Smart Grid Features Research Articles

Introducing edge intelligence to smart meters via federated split learning

The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart enough. They can only perform basic data collection and communication functions and cannot carry out on-device intelligent data analytics due to hardware constraints in terms of memory, computation, and communication capacity. Moreover, privacy concerns have hindered the utilization of data from distributed smart meters. Here, we present an end-edge-cloud federated split learning framework to enable collaborative model training on resource-constrained smart meters with the assistance of edge and cloud servers in a resource-efficient and privacy-enhancing manner. The proposed method is validated on a hardware platform to conduct building and household load forecasting on smart meters that only have 192 KB of static random-access memory (SRAM). We show that the proposed method can reduce the memory footprint by 95.5%, the training time by 94.8%, and the communication burden by 50% under the distributed learning framework and can achieve comparable or superior forecasting accuracy to that of conventional methods trained on high-capacity servers.

Security in advanced metering infrastructures: Lightweight cryptography

Abstract Smart grids are designed to revolutionize the energy sector by creating a smarter, more efficient and reliable power supply network. The rise of smart grids is a response to the need for a more comprehensive and sophisticated energy system that caters to the needs of homes and businesses. Key features of smart grids include the integration of renewable energy sources, decentralized generation and advanced distribution networks. At the heart of smart grids is a sophisticated metering system, consisting of intelligent electronic devices that measure energy consumption and enable two-way communication between the utility company and the consumer. The information exchanged via these devices is critical, as it includes sensitive data such as energy consumption patterns and billing information. With the rise of the Internet of Things (IoT) and Industry 4.0, the security of this information is of utmost importance. This paper delves into the security challenges faced by advanced metering infrastructure (AMI) and highlights the crucial role played by cryptography in ensuring the secure exchange of sensitive information. Cryptographic protocols such as encryption, authentication and digital signatures are essential components of a secure smart grid infrastructure. These protocols work together to ensure the confidentiality, integrity and authenticity of the information exchanged between the utility company and the consumer.

Optimizing smart grid performance: A stochastic approach to renewable energy integration

Smart grids offer numerous possibilities for developing and executing sustainable and efficient electricity supply chains that exhibit increased resilience to disturbances. The exploration of the Electric Supply Chain Network (ESCND) design problem using smart grids has been limited. This article aims to tackle this challenge through the application of a robust multi-objective optimization approach, encompassing three economic goals (maximizing profit), environmental goals (minimizing greenhouse gas emissions), and resilience goals (maximizing network resilience). Additionally, the optimization process takes into account the dual objectives of maximizing efficiency and minimizing energy loss by incorporating relevant cost considerations. The proposed methodology incorporates distinctive features of smart grids, such as demandside management programs, microgrid structures, bidirectional distribution lines, and consumer–supplier nodes. It addresses various interconnected decisions, including location placement, capacity expansion, load allocation, and pricing. To solve the formulated model, a potent combination of multi-objective optimization methods based on cutting-plane algorithms and AUGMECON2 is introduced. Subsequently, the proposed model and solution approach are applied to a practical case study, and the obtained results are comprehensively examined. The findings suggest that, despite potential contradictions between economic and environmental objectives, the implementation of smart grids can concurrently improve environmental performance and network flexibility.

Data security of machine learning applied in low-carbon smart grid: A formal model for the physics-constrained robustness

Towards the low-carbon goal, a smart grid features remote connection, data sharing, and cyber–physical integration to increase the flexibility of energy supplies, to reduce electricity wastage, and to enhance safety under intermittent renewables. In this context, machine learning (ML) is increasingly employed in smart grids to solve tasks that require deep data analytics. However, it is well recognized that ML models are vulnerable to adversarial examples that might be contained in open shared data, which creates opportunities for potential attackers to launch cyberattacks. In particular, system operating states acting as critical inputs to ML-based smart grid applications (MLsgAPPs) can be manipulated by malicious actors to mislead the system operator into making wrong decisions that in turn may cause major blackouts and other damaging cascading events. It is thus imperative to advance vulnerability assessment for MLsgAPPs.In this paper, we propose a physics-constrained robustness evaluation framework for MLsgAPPs, which is a first attempt of using tree ensemble (TE) models. Unlike adversarial attacks against prior studied image-classification tasks, adversarial examples against TE-based smart grid applications (TEsgAPPs) must not only cheat human intuition but also follow laws of physics and bypass error-checking mechanisms for power systems. Thus, formulation of the traditional robustness evaluation problem must be reconsidered to account for domain-specific misclassification, physical limit, power balance, and error-bypass constraints, in order to obtain tight lower bounds of the magnitudes of tolerated adversarial perturbations. We adopt a formal modeling approach to construct a discrete tree structure and to specify the model evasion conditions. We propose an efficient robustness evaluation method by transforming variables considering ℓ1 and ℓ∞ norms, followed by a generalization that expands the variable space to admit other types of norms. Using TE-based power system security assessment as an example, comprehensive simulations are conducted for evaluating the physics-constrained robustness under different TE models and model parameters.

Adaptation of powerline communications-based smart metering deployments with IoT cloud platform

<span lang="EN-US">The necessity of energy management and optimization through smart devices has an essential role in sustainable energy. Smart grid features and cutting-edge technologies are progressively integrated into traditional electricity networks. One of these features is the interference between power line communications and IoT. The introduction and deployment of these grids are mainly focused on the development of the field of smart metering. A new proposed module for smart meter design within the existing infrastructure grid system using power line communication (PLC) is presented. The system will include a transmitter with a microcontroller (MCU) and numerous sensors, as well as communication channels that include PLC and an in-house powerline network, and a receiver with an MCU. The suggested system interacts with the IoT system, characterized by a free web interface showing the data directly in real-time. Based on real-world experience, this paper develops guidelines for various aspects of PLC Smart Metering network deployment via the cloud environment. The practical result of packet losses is about 0, 1, or 2 characters of received data, and the time difference between transmitter and receiver is about 5000 milliseconds for a fixed transmission line.</span>

Self-healing service for lowest load shedding in distribution networks using responsive loads and subcontractor feeder

One of the salient attractive features of smart grids is their self-healing capability. The bus voltage and branch current management method is presented as a multi-agent control framework using voltage-load sensitivity matrices (VLSM) with the cooperation of responsive loads and subcontractor feeder line at the restoration interval. Accordingly, this paper attempts to model the optimal coordination between two control levels, and calculate the available remaining and increasing capacity (ARIC) for both unresponsive and responsive load buses. The functions of the zone agents in the first control layer are: performing switching operations, executing the active/reactive load-setting commands, and monitoring. The functions of the central control agent in the second control layer are: negotiating with the zone agents, fault location detection, tie line selection, calculation the voltage of buses and the flow of lines, determine optimal consumption for each load, regulation of capacitor banks, determine the maximum power consumption capacity of the entire distribution network, and decision-making. And finally, a numerical experimental model conducted to verify the effectiveness of the proposed method.

Advanced Metering Infrastructure for Distribution Planning and Operation: Closing the loop on grid-edge visibility

In the recent history of electric utilities, the potential to have visibility of the grid edge is gaining significance for the reliable planning and operation of a clean energy smart grid. Before the recent large-scale customer adoption of distributed energy resources (DERs), distribution networks were planned with a simpler fit-and-forget philosophy. The importance of customer-sited DERs to achieve climate change goals marks a major shift for utility operations. Changes to traditional fit-and-forget planning paradigms require better availability of grid-edge data. Smart metering is an enterprise-wide tool that is enabling visibility when and where it has been most needed. As of 2020, the rollout of smart metering, or advanced metering infrastructure (AMI), had reached more than 100 million meters in the United States, and nearly half of all electricity customers are now equipped with a smart meter ( <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> ). Smart meters are quickly becoming a ubiquitous data capture feature of smart grids. AMI enables utilities to record and measure electricity usage and power-flow metrics at a minimum of hourly intervals and at least once a day. At a minimum, AMI enables interval metering, automatic meter reading enabling accurate and time-interval billing, and the ability to provide feedback on customer energy consumption.

Market-Based Resource Allocation of Distributed Cloud Computing Services: Virtual Energy Storage Systems

The cloud-based application is a major developing feature of smart grids. Apart from centralized Internet data centers (IDCs), distributed cloud resources (CRs) can also provide cloud computing services with low latency and high reliability. For the distributed cloud computing, CRs aggregators (CRAs) will integrate the distributed idle computing resources, which are dispersed energy consumers in the system, to form virtual IDCs. This article presents a market-based computing resource allocation method for distributed cloud computing services. The computing resource allocation refers to the approach to allocating the computing workloads to different CRs. First, the batch workload scheduling (BWS)-based virtual energy storage system (VESS) model and thermal inertia (TI)-based VESS model are proposed to help CRAs better aggregate the distributed CRs and characterize the energy consumption flexibility of the virtual IDCs. Then, the energy trading behavior of the CRAs in the transactive energy market is modeled in the resource allocation process. Case studies are conducted on a 55-bus electricity system. It can be found that energy consumption costs can be reduced by applying the proposed methodology, and revenues from providing cloud computing services can be increased.

Smart Energy Management System: Design of a Smart Grid Test Bench for Educational Purposes

The presented article aims to design an educational test bench setup for smart grids and renewable energies with multiple features and techniques used in a microgrid. The test bench is designed for students, laboratory engineers, and researchers, which enables electrical microgrid system studies and testing of new, advanced control algorithms to optimize the energy efficiency. The idea behind this work is to design hybrid energy sources, such as wind power, solar photovoltaic power, hydroelectric power, hydrogen energy, and different types of energy storage systems such as batteries, pumped storage, and flywheel, integrating different electrical loads. The user can visualize the state of the components of each emulated scenario through an open-source software that interacts and communicates using OPC Unified Architecture protocol. The researchers can test and validate new solutions to manage the energy behavior in the grid using machine learning and optimization algorithms integrated in the software in form of blocks that can be modified and improved, and then simulate the results. A model-based system of engineering is provided, which describes the different requirements and case studies of the designed test bench, respecting the open-source software and the frugal innovation features in which there is use of low-cost hardware and open-source software. The users obtain the opportunity to add new sources and new loads, change software platforms, and communicate with other simulators and equipment. The students can understand the different features of smart grids, such as defect classification, energy forecasting, energy optimization, and basics of production, transmission, and consumption.

A Unified Topology for the Integration of Electric Vehicle, Renewable Energy Source, and Active Filtering for the Power Quality Improvement of the Electrical Power Grid: An Experimental Validation

Electrical power grids are facing challenges concerning new linked technologies and associated contributions of power electronics, both regarding innovative topologies of power converters and advanced power management algorithms. Additionally, technologies related to renewables and electric mobility have several points in common, especially about the interface with the power grid, which allows to foresee a convergence for unified solutions in the power grid interface, without jeopardizing the functionalities and added values of each technology. Encompassing this purpose, this paper presents a unified topology, based on a three-phase structure, which, in addition to a collaborative operation with the power grid targeting the compensation of power quality problems, also enables the integration of a renewable energy source and an electric vehicle. The main contribution of this paper resides in the fact that only an interface with the power grid is necessary to involve three central features of smart grids: renewables, electric mobility, and power quality. Overall, the unified topology presents a four-quadrant structure, both in the perspective of AC and DC interfaces, offering multiple functionalities, mainly to the power grid. In the AC interface, the structure operates in interleaved mode, while in the DC interface, the structure operates in multilevel mode. The global control algorithm is presented, covering the interconnection between the mentioned technologies, as well as the details of implementation of the individual control algorithms regarding each interface. A laboratory prototype, connected to a three-phase 400 V-50 Hz power grid, was used to obtain an experimental validation for a maximum operating power of 12.5 kW, corroborating the essential advantage characteristics and the correct functioning of the presented unified topology.

A multi-agent system for distribution network restoration in future smart grids

A main feature of future smart grids is their self-healing capability, which allows fault location, isolation and service restoration to be done automatically. Restoration is a combinatorial non-linear optimization problem for which conventional mathematical programming techniques and heuristic methods become computationally very costly. This paper proposes a decentralized restoration method that avoids the computational burden of fully centralized optimization techniques while still providing good solutions and avoiding single points of failure. By pre-calculating the amount of power that can be transferred from other feeders without violating their limits, restoration within the outage area is converted into a binary integer linear optimization problem which can be easily solved. To prove its effectiveness, different case scenarios were tested on 14-bus and 70-bus systems. The multi-agent system was implemented in JAVA Agent Development Framework (JADE) and MATLAB was employed for the main algorithms within each agent. The outcomes were compared to those of previously proposed centralized and decentralized approaches, proving that the method proposed shows better computational efficiency than the centralized approach and better results than the decentralized approach. Therefore, the proposed algorithm provides an overall improved solution.

Global sustainability, innovation and governance dynamics of national smart electricity meter transitions

Smart electricity meters are a central feature of any future smart grid, and therefore represent a rapid and significant household energy transition, growing by our calculations from less than 23.5 million smart meters in 2010 to an estimated 729.1 million in 2019, a decadal growth rate of 3013%. What are the varying economic, governance, and energy and climate sustainability aspects associated with the diffusion of smart meters for electricity? What lessons can be learned from the ongoing rollouts of smart meters around the world? Based on an original dataset twice as comprehensive as the current state of the art, this study examines smart meter deployment across 41 national programs and 61 subnational programs that collectively target 1.49 billion installations involving 47 countries. In addition to rates of adoption and the relative influence of factors such as technology costs, we examine adoption requirements, modes of information provision, patterns of incumbency and management, behavioral changes and energy savings, emissions reductions, policies, and links to other low-carbon transitions such as energy efficiency or renewable energy. We identify numerous weak spots in the literature, notably the lack of harmonized datasets as well as inconsistent scope and quality within national cost-benefit analyses of smart meter programs. Most smart meters have a lifetime of only 20 years, leading to future challenges concerning repair, care, and waste. National-scale programs (notably China) account for a far larger number of installations than subnational ones, and national scale programs also install smart meters more affordably, i.e. with lower general costs. Finally, the transformative effect of smart meters may be oversold, and we find that smart electricity meters are a technology that is complementary, rather than disruptive or transformative, one that largely does not challenge the dominant practices and roles of electricity suppliers, firms, or network operators.

Real-time energy data compression strategy for reducing data traffic based on smart grid AMI networks

In the future, the Internet of things (IoT) may provide huge volumes of data. Smart grids are a class of IoT electricity distribution systems that can control bidirectional energy flows between consumers and service providers (Barman et al. “IOT Based Smart Energy Meter for Efficient Energy Utilization in Smart Grid”, 978-1-5386-4769-1 1831.00, 2018 IEEE). A typical smart grid features an advanced metering infrastructure (AMI), which automatically collects meter data from widely distributed sensors. A utility company that intends to use AMI must deploy smart meters, data concentrator units, and a meter data management system (MDMS). Concentrators collect ubiquitous messages from smart meters and transmit aggregated data to their MDMS. Although ubiquitous messages may enhance the efficiency of some electricity grids, any excessive volume of messages causes data congestion. This research considers the Reference Energy Disaggregation Data Set. Our proposed algorithm can compress meter data efficiently. Our contributions are as follows: First, thus far, numerous researchers have attempted to address smart grid problems with AMI systems, and here, we provide a relatively complete review of these attempts. Second, this paper presents a strategy to analyze this problem and propose a compression algorithm to compress meter data. This proposed algorithm combines several existing compression algorithms and operates from 2 to 10% more efficiently than previously published algorithms.

Prediction of electrical energy consumption based on machine learning technique

The forecast of electricity demand in recent years is becoming increasingly relevant because of market deregulation and the introduction of renewable resources. To meet the emerging challenges, advanced intelligent models are built to ensure precise power forecasts for multi-time horizons. The use of intelligent forecasting algorithms is a key feature of smart grids and an effective tool of resolving uncertainty for better cost and energy efficiency decisions like scheduling the generations, reliability and power optimization of the system, and economic smart grid operations. However, prediction accuracy in forecasting algorithms is highly demanded since many important activities of power operators like load dispatch depend upon the short-term forecast. This paper proposes a model for the estimation of the consumption of electricity in Agartala, Tripura in India, which can accurately predict the next 24 h of load with and estimation of load for 1 week to 1 month. A number of specific characteristics in the city have been analysed in order to extract variables that could affect the pattern of electricity consumption directly. In addition, the present paper shows the way to significantly improve the accuracy of the prediction through ensemble machine learning process. We demonstrated the performance of individual Random forest and XGBoost along with their ensemble. The RF and XGBoost ensemble obtained an accuracy with an improvement of 15–29%. The analyses or findings also provide interesting results in connection with energy consumption.

Cyber–physical security for on‐going smart grid initiatives: a survey

The smart grid is an upgraded concept of electricity network with tight coupling among information, control, and bi-directional communication technologies. Along with the silent features of the on-going smart grid, cyber–physical security appears to be a deep concern due to its significant dependence on sensing technologies, complex networks of computers, intelligence, and wide-area communication infrastructures. Moreover, the smart grid is an extensive critical infrastructure and vulnerable to coordinated cyber–physical attacks. As a result, cyber–physical attacks cause significant threats to the confidentiality and integrity of the power data, including power outages, cascading failures, and unnecessary expenditure. In this study, security issues of smart grid, including cyber–physical interdependency, attack varieties, detection methods, requirements, standards, challenges, and future prospects, are taken into consideration for both cyber and physical systems, aiming to provide an extensive understanding of vulnerabilities and solutions for the smart power grid. By revealing the inherent features of cyber–physical security in the smart grid, this survey study is addressed to facilitate future research in these two areas.

Micro Smart Micro-grid and Its Cyber Security Aspects in a Port Infrastructure

Maritime ports are intensive energy areas with a plenty of electrical systems that require an average power of many tens of megawatts (MW). Competitiveness, profits, reduction of pollution, reliability of operations, carbon emission trading are important energy related considerations for any port authority. Current technology allows the deployment of a local micro-grid of the size of tenths of MW, capable of islanded operation in case of emergency and to grant an increasing energy independency. Ownership of the grid permits a large flexibility on prices of energy sold inside the port, trading on local electric market and reduction of pollution. Renewable energy generation has a large impact on costs since features a low marginal cost. Unfortunately the smart grid is a critical asset within the port infrastructure and its intelligence is a high-level target for cyber-attacks. Such attacks are often based on malicious software (malware), which makes use of a controlling entity on the network to coordinate and propagate. In this document, we will outline some features of a port smart grid and typical characteristics of cyber-attacks including potential ways to recognize it and suggestion for effective countermeasures.

Infrastructure of Sustainable Energy Development in Pakistan: A Review

Pakistan is an energy-resourceful country with vast and untapped renewable energy sources (RESs). The wind, solar, and biomass of the country are practically capable of ending a power sector collapse caused by demand-supply variances. A significant percentage of Pakistan's population resides in rural areas. For rural population, the lack of connection to the mainstream of national development is a direct consequence of frequent power blackouts and, in certain cases, a lack of grid connection altogether. Lucrative features of smart grid are not fully incorporated into the power network yet, but policy-makers are paying attention to increase RES reliance. A comprehensive study describing the renewable energy potential of Pakistan is of importance. This research work attempts to present a collective summary of Pakistan's renewable energy potential. A statistical analysis of the proposed and installed projects in various districts are presented. This paper elaborates the pressing needs of renewable energy integration for resolving Pakistan's energy crisis. Renewable energy projects are acclaimed in this paper for affording higher living standards and better job opportunities than the fossil fuel based industry in Pakistan. Integrating RESs into the national portfolio is guaranteed to offer profound socio-economic benefits to Pakistan's rural population.

Recent Development of Frequency Estimation Methods for Future Smart Grid

The frequency estimated by the Phasor Measurement Unit (PMU) is a critical index of power system status and supports many smart grid applications. The future smart grid features high penetration of renewables and more fast-moving power electronics inverters but raises challenges to the reliable frequency estimation. This article presents three methods to address these challenges. First, an enhanced zero-crossing algorithm was developed to track the fast-changing frequency in system dynamics. Second, we propose a technology that can tolerate the system transient and suppress the outliers. Third, an algorithm was developed to export high time-resolution frequency estimations with minimum computational effort. All of the proposed methods are realized in hardware and compared with classical frequency estimation methods. The testing results indicate that the proposed methods have excellent performance. They can be used in future PMUs and provide reliable and high time resolution data for smart grid applications.

Stochastic Distributed Frequency and Load Sharing Control for Microgrids With Communication Delays

This paper addresses the frequency restoration and accurate load power sharing problem of ac microgrids with massive penetration of distributed generators (DG) under time-delay communication in noisy environments. Existing distributed control methods commonly assume ideal communication among DGs. However, the channels are prone to additive noises and time delays, while each DG obtains noisy and delayed measurements of the states of its neighbors due to environmental noises and communication delays. The proposed distributed cooperative control strategy will achieve frequency restoration and accurate load power sharing among DGs through a sparse communication network subject to time delays and noise disturbances. The theoretical concepts and necessary conditions for stability and robust performance of the proposed distributed cooperative control strategy are outlined by the Lyapunov-Krasovskii functional method and stochastic differential equation theory. Furthermore, the proposed control strategy is implemented through sparse communication networks and thus, satisfies the plug-and-play feature of the future smart grid. Simulation results on an islanded microgrid test system are provided to reveal the effectiveness of the proposed control method to provide accurate proportional power sharing in the MATLAB/SimPowerSystems Toolbox.

NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid

The smart grid features frequent communication of measurements collected at consuming and distributing nodes to other agents in the grid. While this increases grid visibility and improves situation awareness, the sheer volume of such data generated from the geographically vast grid will result in overloading of the communication infrastructure. In this regard, compressing data at the source and communicating compressed measurements has been explored in the literature. However, such techniques rely on the presence of a structure in data that is exploited for compression. In this article we propose the use of Autoencoder for data compression that extracts an appropriate structure from data that then allows for compression. The proposed approach also incorporates nonlinear transformations in the compression mechanism which is likely to improve the compression ratio for the same reconstruction accuracy. The proposed method is applied on four publicly available datasets and results show that the Autoencoder has merit over state-of-the-art Compressive Sensing for high compression ratios. Generalization of Autoencoder models to datasets from different geographical locations is also studied as a distinct feature of the proposed method. The generalizability of models is also improved with transfer learning by adapting pre-trained models to the idiosyncrasies of target dataset.