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The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribution network on fault diagnosis, as well as the misoperation or refusal of protection and switch, and the false alarm or leakage of alarm signal. At the same time, in order to display the results of fault diagnosis accurately and intuitively, an analytical model of fault diagnosis of distribution network based on multi-source information fusion is proposed. Finally, this paper verifies the effectiveness of this method through an example application. This article uses the PEDL dataset for experimental research, Through the comparison of fault data, it can be seen that compared with existing methods, the method proposed in this paper achieves the highest goodness of fit for warning, indicating the best fault warning effect.When there is enough training set, the prediction accuracy of the fault set can reach over 99%, Based on experimental analysis, it can be concluded that the proposed power grid equipment model has higher accuracy and reliability compared to traditional models. And the model in this article integrates the real-time monitoring function of power grid equipment and the equipment fault warning function, which improves the practicality of the power grid equipment monitoring system. [ABSTRACT FROM AUTHOR]

In response to the low operating speed and poor stability of energy harvesting systems in smart grids, an energy harvesting optimization method based on improved convolutional neural networks and digital twin technology is proposed in the experiment. Firstly, a smart grid data transmission framework integrating digital twin technology is proposed. A digital twin mapping method based on time, data, and topology structure is used to realize the digital twin mapping at the device level of power grid. Through data synchronization and interaction between the physical power grid and the digital twin model, the operational efficiency and reliability of the power grid are improved. Then, the classical convolutional neural network and attention mechanism are used to comprehensively analyze the physical topology data in the smart grid energy acquisition system. The improved lightweight target detection model is combined to monitor the equipment status of the smart grid and extract key features. Simultaneously utilizing convolutional attention mechanism to dynamically adjust the feature weights of channels or spaces, completing the preprocessing of energy harvesting data. Finally, combined with energy harvesting and power grid switching system, the process of energy harvesting and power grid operation are optimized together. On the training and validation sets, when the channels exceeded 60, the proposed method achieved a system energy efficiency of 55% during operation. The system energy efficiency of the other three comparative algorithms was all less than 40%. In practical applications, as the energy transfer loss increased to 1.0, the system throughput increased to 50 bits. The electricity needs of different users were met, and the difference between power allocation and optimal power allocation was small, which was very reasonable. This proves that the research has effectively optimized the energy harvesting system in the smart grid, improving the efficiency and reliability of the system in practical applications of the smart grid. At the same time, in the increasingly severe energy problem, this system can further provide technical references for the utilization of renewable energy and help achieve the goal of sustainable energy. [ABSTRACT FROM AUTHOR]

Phasor estimation plays a critical role in island detection, fault detection, voltage stability monitoring, and state estimation within power systems. This paper proposes a phasor estimation method using micro-Phasor Measurement Units (µPMUs) in a Smart Distribution Network. A µPMU model is developed utilizing a recursive Discrete Fourier Transform (DFT) algorithm combined with high-precision filters to enhance accuracy. The optimal placement of µPMUs in the distribution network is determined using a Mixed-Integer Linear Programming (MILP) approach for the IEEE 33 bus test system. The model is tested on the IEEE 33 bus system with Electric Vehicle Charging Stations (EVCS) and Distributed Generators (DGs) under both nominal and off-nominal frequencies. The results comply with the IEEE C37.118.1 standard, achieving phasor magnitude errors as low as + 0.01% and frequency accuracy of 1 mHz with minimal ripple. This study demonstrates the robustness and precision of the µPMU model, highlighting its potential for enhancing real-time monitoring and control in smart grid applications. [ABSTRACT FROM AUTHOR]

Introduction: With the rapid development of human society and economy, the power generation technology of various new energy vehicles has begun to receive widespread attention. Methods: Due to the lack of inertia and frequency stability in the new energy vehicle power generation system, this paper proposes a power generation control method that combines linear active disturbance rejection control technology and virtual synchronous generator technology. This method first introduces the control strategy and inertial response of the virtual synchronous generator. Then, it uses linear active disturbance rejection control technology to improve the virtual synchronous generator technology to deal with the uncertainty and external interference in the system. Results: The results showed that when the virtual inertia coefficient was 0, and the new energy vehicles would hardly intervene in the regulation of the grid voltage. When the virtual inertia coefficient was 5, the decline rate of the DC bus voltage of new energy vehicles had slowed down. When the virtual inertia coefficient increased, the power output of new energy vehicles can be increased to the grid. When the load suddenly increased, and the corresponding DC bus voltage decreased more slowly. In the VSG output power comparison, under the research method, the frequency fluctuation only increased by 0.09 Hz and returned to the rated frequency of 50 Hz. Additionally, the dynamic process of the system output power was the shortest, lasting only 0.05 s. Discussion: The above results show that the research method has significant superiority and effectiveness in improving the inertial response and overall stability of the new energy vehicle power system. [ABSTRACT FROM AUTHOR]

Smart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs. [ABSTRACT FROM AUTHOR]

The future energy consumption of data centers is expected to be significant worldwide. From the perspective of carbon neutrality, designing 100 % renewable energy systems with distributed energy resources that can reliably supply energy to data centers is necessary. However, renewables' intrinsic uncontrollable characteristics make the stable energy supply challenging. Herein, we designed a 100 % renewable energy system by combining abundant but uncontrollable solar energy (e.g., photovoltaic (PV) cells) and controllable hydrogen (H2) energy systems (e.g., hydrogen microturbine and fuel cells) for a stable energy supply to an actual data center in South Korea. The hybrid system with on-site hydrogen production would be favorable after 2030 because of the expected decrease in green hydrogen prices and increase in carbon tax. Also, from sensitivity analyses, we found that the total NPC decreased by 75.2 % ($ 83.8M) with the green hydrogen price change from 14.5 $/kg to 3 $/kg. [ABSTRACT FROM AUTHOR]

Cyber-physical power systems (CPPS) integrate information and communication technology into conventional electric power systems to facilitate bidirectional communication of information and electric power between users and power grids. Despite its benefits, the open communication environment of CPPS is vulnerable to various security attacks. This paper proposes a federated deep learning-based architecture to detect false data injection attacks (FDIAs) in CPPS. The proposed work offers a strong, decentralized alternative with the ability to boost detection accuracy while maintaining data privacy, presenting a significant opportunity for real-world applications in the smart grid. This framework combines state-of-the-art machine learning and deep learning models, which are used in both centralized and federated learning configurations, to boost the detection of false data injection attacks in cyber-physical power systems. In particular, the research uses a multi-stage detection framework that combines several models, including classic machine learning classifiers like Random Forest and ExtraTrees Classifiers, and deep learning architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results demonstrate that Bidirectional GRU and LSTM models with attention layers in a federated learning setup achieve superior performance, with accuracy approaching 99.8%. This approach enhances both detection accuracy and data privacy, offering a robust solution for FDIA detection in real-world smart grid applications. [ABSTRACT FROM AUTHOR]

Residual rights are the right to choose asset uses not specified in the contract. They are essential in situations of uncertainty. The current energy transition presents a greater variety of assets. Moreover, it is impossible to anticipate all relevant states that the assets and the environment can embody, making it impossible to optimize them contractually. Furthermore, there is consensus that the transition must occur quickly, which means high levels of investment in new specific assets. How should we distribute property rights to maximize social benefits in a context with specific and dispersed assets? Because of the complementarity between network and distributed resources, this article questions the premise that deverticalization is invariably beneficial to consumers and argues for the need to revise the concept of network and develop the regulatory implications of such a reconceptualization. We defend the need to evaluate alternative network concepts considering the technological repertoire available to operationalize them. When considering the technological repertoire available today, characterized by the competitiveness of information and communications technology (ICT) and distributed resources, we should recognize the inherently incomplete nature of the contracts signed between network operators and users. [ABSTRACT FROM AUTHOR]

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data's storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to 99.58 %, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event. [ABSTRACT FROM AUTHOR]

In the realm of smart grids, machine learning (ML) detectors—both binary (or supervised) and anomaly (or unsupervised)—have proven effective in detecting electricity theft (ET). However, binary detectors are designed for specific attacks, making their performance unpredictable against new attacks. Anomaly detectors, conversely, are trained on benign data and identify deviations from benign patterns as anomalies, but their performance is highly sensitive to the selected threshold values. Additionally, ML detectors are vulnerable to evasion attacks, where attackers make minimal changes to malicious samples to evade detection. To address these limitations, we introduce a hybrid anomaly detector that combines a Deep Auto-Encoder (DAE) with a One-Class Support Vector Machine (OCSVM). This detector not only enhances classification performance but also mitigates the threshold sensitivity of the DAE. Furthermore, we evaluate the vulnerability of this detector to benchmark evasion attacks. Lastly, we propose an accurate and robust cluster-based DAE+OCSVM ET anomaly detector, trained using Explainable Artificial Intelligence (XAI) explanations generated by the Shapley Additive Explanations (SHAP) method on consumption readings. Our experimental results demonstrate that the proposed XAI-based detector achieves superior classification performance and exhibits enhanced robustness against various evasion attacks, including gradient-based and optimization-based methods, under a black-box threat model. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) play a vital role in collecting and transmitting data in diverse environments. In the context of Smart Grid systems, WSNs are extensively employed within the Advanced Metering Infrastructure (AMI) architecture to facilitate bidirectional communication between electric water/gas meters and city utilities. However, the design of an efficient routing protocol and ensuring secure data transmission present notable challenges in AMI smart systems. In this paper, a novel routing protocol named the Secure Composite Mutation Artificial Bee Colony (SCMABC) algorithm is proposed to perform secure transmission of data in AMI networks. Moreover, the determination of Group Key management scheme-based Lagrange Interpolation Polynomial (GK-LIP) is obtained to validate the security of data within the cluster nodes. The proposed approach capitalizes on the inherent capabilities of WSNs to track and monitor Smart Grid data, leveraging smart meters embedded within sensor nodes. We achieve efficient routing to ensure rapid data transmission while guaranteeing data security through the utilization of cryptographic keys for secure transmission between the smart grid and IoT web pages. The analysis of the result is performed with various metrics to show the robustness of the model. The experimentation results revealed that the proposed method attained better efficiency in security enhancement by diminishing the processing time for encryption as well as the decryption process and the mean error. The research contributes to the challenges determined in Smart Grid AMI networks to improve robustness and security for efficient data transmission. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy in power system, renewable energy power ramp events (REPREs), dominated by wind power and photovoltaic power, pose significant threats to the secure and stable operation of power systems. This paper presents an early warning method for REPREs based on long short-term memory (LSTM) network and fuzzy logic. First, the warning levels of REPREs are defined by assessing the control costs of various power control measures. Then, the next 4-h power support capability of external grid is estimated by a tie line power prediction model, which is constructed based on the LSTM network. Finally, considering the risk attitudes of dispatchers, fuzzy rules are employed to address the boundary value attribution of the early warning interval, improving the rationality of power ramp event early warning. Simulation results demonstrate that the proposed method can generate reasonable early warning levels for REPREs, guiding decision-making for control strategy. [ABSTRACT FROM AUTHOR]

Advanced metering infrastructure (AMI) plays a critical role in the smart grid by integrating metering systems with communication capabilities, especially for the industrial internet of things. However, existing authentication protocols have proven ineffective against quantum computing attacks and are computationally intensive since AMI contains limited computing components, such as smart meters. In this paper, we present a novel, efficient module learning with errors-based authentication and key agreement system for AMI, which we call EMAS. As part of the security measures of EMAS, Kyber Post-Quantum Public Key Encryption, a one-time pad mechanism, and hash functions are used. A formal and informal analysis of the security features is presented, showing that the proposed system is secure and resistant to known attacks. The performance analysis of our proposed EMAS on a B-L475E-IOT01A node equipped with a ARM Cortex M4 microcontroller shows that EMAS is more efficient than existing relevant schemes. About the computation time, EMAS takes 15.693 ms. This result is lower than other existing relevant schemes. [ABSTRACT FROM AUTHOR]

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False data injection attack (FDIA) can affect the state estimation of the power grid by tampering with the measured value of the power grid data, and then destroying the stable operation of the smart grid. Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams. Data-driven features, however, cannot effectively capture the differences between noisy data and attack samples. As a result, slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks. To address this problem, this paper designs a deep collaborative self-attention network to achieve robust FDIA detection, in which the spatio-temporal features of cascaded FDIA attacks are fully integrated. Firstly, a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes, and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node, which guides the network to pay more attention to the node information that is conducive to FDIA detection. Furthermore, the bi-directional Long Short-Term Memory (LSTM) network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps. Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information, efficiently distinguish power grid noise from FDIA attacks, and adapt to diverse attack intensities. Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator (NYISO) in IEEE 14, IEEE 39, and IEEE 118 bus systems, and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness. [ABSTRACT FROM AUTHOR]

ABSTRACT The rapid advancements of intelligent technologies have brought about the potential vulnerability of confidential gradient information linked to cost functions when solving distributed optimization and its related problems. Within the context of distributed energy management, safeguarding such private information has risen to paramount importance. This article investigates a distributed energy management problem (DEMP) to minimize cost while simultaneously satisfying multiple local constraints and protecting the private gradient information of the cost function. To this end, a new privacy‐preserving distributed optimization algorithm under the framework of gradient tracking over time‐varying graphs is proposed for solving the DEMP. Specifically, the auxiliary variables are designed for each node in the algorithm to update the gradient while the original state variables are responsible for the interaction with original neighbors and auxiliary variables. Consequently, the devised algorithm can protect the confidentiality of private cost gradient information, even in the presence of eavesdroppers within the network. In contrast to the homomorphic encryption, signal masking method, and some other algorithms without utilizing the state decomposition, the designed algorithm does not require extra information or computing resources. Moreover, it is proven that the designed algorithm could theoretically converge to the exact optimum of the DEMP at a rate of O((lnk)/k)$$ O\left(\left(\ln k\right)/\sqrt{k}\right) $$ under some mild assumptions. [ABSTRACT FROM AUTHOR]

With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. Furthermore, the fact that unstable cases rarely occur would lead to an imbalanced dataset. Thus, power system transient stability status prediction has the bias problem caused by the imbalance of sample size and class importance. Faced with such a problem, a TSA model based on the sample selection method is proposed in this paper. Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. Primarily, based on the location of training samples in the feature space, the border samples are selected by trained support vector machine (SVM), and the edge samples are selected by the assistance of the approximated tangent hyperplane of a class surface. Then, the selected samples are input to stacked sparse autoencoder (SSAE) as the final classifier. Simulation results in the IEEE 39‐bus system and the realistic regional power system of Eastern China show the high performance of the proposed method. [ABSTRACT FROM AUTHOR]

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Demand-side management of smart grid with electric vehicles (EVs) is overviewed in this review paper. The major objective of the work is to reduce the hourly peak load to obtain a steady load schedule, maximize user satisfaction and reduce cost. This review allows for the probability of leveling the everyday energy load arc and unstable demand response to hourly prices from one time period to another. To obtain a balanced everyday load schedule, increase user satisfaction, and cut costs, the main aim is to reduce peak hourly load. A management system for an EV connected to the national grid for a future household with controllable electric loads. The approach that has been presented enables the integration of EVs and renewable resources while also optimizing the demand and generation in hourly distribution. The agents are taken into account for managing load, storage, and generation; specifically, they are EV aggregators. The vehicle-to-grid (V2G) combination of electric vehicles is a key aspect of this study; with this capability, EVs may offer power grid-specific services like load shifting and congestion management. By maximizing the hourly distribution of demand as well as generation, accounting for technical limitations, and enabling the addition of EVs and RES. [ABSTRACT FROM AUTHOR]

Relevant datasets that depict and/or emulate the smart grid networks (SGN) are key to developing cybersecurity models that can effectively provide security for mission-critical infrastructure. The difficulty in obtaining relevant SGN real-life datasets presents a considerable challenge for researchers in the field and, the existing datasets lack representation of the IEC61850 protocol for modelling substation automation processes for cybersecurity solutions. This paper presents a dataset simulated from a fully virtual testbed, intended to provide researchers with the necessary datasets for research and experiments that require massive amounts of data close to the real-world scenario. Experimentally, the dataset was used to develop an intrusion detection model based on gated recurrent unit (GRU), deep belief network (DBN), long-short term memory (LSTM), and evaluated using accuracy, precision, recall, F1-score, detection rate, false alarm rate (FAR), missed alarm rate (MAR), mean squared error (MSE), mean absolute error (MAE), and loss. Results show that the standalone deep learning (DL) algorithms outperformed the hybridized ones and are more suitable for developing models for securing substation automation systems that support generic object-oriented substation events (GOOSE), and manufacturing message specification (MMS) and run on IEC61850, distributed network protocol version 3 (DNP3), and Modbus-transmission control protocol (ModbusTCP). [ABSTRACT FROM AUTHOR]

As the next-generation power system, smart grid presents challenges to enterprises in managing and analyzing massive data, meeting complex operational and decision-making demands, and predicting future power demand for grid optimization. This paper aims to proposed a fusion algorithm for smart grid enterprise decision-making and economic benefit analysis, enhancing the accuracy of decision-making and predictive capability of economic benefits. The proposed method combines techniques such as Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and edge computing. The LSTM model is employed to model historical data of the smart grid. The GAN model generates diverse scenarios for future power demand and economic benefits. The proposed method is evaluated on four public datasets, including the ENTSO-E Dataset, and outperforms several traditional algorithms in terms of prediction accuracy, efficiency, and stability. Notably, on the ENTSO-E Dataset, the proposed algorithm achieves a reduction of over 46.6% in FLOP, and a decrease in inference time by over 48.3%, and an improvement of 38% in MAPE. The novel fusion algorithm proposed in this paper demonstrates significant advantages in accuracy and predictive capability, providing a scientific basis for smart grid enterprise decision-making and economic benefit analysis while offering practical value for real-world applications. • A novel fusion algorithm for accurate load demand prediction in smart grids. • Training were conducted on multiple datasets and validation was performed. • Significant contributions to risk assessment planning in smart grid enterprises. [ABSTRACT FROM AUTHOR]

Demand response management (DRM) significantly influences the prospective advancement of electricity smart grids. This paper introduces three distinct game-theoretic duopoly models for the smart grid demand response management problem. It delineates several rational assumptions regarding the model variables, functions, and parameters. The first model adopts a Cournot duopoly form, offering a unique closed-form equilibrium solution. The second model adopts a Stackelberg duopoly structure, also providing a unique closed-form equilibrium solution. Following a comparison of the economic viability of the two model equilibria and an assessment of their sensitivity to parametric changes, the paper proposes a third model with a Cartel structure and discusses its advantages over the earlier models. Finally, the paper examines how demand forecasting affects the equilibrium quantities and pricing solutions of each model. [ABSTRACT FROM AUTHOR]

Extreme space weather events can have serious impacts on critical infrastructure, including Global Navigation Satellite Systems (GNSS). The use of GNSS, particularly as sources of accurate timing signals, is becoming more widespread, with one example being the measurement of electricity grid frequency and phase information to aid grid management and stability. Understanding the likelihood of extreme space weather impacts on GNSS timing signals is therefore becoming vital to maintain national electricity grid resilience. This study determines critical intensity thresholds above which the complete failure of a GNSS based timing system may occur. Solar radio bursts are identified as a simple example to investigate in more detail. The probability of occurrence of an extreme space weather event with an intensity equal to or greater than the critical intensity is estimated. Both a power law and extreme value theory were used to evaluate recurrence probabilities based on historical event frequencies. The probability was estimated to be between 3%–12% per decade to cause the complete failure of any GNSS‐based timing system. Plain Language Summary: Society is increasingly reliant on satellite technologies for a wide range of applications. If a huge space weather event were to impact the Earth today, it would likely have catastrophic impacts across many modern technologies including satellites and satellite systems, communications, and electricity grids. Here we assess the probabilities that intense solar events may affect timings derived from Global Navigation Satellite System (of which one commonly used example is the Global Positioning System, or GPS). These timing signals are increasingly used in electricity grid management. Solar radio bursts are used as an example, since they can overwhelm the weak GNSS signal. Statistical methods were employed to assess 46 years of solar radio burst data. Our findings suggest a 3%–12% probability per decade of an event large enough to disrupt the UK electricity grid. Key Points: Extreme space weather can disrupt Global Navigation Satellite Systems (GNSS) timing signals, an increasingly critical aspect of the electricity distribution networkFirst case study illustrating degradation of GNSS timing from solar radio burstsLikelihood of a significantly disruptive event is constrained at 3%–12% per decade [ABSTRACT FROM AUTHOR]

As the adoption of electric vehicles increases, the challenge of managing bidirectional energy flow while ensuring grid stability and respecting user preferences becomes increasingly critical. This paper aims to develop an intelligent framework for vehicle-to-grid (V2G) energy management that balances grid demands with user autonomy. The research presents VESTA (vehicle energy sharing through artificial intelligence), featuring the semantic-aware vehicle access control (SEVAC) model for efficient and intelligent energy sharing. The methodology involves developing a comparative analysis framework, designing the SEVAC model, and implementing a proof-of-concept simulation. VESTA integrates advanced technologies, including artificial intelligence, blockchain, and edge computing, to provide a comprehensive solution for V2G management. SEVAC employs semantic awareness to prioritise critical vehicles, such as those used by emergency services, without compromising user autonomy. The proof-of-concept simulation demonstrates VESTA's capability to handle complex V2G scenarios, showing a 15% improvement in energy distribution efficiency and a 20% reduction in response time compared to traditional systems under high grid demand conditions. The results highlight VESTA's ability to balance grid demands with vehicle availability and user preferences, maintaining transparency and security through blockchain technology. Future work will focus on large-scale pilot studies, improving AI reliability, and developing robust privacy-preserving techniques. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel demand-side management (DSM) system designed to optimize electric vehicle (EV) charging at public stations using model predictive control (MPC). The system adjusts to real-time grid conditions, electricity prices, and user preferences, providing a dynamic approach to energy distribution in smart city infrastructures. The key focus of the study is on reducing peak loads and enhancing grid stability, while minimizing charging costs for end users. Simulations were conducted under various scenarios, demonstrating the effectiveness of the proposed system in mitigating peak demand and optimizing energy use. Additionally, the system's flexibility enables the adjustment of charging schedules to meet both grid requirements and user needs, making it a scalable solution for smart city development. However, current limitations include the assumption of uniform tariffs and the absence of renewable energy considerations, both of which are critical in real-world applications. Future research will focus on addressing these issues, improving scalability, and integrating renewable energy sources. The proposed framework represents a significant step towards efficient energy management in urban settings, contributing to both cost savings and environmental sustainability. [ABSTRACT FROM AUTHOR]

In the power industry, advanced techniques have furthered the development of the smart grid's power system and management. The world's third-largest country is India, which has a producer and consumer of electricity, is struggling with different power-related problems, as well as distribution losses, transmission, environmental concerns, and electricity theft. The energy sector is investigating innovative technologies to enhance grid efficiency, security, and sustainability to address power-related issues. Recently, smart grid technology has ascribed significance to the energy scenario; the term "smart grid" relates to electric electricity. The study aims to thoroughly evaluate how smart grid technologies might improve the reliability and efficiency of India's electrical system. This article examines the impact of smart grid technologies on national grids and makes some proposals to authorities for switching their traditional grid system to a smart grid system. The results indicate the yearly wind profile, comparative analysis of energy consumption, and cost analysis of the system. Smart grid integration is strengthened by the useful insights provided by the annual wind profile study, which reveals the region's renewable energy potential. Analysis of costs and energy consumption patterns show that switching to a smart grid system is financially feasible in the long term, and studies of impacts on utilization of resources show that it is beneficial. [ABSTRACT FROM AUTHOR]

Introduction: The integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging. Methods: To address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis. Results: The proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (N PC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case. Discussion: Seasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption. [ABSTRACT FROM AUTHOR]

A smart grid is a modern electrical system that uses information technology, including sensors, measurement tools and communication devices, to monitor and improve the efficiency of the power system. However, real-time forecasting remains a challenge due to its complexity. This paper presents a forecasting framework that combines Convolutional Neural Networks (CNN) and Bidirectional LongShort Term Memory (BiLSTM) for real-time load forecasting in smart grids. Compared to traditional methods like ARMA and Decision Trees (DTs), the proposed CNN-BiLSTM model demonstrates superior performance in terms of prediction accuracy, reaching up to 99% - higher than Long-Short Term Memory (LSTM) (93%) and Support Vector Machine (SVM) (84%). Additionally, the CNN-BiLSTM model requires fewer computational resources, with 90 Gigaflops (G) and 94 Million (M) parameters, compared to 151 (G) and 120 (G) for ARIMA and CNN-LSTM, respectively. These results indicate the proposed model's ability to accurately predict power system loads in real time with high computational efficiency. [ABSTRACT FROM AUTHOR]

Load balancing between required power demand and the available generation capacity is the main task of demand response for a smart grid. Matching between the objectives of users and utilities is the main gap that should be addressed in the demand response context. In this paper, a multi-user optimal load scheduling is proposed to benefit both utility companies and users. Different objectives are considered to form a multi-objective artificial hummingbird algorithm (MAHA). The cost of energy consumption, peak of load, and user inconvenience are the main objectives considered in this work. A hybrid multi-criteria decision making method is considered to select the dominance solutions. This approach is based on the removal effects of criteria (MERECs) and is utilized for deriving appropriate weights of various criteria. Next, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is used to find the best solution of load scheduling from a set of Pareto front solutions produced by MAHA. Multiple pricing schemes are applied in this work, namely the time of use (ToU) and adaptive consumption level pricing scheme (ACLPS), to test the proposed system with regards to different pricing rates. Furthermore, non-cooperative and cooperative users' working schemes are considered to overcome the issue of making a new peak load time through shifting the user load from the peak to off-peak period to realize minimum energy cost. The results demonstrate 81% cost savings for the proposed method with the cooperative mode while using ACLPS and 40% savings regarding ToU. Furthermore, the peak saving for the same mode of operation provides about 68% and 64% for ACLPs and ToU, respectively. The finding of this work has been validated against other related contributions to examine the significance of the proposed technique. The analyses in this research have concluded that the presented approach has realized a remarkable saving for the peak power intervals and energy cost while maintaining an acceptable range of the customer inconvenience level. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) continues to expand in the industrial domain, cyber threats have become a major concern, with routing attacks posing significant risks due to the heterogeneity of IoT devices, limited resources, and extensive connectivity. This research aims to enhance the security of IoT-based RPL networks by developing an advanced Intrusion Detection System (IDS) utilizing ensemble learning techniques. The primary objective is to create a robust cybersecurity solution capable of detecting and mitigating Version Number (VN), Hello Flood (HF), and Decrease Rank (DR) attacks, which can cause substantial disruptions and data loss. The proposed IDS model is validated using the IRAD dataset, attaining exceptional performance with 99.88% accuracy, precision, recall, and F1 scores. The methodology incorporates a 5-fold cross-validation approach to confirm reliability and scalability. Comparative analysis with existing models validates the statistical significance and robustness of the proposed solution, highlighting its effectiveness in enhancing IoT network security against evolving cyber threats. This study underscores the critical need for advanced IDS solutions to safeguard the integrity and functionality of IoT networks. Additionally, recent incidents, such as the CrowdStrike 2024 incident, highlight the ongoing challenge and the importance of robust cybersecurity measures in today’s digital landscape. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes a smart grid model named "GridOptiPredict", which aims to achieve efficient analysis and processing of power system data through deep fusion of deep learning and graph neural network, so as to improve the intelligent level and overall efficiency of power grid operation. The model integrates three core functions of load forecasting, power grid state sensing and resource optimization into one, forming a closely connected and complementary framework. Through carefully designed experimental scheme, the practical value and effectiveness of "Grid OptiPredict" model are fully verified from three aspects: accuracy of load forecasting, sensitivity of power grid state sensing and efficiency of resource allocation strategy. Experimental results show that the model has significant advantages in prediction accuracy, model stability and robustness, resource optimization, security, information security, social and economic benefits and user experience. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The smart grid (SG) is an efficient and reliable framework capable of controlling computers, automation, new technologies, and devices. Advanced metering infrastructure (AMI) is a crucial part of the SG, facilitating two-way communication between users and service providers (SPs). Computation, storage, and communication are extremely limited as the AMI's device is typically deployed outdoors and connected to an open network. Therefore, an authentication and key agreement protocol is necessary to ensure the security and confidentiality of communications. Existing research still does not meet the anonymity, perfect forward secrecy, and resource-limited requirements of the SG environment. To address this issue, we advance a lightweight authentication and key agreement scheme based on elliptic curve cryptography (ECC). The security of the proposed protocol is rigorously proven under the random oracle model (ROM), and was verified by a ProVerif tool. Additionally, performance comparisons validate that the proposed protocol provides enhanced security features at the lowest computation and communication costs. [ABSTRACT FROM AUTHOR]

With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks. [ABSTRACT FROM AUTHOR]

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency. [ABSTRACT FROM AUTHOR]

This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation of DSM in conjunction with factors including renewable energy generation, load shifting and energy costs. An increased understanding of the practicality of DSM will support future improvements in building energy efficiency and sustainability. Ten occupants of the National Build Energy Retrofit Test-bed (NBERT) were selected as a case study. The electricity consumption pattern of the NBERT occupants was derived over a period of two years. Five unique DSM schedules were developed for each of the NBERT occupants, and a survey was carried out to investigate the practicality of these DSM schedules. A real-time electricity pricing tariff, electricity CO2 intensity, three feed-in tariffs and two microgrid control methods were evaluated to assess the practicality of each DSM schedule on the ten NBERT occupants. The results showed that the incorporation of onsite renewable energy generation with price-based DSM had a positive impact (r = 0.69–0.75) on occupant practicality. Onsite renewable energy generation was able to offset the demand for expensive electricity from the grid during peak hours, which aligned with the occupants' typical work schedules. However, the introduction of a feed-in tariff with a renewable energy microgrid made price-based DSM less practical (r = 0.15–0.64). [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid method for energy management (EM) of a solar photovoltaic (PV) hybrid microgrid (MG) for the residential distribution system (DS). The proposed approach integrates the single candidate optimizer algorithm (SCOA), and gradient boost decision tree algorithm (GBDT), called the SCOA-GBDT algorithm. The main contribution of this manuscript is to (a) effectively achieve battery storage, solar PV, and loads to improve energy savings and lessen the loss of conversion; (b) effectively handle solar photovoltaic, battery storage, and loads to recognize cost-effective power distribution using the proposed technique; and (c) effectively handle weak photovoltaic power prevalent in the DC side of MG by an auxiliary-battery arrangement to preserve energy. The proposed energy management strategy lowers the conversion losses in the residential DS. Here, the dc loads are provided by a solar photovoltaic, the utility-grid provides the AC loads, and an auxiliary-battery bank is considered for storing the energy. Then, the performance of the proposed technique is done in MATLAB software and is compared to different existing approaches. From the simulation outcome, it is concluded that the proposed approach reduces costs and losses compared to the existing approaches. [ABSTRACT FROM AUTHOR]

The growing adoption of behind-the-meter (BTM) photovoltaic (PV) systems, electric vehicle (EV) home chargers, and heat pumps (HPs) is causing increased grid congestion issues, particularly in power distribution grids. Leveraging BTM prosumer flexibility offers a cost-effective and readily available solution to address these issues without resorting to expensive and time-consuming infrastructure upgrades. This work evaluated the effectiveness of this solution by introducing a novel modeling framework that combines a rolling horizon (RH) optimal power flow (OPF) algorithm with a customized piecewise linear cost function. This framework allows for the individual control of flexible BTM assets through various control measures, while modeling the power flow (PF) and accounting for grid constraints. We demonstrated the practical utility of the proposed framework in an exemplary residential region in Schutterwald, Germany. To this end, we constructed a PF-ready grid model for the region, geographically allocated a future BTM asset mix, and generated tailored load and generation profiles for each household. We found that BTM storage systems optimized for self-consumption can fully resolve feed-in violations at HV/MV stations but only mitigate 35% of the future load violations. Implementing additional control measures is key for addressing the remaining load violations. While curative measures, e.g., temporarily limiting EV charging or HP usage, have minimal impacts, proactive measures that control both the charging and discharging of BTM storage systems can effectively address the remaining load violations, even for grids that are already operating at or near full capacity. [ABSTRACT FROM AUTHOR]

Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones. [Display omitted] • Encryption-based data consolidation strategy for 5G in fog computing is presented. • Data consolidation and data extraction algorithm at fog devices and cloud servers. • A comparative analysis based on storage, communication and transmission cost is done. [ABSTRACT FROM AUTHOR]

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Power theft and other abnormal power consumption behaviors seriously affect the safety, reliability, and stability of the power grid system. The traditional abnormal power consumption detection methods have complex models and low accuracy. In this paper, an unsupervised abnormal power consumption detection method based on multi-cluster feature selection and the Gaussian mixture model is proposed. First of all, twelve features are extracted from the load sequence to reflect the overall form, fluctuation, and change trend of the user's electricity consumption. Then, multi-cluster feature selection algorithm is employed to select a subset of important features. Finally, based on the selected features, the Gaussian mixture model is formulated to cluster the normal power users and abnormal power users into different groups, so as to realize abnormal power consumption detection. The proposed method is evaluated through experiments based on a power load dataset from Anhui Province, China. The results show that the proposed method works well for abnormal power consumption detection, with significantly superior performance comapred to the traditional approaches in terms of the popular binary evaluation indicators like recall rate, precision rate, and F-score. [ABSTRACT FROM AUTHOR]

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device's continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%. [ABSTRACT FROM AUTHOR]

Statistical methods must be developed and optimized in distributed systems due to the increasing amount of data and processing demands in modern applications. The application and optimization of mathematical and probabilistic statistical methods in distributed computing settings is the main topic of this study. Algorithms like these have the potential to improve performance, scalability, and parallel processing abilities when integrated into distributed systems. We commence our investigation by reviewing current mathematical and probabilistic statistical algorithms, determining their advantages and disadvantages, and evaluating their suitability for distributed architectures. We then suggest new approaches for their smooth incorporation into distributed computing structures, making use of distributed storage and parallel processing to effectively manage massive datasets. Improving these algorithms' performance in distributed environments is the focus of this research's refinement phase. We seek to optimize the use of distributed infrastructures by minimizing latency and maximizing computational resources by investigating efficient communication protocols, load balancing mechanisms, and parallelization approaches. The suggested algorithms are put into practice inside a distributed structure for empirical confirmation, and their effectiveness is evaluated in comparison to more conventional, non-distributed competitors. We test the scaling, precision, and effectiveness of the methods in practical scenarios using a variety of datasets and use cases. [ABSTRACT FROM AUTHOR]

The demand-side management (DSM) research field has expanded due to rising energy consumption. In the traditional electrical grid, unknown energy usage results in high costs. This paper introduces a reinforcement learning-based self-adaptive learning-black widow optimization (RL-SAL-BWO) approach for dynamic load scheduling and power allocation, aimed at improving energy efficiency and reducing costs and energy consumption. The proposed strategy utilizes pricing signals and real-time load profiles to estimate the changing energy consumption within residential buildings. To optimize energy allocation across different appliances, this algorithm considers both energy efficiency and load characteristics. The RL agent, comprising action space, reward function, and Q-value function, is utilized for decision-making on power allocation and load scheduling. The SAL algorithm automatically adjusts the exploration rate and learning rate which leads to enhanced efficiency. By exploring the solution space, the BWO improves the learning process. Through the integration of RL, SAL, and BWO techniques, energy efficiency is increased, energy consumption is reduced, and electricity costs are lowered. The smart grid is utilized for estimating changes in energy consumption. The purpose of this is to estimate changes in energy consumption, aiding in informed decisions about energy management and infrastructure planning. The proposed approach is implemented using MATLAB R2021b software, followed by the evaluation and calculation of performance metrics. The findings demonstrate that the proposed strategy significantly enhances energy efficiency by 18.5%, reduces energy consumption by 31.91%, and decreases electricity costs by 40.66%. Furthermore, the computation time reduction of the proposed approach is 13.7 s. [ABSTRACT FROM AUTHOR]

Double auction provides a cost-effective manner for sellers/buyers in smart grid. Due to concerns about information leakage, the asks/bids from sellers/buyers are sealed, making it challenging to select potential winners. To address this problem, the concept of public key encryption with equality test is deployed in double auction, since it is able to perform information retrieval over secure asks/bids. However, previous solutions suffer from the following two limitations: (i) unable to check inconsistent secure asks/bids due to the lack of tester-verifiable mechanism; (ii) incurring high matching time costs caused by one-to-one secure asks/bids. Therefore, we propose the VerDA, a secure double auction retrieval system with verifiable equality retrieval towards multiple secure asks/bids. Technically, to achieve the property of consistency over secure asks/bids, we develop the tester-verifiable technology by combining the decryption module and test module. To improve the efficiency of retrieval, we introduce secure multi-asks/bids testing function by augmenting the number of inputs in a same retrieval process. Moreover, we implement VerDA based on the PJM dataset in real cloud environment, where the experimental results show practical performance with encryption and test costs amounting to only 57.4% and 18.7% compared to state-of-the-art solution. [ABSTRACT FROM AUTHOR]

The integration of IoT (Internet of Things) in the energy sector has the potential to transform the way it generates, distributes, and consumes energy. IoT can enable real-time monitoring, control, and optimization of energy systems, leading to improved efficiency, reliability, and sustainability. This work is an attempt to provide an in-depth analysis of the integration of the IoT in the energy sector, examining the characteristics of IoT, its components, and protocols. It also explores the architecture of IoT, the latest advancements and challenges in the field of IoT, including the IoT communications model, IoT sensor boards, and the current challenges facing the industry and related security threats, and also provides suggestions for solutions to address IoT vulnerabilities. The work further delves into IoT in the energy sector aspect and explores the latest advancements and challenges in the field of IoT, including IoT in energy generation, smart cities, smart grids, smart buildings, and intelligent transportation. Additionally, the work explores the challenges of applying IoT in the energy sector discusses future trends in IoT in the energy sector, and aims to provide a detailed understanding of the latest developments and challenges of IoT in the energy sector, as well as its potential impact on the future of the industry. The work critically analyzes securing IoT devices and offers practical solutions to mitigate risks associated with IoT vulnerabilities. This work serves as a valuable resource for researchers, policymakers, and practitioners interested in understanding the impact of IoT on energy security. Taxonomy of the study. [ABSTRACT FROM AUTHOR]