Smart Grid Research Articles

Security with Wireless Sensor Networks in Smart Grids: A Review

Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on “Cybersecurity with WSN in Smart Grids,” presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles.

An effective ensemble electricity theft detection algorithm for smart grid

AbstractSeveral machine learning and deep learning algorithms have been presented to detect the criminal behaviours in a smart grid environment in recent studies because of many successful results. However, most learning algorithms for the electricity theft detection have their pros and cons; hence, a critical research issue nowadays has been how to develop an effective detection algorithm that leverages the strengths of different learning algorithms. To demonstrate the performance of such an integrated detection model, the algorithm proposed first builds on deep neural networks, a meta‐learner for determining the weights of detection models for the construction of an ensemble detection algorithm and then uses a promising metaheuristic algorithm named search economics to optimise the hyperparameters of the meta‐learner. Experimental results show that the proposed algorithm is able to find better results and outperforms all the other state‐of‐the‐art detection algorithms for electricity theft detection compared in terms of the accuracy, F1‐score, area under the curve of precision‐recall (AUC‐PR), and area under the curve of receiver operating characteristic (AUC‐ROC). Since the results show that the meta‐learner of the proposed algorithm can improve the accuracy of deep learning algorithms, the authors expect that it will be used in other deep learning‐based applications.

A detection model for false data injection attacks in smart grids based on graph spatial features using temporal convolutional neural networks

Due to the intelligence and opening of future generation cyber–physical power systems, smart grids are vulnerable to the emergency of cyber-attacks aiming at the cyber–physical systems. Among these attacks, false data injection (FDI) attacks are particularly concerning in smart grids due to their stealthiness. Attackers can tamper with data transmitted through communication networks, influencing the analysis results of the control center, leading it to make wrong decisions and thereby undermining the stability of smart grids. The inability of traditional bad data detection models to detect false data injection attacks poses huge security risks to smart grids. For this reason, a new attack detection model using spatial–temporal features is proposed. The proposed detection model includes the following two parts: spatial features extraction and temporal convolutional network. First, graph convolutional operations are employed to disentangle the interactions among buses and extract spatial features of measurement; Then, the temporal convolutional network model is used to extract the temporal features. Through these processes, the proposed detection model can effectively identify the injected false data injection attacks in smart grids. The detection performance and robustness of the proposed FDI attack detection model are tested through simulations on IEEE 14-bus and 118-bus grid systems.

Secure smart grid implementation with automatic data integrity attack location prediction and exalted energy theft detection

Smart grid systems use digital communication technology to efficiently monitor and manage electricity distribution. However, there is a need for improve the robustness of smart grid systems. Hence a novel “Secure Smart Grid Implementation with Automatic Data Integrity Attack Location Prediction and Exalted Energy Theft Detection” has been proposed. Current detection methods are not good at identifying data integrity attacks. So a novel Automatic Deterministic Attack Location Prediction Model detects data integrity attacks using a Stacked Watermarking BiGRU-based Deep Deterministic Q network, extracting system matrix and state time series variation. Moreover, Energy theft detection algorithms, which are intended to detect dishonest clients, provide erroneous findings because of linear interpolation processes and randomised near misses, which ignore important data points. Thus a novel Exalted Energy Theft Detector with Secure Encoding is introduced where Amortized Multicode GAN method secures smart grids by detecting energy theft and preventing firmware code modifications, utilising fractional multi-level quantum encoding to identify injection locations. As a result, the proposed model has an accuracy of 97.1%, recall of 96.25%, error rate is 1.36%, F1-score is 96.20%, sensitivity is 96.10%, specificity is 96.10%, MAPE is 0.48%, MSE is 0.0001 and RMSE is 0.017.

OVERVIEW ON IMPLEMENTATION OF SMART GRID TECHNOLOGY

The Elevated energy consumption and a growing need for power in commercial, residential, and industrial settings have drawn researchers to explore novel solutions for the grid of the future. This review provides an overview on smart grid technology and its historical development from its conceptual beginnings to its today real-world implementation (intelligent networks). The review then analyzes the multifaceted impacts of smart grids such as social impact, user impact, security impact and environmental impacts. We also delve into their positive performance metrics, including enhanced reliability, efficiency, and resilience, with specific examples of improved power quality and increased renewable energy integration. Finally, the various challenges and opportunities that smart grids offer. We explore cutting-edge advancements like artificial intelligence, edge computing, & blockchain technology, showcasing their potential to overcome existing barriers and pave the way for a new era of intelligent energy management.

A Review on Privacy Protection Techniques in Smart Grid Applications

: The extensive use of electricity and the increasing number of consumers challenge matching power consumption with the power generated. Having a traditional way of power generation and distribution, power is also widely fetched through renewable energy sources. So, to have improved efficiency and reliable means of the power source, to be able to integrate multiple sources of power generation like PV Cells, Solar Power, and Wind Power into the existing standards of the power source, precise calculations of the power consumption in the multisource environment, provision to scale up the smart and electric vehicle and most importantly, to reduce the carbon emissions, several attempts have been made to convert the traditional grids into smart grids. A tiny step in the smart grid's development is the smart metering infrastructure, in which smart meters are deployed through the consumer end. Through smart meters, it is possible to establish the link, either through wireless media or wired connections, between the consumer and the grid. Once the smart meters are deployed through the Advanced Metering Infrastructure (AMI), the meters remain active round the clock, giving a window to hackers. Through this window, utility bill manipulations, payment transaction information, and other significant data can be accessed by unethical approaches and threaten the consumer's privacy. This review-research paper discusses various methods presented by distinct authors to address the issues related to customer privacy protection in the smart grid.

Communication Technology for Renewable Energy Access Grid System Based on Multi-source Heterogeneous Data Fusion

Abstract Incorporating renewable energy solutions into the power grid is essential for facilitating a shift in energy sources. Ensuring the effectiveness and dependability of communication technologies within smart distribution grids is vital, as these grids serve as the link between end users and the central station, thereby contributing to the grid’s stable functioning and the effective dissemination of energy. This study introduces a framework for communication that tackles the challenges associated with the integration of multi-source, heterogeneous data in smart distribution grids. The framework leverages multi-source heterogeneous data fusion and capitalizes on edge computing to efficiently process and consolidate data from diverse sensors and devices. An optimal weight allocation algorithm is proposed to mitigate data redundancy and to improve the energy efficiency of communication. The paper demonstrates, through theoretical examination and empirical tests, the proposed system’s enhanced communication capabilities, especially in terms of data transmission effectiveness and energy usage optimization. The discussion highlights the advantages of smart distribution networks over conventional communication methods and offers insights for further advancements in communication technologies within this sector.

Multi-swarm multi-tasking ensemble learning for multi-energy demand prediction

The increasing uptake of renewable energy sources leads to more uncertainties of energy scheduling, resulting in more difficulties of energy demand management in smart grid. Accurate forecasting plays a significant role in energy management, and in some cases it may involve predictions of different consumers or geographic regions. Traditionally, these prediction problems have been solved independently, ignoring the potential shared knowledge among them that may help facilitate the overall performance. In this manuscript, we propose a multi-swarm multi-tasking ensemble learning (MSMTEL) framework for solving the energy demand forecasting across multiple cities. The proposed method comprises single-task pretraining, multi-task optimization (MTO), and ensemble learning (EL). For each prediction task, several subtasks are generated based on predefined parameters in the forecasting model. Each subtask utilizes an independent deep neural network (DNN) as the predictor, which is pretrained individually. Subsequently, we modify the dynamic multi-swarm particle swarm optimization (DMS-PSO) as a customized multi-swarm PSO (CMS-PSO) algorithm for implementing MTO. Each subswarm in CMS-PSO focuses on finding the optimal model knowledge (pretrained DNN weights and biases) of source tasks (all subtasks) to be reused by the target subtask. Finally, instead of choosing the best forecasting among the subtasks, results of subtasks over the same energy prediction problem are combined to yield the EL result, wherein weight coefficients determining the contributions of subtasks to EL are optimized by PSO. MSMTEL is evaluated against single-task learning (STL) and MTO to demonstrate its superior performance.

Random subspace ensemble-based detection of false data injection attacks in automatic generation control systems

Automatic Generation Control (AGC) systems in smart grids are increasingly vulnerable to cyber-attacks, particularly False Data Injection (FDI) attacks, due to their reliance on information and communication technologies. These vulnerabilities pose significant threats to the reliable operation of power systems. To address this challenge, this research paper introduces the machine learning (ML) based cyberattack detection technique designed to identify FDI attacks with the highest accuracy proficiently. The study involves a comprehensive analysis of three features: the original discrete signal feature, the cycle-to-cycle-based feature, and the sample-to-sample-based feature. These features are utilized for detecting FDI attacks and distinguishing them from normal load variations. The research meticulously collects data by simulating diverse FDI attacks, including step attacks, pulse attacks, random attacks, and normal load variation cases on an AGC power system. Four ML classifiers are selected and compared for the classification task. The simulation results reveal that the sample-to-sample-based feature proves highly effective in distinguishing FDI attacks compared to the original signal and cycle-to-cycle-based features. Notably, the results indicate that the random subspace ensemble (RaSE) classifier, utilizing sample-to-sample-based features, effectively identifies and classifies all normal and FDI attacks. This research provides valuable insights into the potential of ML techniques for enhancing FDI attack detection in the AGC of power systems. It provides a potential pathway for overcoming the limitations associated with traditional model-based FDI detection methods.

Distributed robust [formula omitted]asso-[formula omitted] based on [formula omitted]ash optimization for smart grid: Guaranteed robustness and stability

The integration of variable renewable energy supplies into smart grid energy management poses several obstacles to system operation. An efficient solution for resource management is essential to ensuring reliable operation. This research presents distributed robust Lasso-model predictive control (D − RLMPC) as a way to handle energy problems in a multi-layer and multi-time frame optimization method. The D − RLMPC is a hierarchical system that integrates a centralized supervisory management (SM) layer for long-term optimization with a distributed coordination management (CM) layer for short-term adaptation to high power fluctuations. The higher layer, known as the SM, is responsible for providing the grid operator with specific operating plans and offering guidance to the bottom layer, known as the CM. The CM is responsible for coordinating the interaction between the centralized optimization goals and the physical power system layer. Furthermore, a distributed extended Kalman filter (DEKF) is used to ascertain the inter-dependencies among subsystems. Next, an iterative approach based on Nash optimization is proposed to get the globally optimum solution of the whole system in a partly distributed manner. The simulation results demonstrate the effectiveness of the proposed control approach, which combines the advantages of centralized and distributed control to provide a comprehensive solution for the grid operating issue. To verify and assess the effectiveness of the suggested approach, the acquired outcomes are compared to those of the centralized robust, distributed robust, and distributedMPC approaches. The simulation findings confirm the practicality of using the suggested system to manage future smart grid assets.

Time Synchronized Power Meters for Advanced Smart Distribution of Energy in Smart Grids

Time Synchronized Power Meters for Advanced Smart Distribution of Energy in Smart Grids

A Self-Adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids

A Self-Adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids

Edge Computing Method for False Data Injection Attack Detection in Electromechanical Transient Simulation Grid

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies.

Preface

In the ever-evolving landscape of technological advancements, the fusion of smart energy systems and the Internet of Things has emerged as a pivotal force driving global sustainability and innovation. With this vision in mind, the 2024 3rd International Conference on Smart Energy and Energy Internet of Things (SEEIoT 2024) was successfully convened from June 21st to 23rd, 2024, in Chengdu, China. SEEIoT 2024 is not only a focused display of the latest research results in smart energy systems and the Internet of Things, but also an important platform to promote the development and application of disciplines and domains related to smart energy systems and the Internet of Things globally. It attracted scholars, experts, researchers and engineers from all over the world, and a total of about 100 delegates participated in the event, including some international delegates, which reflects the internationalization and extensiveness of the fields of smart energy systems and the Internet of Things. The event was packed with a diverse and engaging agenda, comprising keynote speeches, oral presentations, poster sessions, and academic discussions. During the conference, participants had in-depth discussions and exchanges on the latest progress and trends about smart energy systems and the Internet of Things. In the keynote speech session, a number of leading authorities in the field shared their research results and insightful perspectives, which provided participants with valuable academic insights and set the tone for the conference. In the oral presentation and poster sessions, participants gained more opportunities to demonstrate their research results and interacted directly with each other, which promoted the collision of academic ideas and deepened the cooperation and exchange. The Proceedings, a collection of accepted papers from SEEIoT 2024, covers a wide range of aspects such as Smart Grid, Intelligent Transmission and Distribution, Power Grid Dispatching and Automation Control, Power System Condition Monitoring, Energy Management for Microgrids, Ubiquitous Electric Internet of Things, Intelligent Energy Management, etc., which fully demonstrates the latest research trends and practical achievements in the fields of smart energy systems and the Internet of Things. It not only captures the essence of the conference discussions and presentations, but also serves as a valuable resource for researchers, practitioners, and policymakers alike. The successful organization of SEEIoT 2024 not only promotes academic exchanges and technical progress in the fields of smart energy systems and the Internet of Things, but also strengthens international cooperation and ties. And the international influence of the conference has been significantly enhanced, which has injected new vitality and impetus for the future development of relevant domains and industries. Finally, we extend our heartfelt gratitude to all the authors, reviewers, sponsors, organizers, and participants who made SEEIoT 2024 a truly remarkable event. Meanwhile, we also expect that the SEEIoT series of conferences will continue to play an important role in promoting international academic exchanges and technical cooperation, and contribute to the prosperity and development of smart energy systems and the Internet of Things. The Committee of SEEIoT 2024 List of Committee Member is available in this PDF.

Authentication of Smart Grid by Integrating QKD and Blockchain in SCADA Systems

Authentication of Smart Grid by Integrating QKD and Blockchain in SCADA Systems

Analysis of distributed smart grid system on the national grids

Analysis of distributed smart grid system on the national grids

HMLB: Holonic Multi-Agent Approach for Preventive Controllers Load-balancing in SDN-enabled Smart Grid

HMLB: Holonic Multi-Agent Approach for Preventive Controllers Load-balancing in SDN-enabled Smart Grid

Large-scale energy storage for carbon neutrality: thermal energy storage for electrical vehicles

AbstractThermal Energy Storage (TES) systems are pivotal in advancing net-zero energy transitions, particularly in the energy sector, which is a major contributor to climate change due to carbon emissions. In electrical vehicles (EVs), TES systems enhance battery performance and regulate cabin temperatures, thus improving energy efficiency and extending vehicle range. The enhanced efficiency reduces overall energy consumption in EVs. Consequently, this reduction in energy demand can lead to decreased infrastructure needs, minimising the scale and investment required in energy production and distribution systems. Furthermore, the integration of TES with existing infrastructure allows for the simultaneous charging of thermal and electrical energy, leveraging waste heat or renewable energy sources. This not only cuts costs by optimizing resource use but also bolsters sustainability by minimising reliance on non-renewable energy sources. The widespread adoption of TES in EVs could transform these vehicles into nodes within large-scale, distributed energy storage systems, thus supporting smart grid operations and enhancing energy security. Strategic investments and regulatory updates are essential to realise a sustainable, carbon-neutral transportation future, underpinned by robust, cost-efficient infrastructure.

Secured energy data transaction for prosumers under diverse cyberattack scenarios

Due to the increasing use of renewable energy sources and the advancement of smart grid technology, bilateral energy transactions between prosumers have attracted significant interest as a potential solution for efficient and decentralized energy distribution. Prosumers can establish direct energy exchanges by utilizing internet of things (IoT) technologies and arrangements with smart metering capabilities, eliminating the need for middlemen and allowing for more effective use of renewable energy sources. However, these direct energy exchanges between prosumers can be susceptible to cyber-threats, which hinder secure and effective energy transactions while protecting privacy. To enable safe and seamless energy transactions among prosumers and the grid, the cyber-security of IoT devices should be of paramount significance as a possible solution. Therefore, this paper focuses on securing the energy transactions among prosumers facilitated by smart meters. It aims to address potential threats against data integrity, confidentiality, and availability from the prosumers’ point of view and develop a comprehensive framework for securing energy transactions based on artificial intelligence (AI). The proposed structured roadmap not only identifies compromised trading data but also prevents prosumers from reacting to it by replacing the contaminated as well as missing trading data. A comparative analysis on AI-based algorithms indicates that decision tree (DT) outperforms support vector machine (SVM) and multi-layer perceptron (MLP) for the proposed framework to profile the corrupted trading data identification and categorization in order to provide effective outcomes. Additionally, the proposed framework adopts a deep learning (DL)-based model for the replacement of compromised trading data. All the numerical analyses, along with extensive simulation results, justify, the efficacy of the proposed framework.

Smart grid solutions for sustainable photovoltaic-electric vehicle integration in Bangladesh

Smart grid solutions for sustainable photovoltaic-electric vehicle integration in Bangladesh