meters. The smart meter, a crucial component of the smart grid, is anticipated to benefit several stakeholders on the economic, social, and environmental levels. Smart meter data analytics, which deals with data gathering, transmission, processing, and interpretation that benefits all stakeholders, is one of the crucial elements that will define the success of smart meters. An artificial neural network can be created in order to find and identify meter manipulation or energy theft. The outcomes can also be applied to larger real-world systems. Additionally, a communication network that satisfies the security requirements for smart grid communication must be properly chosen and implemented in order to deploy smart meters. This article discusses different problems and difficulties related to the development, implementation, use, and upkeep of the smart metering system. Catchphrases- NTL, losses detection, Smart meter, Advanced Metering Infrastructure, Automatic Meter Reading, Neural Networks, Communication Systems, Energy Consumption Profiles.

Microgrid to smart grid's evolution: Technical challenges, current solutions, and future scopes

AbstractRenewable energy (RE) sources play an important role that not only reduces the pressure on fossil fuels but also produces safe and clean energy by developing the microgrid (MG) technology that fulfills the excess demand for power throughout the world. The technological development and the blessing of information and communication technology converts the MG technology to a smarter one, termed as smart grid (SG) and virtual power plant, by establishing a two‐way communication between the consumers and service provider with the aid of smart metering infrastructure, dynamic pricing scheme, energy management system, smart transmission and distribution system. The transformation of the traditional power system to SG enhances the efficiency, flexibility, resiliency, and robustness of the power system due to the increasing level of cyber security and automation. A directional pathway from conventional to smart power system has been carried out in this paper by addressing the present status of the power system, challenges during the operations, and possible solutions. Initially, the control operation of MG with RE and energy storage system has been investigated which is essential to understand the operation complexity and challenges due to the variable nature of the RE, load dynamics, and uncertainties. But the facilities of end‐user feedback, dynamic pricing scheme, cyber security, and smart metering system are not present in MG technology that hampers the proper regulation of controlling operation, generating and distributing energy as well as energy losses. The development of SG with smart technologies overcomes these problems by proper controlling of power usage. Finally, this paper provides a path to move the MG toward SG by analyzing the technical and critical challenges of smart devices, equipment integration, and their control issues and provides possible solutions to overcome the integration and control challenges for a reliable and sustainable operation.

Data Privacy Preservation and Security in Smart Metering Systems

Smart meters (SMs) can play a key role in monitoring vital aspects of different applications such as smart grids (SG), alternative currents (AC) optimal power flows, adversarial training, time series data, etc. Several practical privacy implementations of SM have been made in the literature, but more studies and testing may be able to further improve efficiency and lower implementation costs. The major objectives of cyberattacks are the loss of data privacy on SM-based SG/power grid (PG) networks and threatening human life. As a result, losing data privacy is very expensive and gradually hurts the national economy. Consequently, employing an efficient trust model against cyberattacks is strictly desired. This paper presents a research pivot for researchers who are interested in security and privacy and shade light on the importance of the SM. We highlight the involved SMs’ features in several applications. Afterward, we focus on the SMs’ vulnerabilities. Then, we consider eleven trust models employed for SM security, which are among the common methodologies utilized for attaining and preserving the data privacy of the data observed by the SMs. Following that, we propose a comparison of the existing solutions for SMs’ data privacy. In addition, valuable recommendations are introduced for the interested scholars, taking into consideration the vital effect of SM protection on disaster management, whether on the level of human lives or the infrastructure level.

Anomaly detection of smart metering system for power management with battery storage system/electric vehicle

AbstractA novel smart metering technique capable of anomaly detection was proposed for real‐time home power management system. Smart meter data generated in real‐time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short‐term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi‐objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

An Energy Efficient Smart Metering System Using Edge Computing in LoRa Network

An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a deep learning based compression-decompression model for reducing the size of the energy time series at the Edge devices. Next, it formulates an optimization problem for finding the suitable compressed energy time series to reduce the energy consumption and delay of the system. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed time series to the operator in the given time. Our simulation and prototype results demonstrate the impact of the parameters of the compression model, network, and the number of smart meters and appliances on delay, energy consumption, and accuracy of the system.

Shaping of converter interference for error rate reduction in PLC based smart metering systems

Smart metering systems often utilize Power Line Communication (PLC) for bidirectional data exchange between smart energy meters and data concentrators. Electromagnetic interference generated by power electronic converters have been found as a main cause of communication errors in PLC data transmission. In this paper novel, modified pseudo-random pulse width modulation technique was developed to significantly reduce PLC frame error rate caused by interference generated by power electronic converters. Moreover, cyber–physical system has been presented as a case study providing high flexibility in controlling the switching rate of change of the modulation. As a general contribution, in the paper has been proposed the concept of electromagnetic interference noise shaping to broaden the possibility of preventing and mitigating non-compatibility scenarios between power electronic converters and telecommunications devices. The experimental results might be useful for system integrators involved in assurance of electromagnetic compatibility of power electronic interfaces and PLC based smart metering systems.

Arduino Interfacing of FLC-based Power Flow Management in Smart Microgrid Systems

There is a rapid increase in power demand which has led to an increase in the need for smaller grids all over the world. The microgrid is such a small power supply system that consists of loads, distributed energy resources, and energy storage systems. With the development of the internet, the power sector has started the development of the newest type of grids, involving a smart technology called smart grids. Smart grids have got smart integration and advanced storage, along with dynamic optimization of grid operation and resources, digital information and control, distributed resources, demand-side resources, energy efficiency resources, and smart metering systems. The developments in intelligent controllers reflect in power flow management also. This paper gives an idea of how to achieve power flow management through a tariff-based Fuzzy Logic Controller (FLC). The control algorithm used here is the k factor-based Icosϕ algorithm, which decides the amount of power shared by microgrids and the grid based on the load demand. The power forecasting of the microgrid with a solar source is performed using ANN. The regression plot and the error histogram obtained show the accuracy of the ANN controller. The simulation of power flow management of the smart microgrid system is tested and analyzed and the proposed system is compared with other existing research. The hardware implementation of the FLC controller with the Arduino interface which is analogous to real-time measurement is explained in detail to compare the simulation results obtained.

Identification of Non-technical Losses in Smart Metering Systems via a Dispersive Flies Algorithm

Identification of Non-technical Losses in Smart Metering Systems via a Dispersive Flies Algorithm

Tackling cybersecurity challenges in the energy and water sectors in the context of the cybersecurity and sectoral regulatory frameworks: the case of smart metering systems in the new digitalised environment

ABSTRACT Critical Infrastructures (CIs) are the backbone of our societal and economic activities. Safeguarding their uninterrupted operation and keeping them safe against different types of threats, from natural disasters to human-induced acts, is of the essence. This analysis focuses on the energy sector mostly and the water sector secondarily as these two sectors are among the CIs that have mainly suffered the consequences of cyber incidents. In this context, the paper examines the applicability of the EU cybersecurity regulatory framework in the energy (including both electricity and gas) and the water sectors, as well as the sector specific initiatives that have been adopted so far to tackle the cybersecurity challenges the two sectors face. Given the expansive deployment of smart technologies and devices in both sectors, the regulatory regime of smart metres and the complications that are associated with their installation and use in terms of privacy and security of the collected data is examined separately. Finally, the analysis attempts to shed some light on the shortcomings of the existing legal framework and to contribute to its further effectiveness by suggesting further steps that could potentially help make the energy and water sectors more cyber resilient in the new threat landscape.

Toward an intelligent community microgrid energy management system based on optimal control schemes

This article investigates a modelization of the energy management strategy of a grid-tied photovoltaic (PV), wind generator, and battery system. The model uses a smart metering system to collect data from various electrical components of a microgrid. The power optimization strategy is modeled under demand response, including time-of-use pricing to create a home's energy flow more efficiently. Two optimal control strategies are developed to coordinate energy expenses, expressed as the cost of minimizing electricity supplied by the utility grid and maximizing the opportunity for energy to sell to the grid. A nonlinear open-loop control and quadratic programming using model predictive control (MPC) are compared to assess the performance of the developed model. The design model takes into consideration the operational cost of batteries and degradation. The importance of the energy storage system is demonstrated in the context of energy conservation and gain on the demand side. MPC's capacity to specify the proper control and forecast future system reaction enables the proposed method to ensure significant daily energy and cost savings from 70.44 to 24.74 $ day−1 in the first scenario (without disturbances) and from 70.44 to 18.24 $ day−1 in the second case (with disturbances). In both methods, the net savings of greenhouse gas are approximately 288.10 (kgCO2-eq) and 338.87 (kgCO2-eq). These show better performance of the MPC compared to the linear model. Highlights Investigate the dynamic performance of an intelligent home energy management scheme. Implement a grid-tied photovoltaic, wind turbine, and energy storage system. Use intelligent metering to collect data from various electrical components of a smart home. Compare the energy saving from an open-loop algorithm and a model predictive control. Assess the topmost benefits of the optimal control method of grid-tied distributed energy resources.

A signaling game-optimization algorithm for residential energy communities implemented at the edge-computing side

The ongoing tremendous progress of IT&C technologies, large-scale integration of smart metering systems, along with smart appliances and energy management systems allow the implementation of various advanced tariff scheme such as: Time-of-Use (ToU), critical peak pricing, real-time tariff (RTT), etc. These new features enhance suppliers to interact with consumers and provide incentives for peak shaving. Thus, the electricity consumers schedule the operation of their appliances to minimize the electricity cost. Game theory is applied to various economic processes, including electricity consumption optimization, leading to significant savings. Hence, in this paper, we propose a Signaling Game model for Optimization (SGO), that is performed at the Edge-computing node and involves multiple optimization steps. An advanced tariff signal is iteratively computed based on the consumption level. The game-optimizing process stops when there is no further improvement of the players’ reward. The simulations using advanced tariffs and a one-year dataset of a small residential community of several houses reveal excellent results in terms of savings, gains, Flat Index (FI) and Peak Average to Ratio (PAR). The gain of the community is about 11% and the savings are approx. 3,357 Euro per year. Compared to the results with a ToU tariff, the results with advanced tariffs (RTT) are significantly improved: FI increased from 0.68 to 0.76 and PAR decreased from 2.3 to 1.86.

PROSUMERS: АN OVERVIEW OF INNOVATIVE MODELS OF INTERACTION BETWEEN SUBJECTS OF THE ELECTRIC POWER INDUSTRY AND END CONSUMERS

Technological changes taking place in Russia and in the world have an impact on many sectors of the economy, including the electric power industry. There has been a tendency for consumers to withdraw from centralized power supply due to a wide range of factors: the spread and cheapening of generation technologies using renewable energy sources, energy storage systems, as well as the development of smart metering systems. The proliferation of digital technologies of Industry 4.0 allows innovative energy technologies to be integrated together.The purpose of this work was to identify and verify the effects generated by the technologies of the fourth industrial revolution in the electric power industry, and the resulting new models of interaction between consumers and energy companies in the electricity market.At the beginning of the study, the effects of the spread of distributed generation (including the use of renewable energy sources), energy storage systems, smart electricity metering systems, as well as digital technologies of industry 4.0 were identified, and the impact of these technologies on changing the nature of consumer interaction and energy companies.Further, the analysis of the main approaches to organizing the interaction of energy companies with a new type of electricity consumers - an active consumer, is carried out, and the key effects from the spread of active consumer models are determined.At the end of the work, industry experts were interviewed with subsequent questionnaires, which made it possible to assess the prospects for deploying active consumer models.

Fuzzy C-Means Clustering Applied to Load Profiling of Industrial Customers

Nowadays, knowing load profiles of different customers in electricity market-oriented operation of power system is very important. Smart metering system provides more than enough information about it. However, even if there is a load measurement on each customer in the system, it is economically unjustified and it is time consuming to conduct a detailed analysis each of them. In this study, unlike literature approaches, the different industrial load profiles are observed. The fuzzy c-means clustering is applied to get typical load curves representing different type of industry. For that purpose, the detailed statistical analysis and calculations of load shape factors of the industrial customers for different period of the year have been carried out. Customer’s 15-min load was collected by developing Advanced Metering Infrastructure in the power system of Bosnia and Herzegovina. According to available data, statistical analysis and applied fuzzy c-means clustering, three typical load profiles for different industrial customers are proposed.

Secure Multi-Dimensional and Multi-Angle Electricity Data Aggregation Scheme for Fog Computing-Based Smart Metering System

Smart Meters (SMs) can regularly report their detailed real time electricity consumption data to utility company, which can improve the balance of electrical load and the efficiency of energy usage. However, the fine-grained electricity data reported by smart meters is easy to disclose the privacy of users. Therefore, data aggregation technology is often used to protect the privacy of users. Currently the existing data aggregation schemes cannot meet the requirement of further fine-grained analysis. In this paper, we propose a multi-dimensional and multi-angle electricity data aggregation scheme for fog computing-based smart metering system. In our proposed scheme, the smart meters use the super-incremental sequence matrix to structure and encrypt their obtained electricity consumption data; the fog nodes regularly collect and aggregate these ciphertexts of electricity consumption data by using the homomorphic Paillier cryptosystem; the control center decrypts the aggregated ciphertext to obtain multi-dimensional and multi-angle electricity data, which is accurate to the types of household appliances and their corresponding electricity angles (energy efficiency indexes). According to the security requirements, we analyze the security of our proposed scheme, which can achieve identity anonymity. Additionally, the experimental results show our scheme is efficient.

Secure and Lightweight Authentication Protocol for Smart Metering System

One of the main advantages of the new power grid over the traditional grid is the intelligent energy management by the customer and the Operator. Energy supply, demand response management, and consumption regulation are only possible with the smart metering system. Smart meters are the main component of that system. Hence, a compromised smart meter or a successful attack against this entity may cause data theft, data falsification, and server/device manipulation. Therefore, Smart grids’ develop-ment and the guarantee of their services are related to the ability to avoid attacks and disasters by ensuring high security. This paper aims to provide a secure and lightweight security protocol that respects the IOT device constraints. The proposition deploys the distributed OTP calculations combined with the Blake2s hash function and the Ascon AEAD cipher to ensure authentication, confidentiality, and integrity. We propose a performance analysis, an informal and a formal security evaluation made by the AVISPA-SPAN tool. Also, we compare the proposed protocol to other similar works. The assessment proves that the proposed protocol is light, valid, secure, and robust against many attacks that threaten the NAN area of the smart metering system, namely MITM and replay attacks.