Advanced mathematical modeling of mitigating security threats in smart grids through deep ensemble model.

A smart grid (SG) is a cutting-edge electrical grid that utilizes digital communication technology and automation to effectively handle electricity consumption, distribution, and generation. It incorporates energy storage systems, smart meters, and renewable energy sources for bidirectional communication and enhanced energy flow between grid modules. Due to their cyberattack vulnerability, SGs need robust safety measures to protect sensitive data, ensure public safety, and maintain a reliable power supply. Robust safety measures, comprising intrusion detection systems (IDSs), are significant to protect against malicious manipulation, unauthorized access, and data breaches in grid operations, confirming the electricity supply chain's integrity, resilience, and reliability. Deep learning (DL) improves intrusion recognition in SGs by effectually analyzing network data, recognizing complex attack patterns, and adjusting to dynamic threats in real-time, thereby strengthening the reliability and resilience of the grid against cyber-attacks. This study develops a novel Mountain Gazelle Optimization with Deep Ensemble Learning based intrusion detection (MGODEL-ID) technique on SG environment. The MGODEL-ID methodology exploits ensemble learning with metaheuristic approaches to identify intrusions in the SG environment. Primarily, the MGODEL-ID approach utilizes Z-score normalization to convert the input data into a uniform format. Besides, the MGODEL-ID approach employs the MGO model for feature subset selection. Meanwhile, the detection of intrusions is performed by an ensemble of three classifiers such as long short-term memory (LSTM), deep autoencoder (DAE), and extreme learning machine (ELM). Eventually, the dung beetle optimizer (DBO) is utilized to tune the hyperparameter tuning of the classifiers. A widespread simulation outcome is made to demonstrate the improved security outcomes of the MGODEL-ID model. The experimental values implied that the MGODEL-ID model performs better than other models.

Applications of blockchain technology in peer-to-peer energy markets and green hydrogen supply chains: a topical review.

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.

A New Semi-Quantum Two-Way Authentication Protocol between Control Centers and Neighborhood Gateways in Smart Grids.

To address the potential threat to the power grid industry posed by quantum computers and ensure the security of bidirectional communication in smart grids, it is imperative to develop quantum-safe authentication protocols. This paper proposes a semi-quantum bidirectional authentication protocol between a control center (CC) and a neighboring gateway (NG). This method uses single photons to facilitate communication between the CC and the NG. Security analysis demonstrates that the protocol can effectively resist common attack methods, including double CNOT attacks, impersonation attacks, interception-measurement-retransmission attacks, and entanglement-measurement attacks. Comparisons with other protocols reveal that this protocol has significant advantages, making it more appealing and practical for real-world applications. Finally, by simulating the protocol on the IBM quantum simulator, this protocol not only validates the theoretical framework but also confirms the practical feasibility of the protocol.

Big data analytics and artificial intelligence aspects for privacy and security concerns for demand response modelling in smart grid: A futuristic approach.

Next generation electrical grid considered as Smart Grid has completely embarked a journey in the present electricity era. This creates a dominant need of machine learning approaches for security aspects at the larger scale for the electrical grid. The need of connectivity and complete communication in the system uses a large amount of data where the involvement of machine learning models with proper frameworks are required. This massive amount of data can be handled by various process of machine learning models by selecting appropriate set of consumers to respond in accordance with demand response modelling, learning the different attributes of the consumers, dynamic pricing schemes, various load forecasting and also data acquisition process with more cost effectiveness. In connected to this process, considering complex smart grid security and privacy based methods becomes a major aspect and there can be potential cyber threats for the consumers and also utility data. The security concerns related to machine learning model exhibits a key factor based on different machine learning algorithms used and needed for the energy application at a future perspective. This work exhibits as a detailed analysis with machine learning models which are considered as cyber physical system model with smart grid. This work also gives a clear understanding towards the potential advantages, limitations of the algorithms in a security aspect and outlines future direction in this very important area and fast-growing approach.

A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion.

In large-scale power systems, accurately detecting and diagnosing the type of faults when they occur in the grid is a challenging problem. The classification performance of most existing grid fault diagnosis methods depends on the richness and reliability of the data, in addition, it is difficult to obtain sufficient feature information from unimodal circuit signals. To address these issues, we propose a deep residual convolutional neural network (DRCNN)-based framework for grid fault diagnosis. First, we design a comprehensive information entropy value (CIEV) evaluation metric that combines fuzzy entropy (FuzEn) and mutual approximation entropy (MutEn) to integrate multiple decomposition subsequences. Then, DRCNN and heterogeneous graph transformer (HGT) are constructed for extracting multimodal features and considering modal variability. In addition, to obtain the implicit information of multimodal features and control the degree of their performance, we propose to incorporate the cross-modal attention fusion (CMAF) mechanism in the synthesis framework. We validate the proposed method on the three-phase transmission line dataset and VSB power line dataset with accuracies of 99.4 % and 99.0 %, respectively. The proposed method also achieves superior performance compared to classical and state-of-the-art methods.

Deep learning-driven hybrid model for short-term load forecasting and smart grid information management.

Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load demand. The study introduces a hybrid method that combines multiple deep learning models, the Gated Recurrent Unit (GRU) is employed to capture long-term dependencies in time series data, while the Temporal Convolutional Network (TCN) efficiently learns patterns and features in load data. Additionally, the attention mechanism is incorporated to automatically focus on the input components most relevant to the load prediction task, further enhancing model performance. According to the experimental evaluation conducted on four public datasets, including GEFCom2014, the proposed algorithm outperforms the baseline models on various metrics such as prediction accuracy, efficiency, and stability. Notably, on the GEFCom2014 dataset, FLOP is reduced by over 48.8%, inference time is shortened by more than 46.7%, and MAPE is improved by 39%. The proposed method significantly enhances the reliability, stability, and cost-effectiveness of smart grids, which facilitates risk assessment optimization and operational planning under the context of information management for smart grid systems.

An efficient neighbor nodes trust tagging model for optimized route detection in smart grids with secure data transmission.

Smart Grids are the control and transmission networks of the future; they allow suppliers and customers to interact in real time to optimize and intelligently manage resources. Although wireless mesh network technology can facilitate these smart functionalities, it is important to address the vulnerabilities and cyber-attack risks that are inherent to it. Smart Grid's reliance on the Internet amplifies security concerns. A number of methods have been proposed to address this issue; while some have made promises, they all need substantial amounts of computational resources. With this technology, channels based on trust can be set up. The security of the system is built utilizing a family relationship-based method, which makes use of measures that may be used to gauge a node's originality. Additionally, the power consumption and signal strength of the node are taken into account. The complexity and extent of the network need the development of new types of smart grid communication. Finding a way to detect and thwart major assaults on routing protocols is another design challenge. Smart grids rely on these protocols in its data system for efficient interchange of renewable energy. A unique secure energy routing mechanism is developed in this proposed model for secure data communication. In the proposed model, a new method for Neighbor Nodes Trust Tagging Model for Optimized Route Detection (NNTT-ORD) is proposed for establishing secure route for data communication in smart grids. The proposed model is compared with the existing model and the results represent that the proposed model route provides a secure environment for data transmission.

Study of smart grid cyber-security, examining architectures, communication networks, cyber-attacks, countermeasure techniques, and challenges.

Smart Grid (SG) technology utilizes advanced network communication and monitoring technologies to manage and regulate electricity generation and transport. However, this increased reliance on technology and connectivity also introduces new vulnerabilities, making SG communication networks susceptible to large-scale attacks. While previous surveys have mainly provided high-level overviews of SG architecture, our analysis goes further by presenting a comprehensive architectural diagram encompassing key SG components and communication links. This holistic view enhances understanding of potential cyber threats and enables systematic cyber risk assessment for SGs. Additionally, we propose a taxonomy of various cyberattack types based on their targets and methods, offering detailed insights into vulnerabilities. Unlike other reviews focused narrowly on protection and detection, our proposed categorization covers all five functions of the National Institute of Standards and Technology cybersecurity framework. This delivers a broad perspective to help organizations implement balanced and robust security. Consequently, we have identified critical research gaps, especially regarding response and recovery mechanisms. This underscores the need for further investigation to bolster SG cybersecurity. These research needs, among others, are highlighted as open issues in our concluding section.

Multilayer cyberattacks identification and classification using machine learning in internet of blockchain (IoBC)-based energy networks.

The world's need for energy is rising due to factors like population growth, economic expansion, and technological breakthroughs. However, there are major consequences when gas and coal are burnt to meet this surge in energy needs. Although these fossil fuels are still essential for meeting energy demands, their combustion releases a large amount of carbon dioxide and other pollutants into the atmosphere. This significantly jeopardizes community health in addition to exacerbating climate change, thus it is essential need to move swiftly to incorporate renewable energy sources by employing advanced information and communication technologies. However, this change brings up several security issues emphasizing the need for innovative cyber threats detection and prevention solutions. Consequently, this study presents bigdata sets obtained from the solar and wind powered distributed energy systems through the blockchain-based energy networks in the smart grid (SG). A hybrid machine learning (HML) model that combines both the Deep Learning (DL) and Long-Short-Term-Memory (LSTM) models characteristics is developed and applied to identify the unique patterns of Denial of Service (DoS) and Distributed Denial of Service (DDoS) cyberattacks in the power generation, transmission, and distribution processes. The presented big datasets are essential and significantly helps in identifying and classifying cyberattacks, leading to predicting the accurate energy systems behavior in the SG.

Lightweight Hash-Based Authentication Protocol for Smart Grids.

Smart grids integrate information and communications technology into the processes of electricity production, transportation, and consumption, thereby enabling interactions between power suppliers and consumers to increase the efficiency of the power grid. To achieve this, smart meters (SMs) are installed in households or buildings to measure electricity usage and allow power suppliers or consumers to monitor and manage it in real time. However, SMs require a secure service to address malicious attacks during memory protection and communication processes and a lightweight communication protocol suitable for devices with computational and communication constraints. This paper proposes an authentication protocol based on a one-way hash function to address these issues. This protocol includes message authentication functions to address message tampering and uses a changing encryption key for secure communication during each transmission. The security and performance analysis of this protocol shows that it can address existing attacks and provides 105,281.67% better computational efficiency than previous methods.

Demand-side load forecasting in smart grids using machine learning techniques.

Electrical load forecasting remains an ongoing challenge due to various factors, such as temperature and weather, which change day by day. In this age of Big Data, efficient handling of data and obtaining valuable information from raw data is crucial. Through the use of IoT devices and smart meters, we can capture data efficiently, whereas traditional methods may struggle with data management. The proposed solution consists of two levels for forecasting. The selected subsets of data are first fed into the "Daily Consumption Electrical Networks" (DCEN) network, which provides valid input to the "Intra Load Forecasting Networks" (ILFN) network. To address overfitting issues, we use classic or conventional neural networks. This research employs a three-tier architecture, which includes the cloud layer, fog layer, and edge servers. The classical state-of-the-art prediction schemes usually employ a two-tier architecture with classical models, which can result in low learning precision and overfitting issues. The proposed approach uses more weather features that were not previously utilized to predict the load. In this study, numerous experiments were conducted and found that support vector regression outperformed other methods. The results obtained were 5.055 for mean absolute percentage error (MAPE), 0.69 for root mean square error (RMSE), 0.37 for normalized mean square error (NRMSE), 0.0072 for mean squared logarithmic error (MSLE), and 0.86 for R2 score values. The experimental findings demonstrate the effectiveness of the proposed method.

Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

A hybrid energy harvesting approach for transmission lines based on triboelectric nanogenerator and micro thermoelectric generator.

To effectively detect faults in transmission lines, monitoring the operating status of these lines is imperative. However, providing power to monitoring devices for transmission line status presents a significant challenge. In this research, a hybrid energy harvesting approach based on micro thermoelectric generator (MTEG) and triboelectric nanogenerator (TENG) is proposed, and a theoretical model for MTEG-TENG hybrid energy harvesting is established. This study develops an integrated energy harvesting prototype, which incorporates oscillating-TENG (O-TENGs), MTEGs, and a power management control unit. This prototype not only harvests energy from the vibrations of transmission lines but also converts the lines thermal energy into electricity. The Experiment results show that the maximum open-circuit voltages of O-TENG and MTEG reach 80.3 V and 1.094 V, respectively. Compared to a single MTEG energy harvesting device, the prototype of the MTEG-TENG hybrid energy harvesting device demonstrates a 5.36% improvement in energy harvesting and battery charging performance. Consequently, this approach achieves self-powered monitoring with excellent stability and lower manufacturing costs. It provides an efficient and durable power approach for transmission line status monitoring devices.

Construction of power network security risk assessment model based on LSA-SVM algorithm in the background of smart grid.

Due to theintricate and interdependent nature of the smart grid, it has encountered an increasing number of security threats in recent years. Currently, conventional security measures such as firewalls, intrusion detection, and malicious detection technologies offer specific protection based on their unique perspectives. However, as the types and concealment of attacksincrease, these measures struggle to detect them promptly and respond accordingly. In order to meet the social demand for the accuracy and computation speed of the power network security risk evaluation model, the study develops a fusion power network security risk evaluation algorithm by fusing the flash search algorithm with the support vector machine. This algorithm is then used as the foundation for building an improved power network security risk evaluation model based on the fusion algorithm.The study's improved algorithm's accuracy is 96.2%, which is higher than the accuracy of the other comparative algorithms; its error rate is 3.8%, which is lower than the error rate of the other comparative algorithms; and its loss function curve convergence is quicker than that of the other algorithms.The risk evaluation model's accuracy is 97.8%, which is higher than the accuracy of other comparative models; the error rate is 1.9%, which is lower than the error rate of other comparative models; the computing time of the improved power network security risk evaluation model is 4.4 s, which is lower than the computing time of other comparative models; and its expert score is high. These findings are supported by empirical analysis of the improved power network security risk evaluation model proposed in the study. According to the study's findings, the fusion algorithm and the upgraded power network security risk evaluation model outperform other approaches in terms of accuracy and processing speed. This allows the study's maintenance staff to better meet the needs of the community by assisting them in identifying potential security hazards early on and taking the necessary preventative and remedial action to ensure the power system's continued safe operation.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Blockchain technology in the renewable energy sector: A co-word analysis of academic discourse.

The transformative potential of blockchain technology in the renewable energy sector is increasingly gaining recognition for its capacity to enhance energy efficiency, enable decentralized trading, and ensure transaction transparency. However, despite its growing importance, there exists a significant knowledge gap in the holistic understanding of its integration and impact within this sector. Addressing this gap, the current study employs a pioneering approach, marking it as the first comprehensive bibliometric analysis in this field. We have systematically examined 390 journal articles from the Web of Science database, covering the period from 2017 through the end of February 2024, to map the current landscape and thematic trajectories of blockchain technology in renewable energy. The findings highlight several critical thematic areas, including blockchain's integration with smart grids, its role in electric vehicle integration, and its application in sustainable urban energy systems. These themes not only illustrate the diverse applications of blockchain but also its substantial potential to revolutionize energy systems. This study not only fills a crucial gap in existing literature but also sets a precedent for future interdisciplinary research in this domain, bridging theoretical insights with practical applications to fully harness the potential of blockchain in the renewable energy sector.

Optimizing home energy management: Robust and efficient solutions powered by attention networks.

This paper explores the integration of attention networks in the realm of home energy management systems (HEMS) to enhance the robustness and efficiency of energy consumption optimization. With the growing demand for smart grid technologies, the need to achieve demand side response becomes paramount. The proposed solution leverages attention networks to dynamically allocate significance to various aspects of energy consumption patterns, considering the diverse load types and dynamic loading scenarios present in households. In this investigation, we focus on the AMpds2 dataset, characterized by intricate loading patterns, and assess its performance across various time series forecasting methodologies, including (RNN), (LSTM), (TCN), and transformers. Multiple methodologies undergo performance evaluation using diverse hyperparameter combinations. Evaluation metrics, specifically (RMSE) and (MAE), are employed. Advanced optimizers such as (Adam) and (Adamax) are applied, and activation functions, including sigmoid, linear, tanh, and ReLU, are implemented. A comprehensive performance analysis involves 16 hyperparameter combinations across four distinct time series models. Through meticulous scrutiny, it is determined that the utilization of transformers in forecasting energy and load patterns results in a 4% increase in accuracy, as elucidated in the results section. The implementation of this study is carried out on the Python 3.2 platform, and the matplotlib library is employed to visualize the comparison between actual and predicted data.

Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles.

In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. We fill this gap with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters.•From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences, and clustering profiles into behaviour clusters. Transitions between successive behaviour clusters and profiles magnitudes are characterised.•Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns.•Using the characterisation of behaviour cluster and profile magnitude transitions, and the GAN-based profiles generator, a Markov chain mechanism can generate realistic energy data for successive days.

Cyberattack patterns in blockchain-based communication networks for distributed renewable energy systems: A study on big datasets.

Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs. The datasets provided include both raw (unprocessed) and refined (processed) data, which highlight distinct trends in cyberattacks in DERs. These distinctive patterns demonstrate problems like superfluous mass data generation, transmitting invalid packets, sending deceptive data packets, heavily using network bandwidth, rerouting, causing memory overflow, overheads, and creating high latency. These issues result in ineffective real-time events monitoring and control of DERs in SGs. The thorough nature of these datasets is expected to play a crucial role in identifying and mitigating a wide range of cyberattacks across different smart grid applications.

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns.

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.