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Cogeneration is widely recognized as one of the most efficient methods of electricity generation, with gas turbine-based systems playing a critical role in ensuring reliability, sustainability, and consistent power output. This paper presents an energy efficiency analysis of a 14 MW high-efficiency cogeneration unit, featuring a modernized gas turbine as its core component. Since gas turbines often operate under varying loads due to fluctuating demand, this study examines their performance at 100%, 75%, and 50% load levels. It is observed that the efficiency of the gas turbine declines as the load decreases, primarily due to losses resulting from deviations from the design flow conditions. A detailed energy balance, Sankey diagram, and a comparative analysis of performance metrics against the manufacturer's guarantees are provided for each load scenario. The results indicate that net thermal efficiency decreases by 10.7% at 75% load and by 30.6% at 50% load compared to nominal performance at full load. The performance at full load closely aligns with the values guaranteed by the gas turbine supplier. The gross electrical power output is 1.33% higher than the guaranteed value, and the thermodynamic circuit's efficiency is 0.49% higher under real conditions. This study represents the initial phase of transitioning the turbine to operate on a fuel blend of natural gas and up to 20% hydrogen, with the goal of reducing CO2 emissions. As a novel contribution, this paper provides a systematized method for calculating and monitoring the in-service performance of gas turbines. The mathematical model is implemented using the Mathcad Prime 8.0 software, which proves to be beneficial for both operators and researchers. [ABSTRACT FROM AUTHOR]

Today's societies need a sustainable supply of energy more than ever, whereas unseen events challenge the electric power supply. The coordinated operation of a power‐gas‐transportation system can decrease the effects of such faults in the distribution level. This paper proposes a model predictive control approach for dynamic load restoration in an integrated energy system. The coordination of electric and gas networks besides the mobile energy storage (MES) units provides a novel resilient alternative to serve active and reactive loads. First, the location and time of drastic events are identified as the initialization phase in this paper using a vulnerability analysis by a master–slave problem to find the weak points of the electric grid. Then, a rolling horizon model predictive control‐based approach is proposed to make corrective decisions to serve loads. Meanwhile, a new linearization approach for the Weymouth equation has been proposed based on the binary expansion method. The proposed linear resiliency‐oriented scheduling problem is tested on IEEE 33‐bus and IEEE 69‐bus integrated with a seven‐node gas distribution grid and a six‐station transportation railway. The analysis revealed that the coordination between electric and gas resources minimizes the not‐supplied load; MES units act as certain emergency tools to serve the critical loads. [ABSTRACT FROM AUTHOR]

Advent of distributed generation and progression towards an intelligent grid infrastructure within the domain of contemporary electrical power systems have created dynamic load profiles. Accompanying these developments, protective relays are faced with an evolving electrical load landscape and variable fault current conditions, resulting in disparate operational timings throughout the diurnal cycle. In light of these challenges, this paper delineates the formulation and simulation of a novel adaptive protection strategy for overcurrent relays, meticulously tailored to accommodate the fluctuations in electrical load. To construct a robust framework for this adaptive mechanism, a series of hypothetical fault current scenarios are meticulously crafted to activate the relays within the briefest time interval feasible. Further innovating within this sphere, this paper introduces a new hybrid algorithm, deftly amalgamating the strengths of three preeminent metaheuristic models: Improved Harmony Search, Particle Swarm Optimization, and Differential Evolution. Simulations and analyses substantiate the efficacy of the algorithm in optimizing the coordination among overcurrent relays aiming to uphold the overarching protective imperatives of the grid. For the IEEE 6‐bus system, the mean value of the objective function during 24 h in Monte Carlo is 292.6607 and very close to 272.0758 in the simulation of eight stochastic scenarios, which contributes to the validity of the approach in practical settings. Also, in the IEEE 30‐bus system, the results of the mean relay operation time set for the hours with the lowest and highest consumption load are 17.1297 and 14.8049 s, which reveals the increase in the operation speed of the relays. [ABSTRACT FROM AUTHOR]

Electric power substations, as critical components of modern power grids, are increasingly becoming targets for intentional physical attacks, including vandalism, theft, and sabotage. These threats, coupled with the potential for cyber-attacks and the weaponization of technologies, necessitate robust security measures and comprehensive auditing practices. Despite utilities providers' focus on understanding grid vulnerability and implementing physical security upgrades, there is a recognized gap in evaluating the effectiveness and long-term usability of these measures. This paper addresses the need for regular security audits to identify vulnerabilities and ensure the overall resilience of substations against evolving threats. The rationale behind this study is to propose a conventional auditing method that includes an auditing framework, checklists, inspections, and post-inspection suggestions. Through the systematic identification and addressing of vulnerabilities via security auditing, the framework aims to significantly enhance the resilience of substations against physical threats. This paper provides a comprehensive guideline for the physical security auditing procedure, which is essential for the reliable operation of the power grid. [ABSTRACT FROM AUTHOR]

Time-of-use pricing of electric energy, as an important part of the national policy of energy conservation and emission reduction, requires accurate electric energy data as support. However, due to various reasons, the electric energy data are often missing. To address this thorny problem, this paper constructs a CNN and GRU-based recovery model (RCNN-BiGRU) for electric energy data by taking the missing data as the output and the historical data of the neighboring moments as the input. Firstly, a convolutional network with a residual structure is used to capture the local dependence and periodic patterns of the input data, and then a bidirectional GRU network utilizes the extracted potential features to model the temporal relationships of the data. Aiming at the difficult selection of network structure parameters and training process parameters, an accelerated adaptive differential evolution (AADE) algorithm is proposed to optimize the electrical energy data recovery model. The algorithm designs an accelerated mutation operator and at the same time adopts an adaptive strategy to set the two key parameters. A large amount of real grid data are selected as samples to train the network, and the comparison results verify that the proposed combined model outperforms the related CNN and GRU networks. The comparison experimental results with other optimization algorithms also show that the AADE algorithm proposed in this paper has better data recovery performance on the training set and significantly better performance on the test set. [ABSTRACT FROM AUTHOR]

Nowadays, the microgrid system is characterized by a diversification of power factors and a complex network structure. Existing studies on microgrid fault diagnosis and troubleshooting mostly focus on the fault detection and operation optimization of a single power device. However, for increasingly complex microgrid systems, it becomes increasingly challenging to effectively contain faults within a specific spatiotemporal range. This can lead to the spread of power faults, posing great harm to the safety of the microgrid. The topology optimization of the microgrid based on deep reinforcement learning proposed in this paper starts from the overall power grid and aims to minimize the overall failure rate of the microgrid by optimizing the topology of the power grid. This approach can limit internal faults within a small range, greatly improving the safety and reliability of microgrid operation. The method proposed in this paper can optimize the network topology for the single node fault and multi-node fault, reducing the influence range of the node fault by 21% and 58%, respectively. [ABSTRACT FROM AUTHOR]

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O (N 2 log (N)) , superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification. [ABSTRACT FROM AUTHOR]

A reduction in greenhouse gases has become an inescapable requirement. An effective scenario for achieving carbon neutrality is to develop a hydrogen economy. Its success, however, requires strict control of the different processes involved in planned hydrogen chains. The energy chain considered in this paper is a stationary application which involves the production of hydrogen by electrolysis (a power-to gas process) and its combustion in gas turbine combined cycles to generate electricity (a gas-to-power process). In such applications, the need is twofold: (i) to control the risk of explosive atmospheres by performing safe gas detection in the presence of hydrogen and (ii) to secure the reliability of all chain processes using hydrogen-rich gases by achieving reliable analyses of these gases. This paper is dedicated to the development of hydrogen energy to decarbonize the thermal production of electricity. We will first describe the hydrogen chain that would best suit the power generation sector. Then, we will highlight the properties of hydrogen that are critical for its reliable operation. Finally, we will review the sensing technologies suitable for hydrogen-containing fuels. This review paper was published as part of a Joint Industrial Project (JIP) aimed at enabling the safe and reliable deployment of hydrogen energy. [ABSTRACT FROM AUTHOR]

The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable to wastewater treatment but can also be used for resource recovery from various organic wastes. The MFC usually requires an external controller that allows it to operate under controlled conditions to obtain a stable output voltage. Therefore, the application of a PID controller to the MFC is proposed in this paper. The design phase for this controller involves the identification of three parameters. Although the particle swarm optimization (PSO) algorithm is an advanced optimization algorithm based on swarm intelligence, it suffers from issues such as unreasonable population initialization and slow convergence speed. Therefore, this paper proposes an improved particle swarm algorithm based on the Golden Sine Strategy (GSCPSO). Using Circle chaotic mapping to make the distribution of the initial population more uniform, and then using the Golden Sine Strategy to improve the position update formula, not only improves the convergence speed of the population but also enhances convergence precision. The GSCPSO algorithm is applied to execute the described design process. The results of the simulation show that the designed control method exhibits smaller steady-state error, overshoot, and chattering compared with sliding-mode control (SMC), backstepping control, fuzzy SMC (FSMC), PSO-PID, and CPSO-PID. [ABSTRACT FROM AUTHOR]

Hydrogen energy, as a zero-carbon emission type of energy, is playing a significant role in the development of future electricity power systems. Coordinated operation of hydrogen and electricity will change the direction and shape of energy utilization in the power grid. To address the evolving power system and promote sustainable hydrogen energy development, this paper initially examines hydrogen preparation and storage techniques, summarizes current research and development challenges, and introduces several key technologies for hydrogen energy application in power systems. These include hydrogen electrification technology, hydrogen-based medium- and long-term energy storage, and hydrogen auxiliary services. This paper also analyzes several typical modes of hydrogen–electricity coupling. Finally, the future development direction of hydrogen energy in power systems is discussed, focusing on key issues such as cost, storage, and optimization. [ABSTRACT FROM AUTHOR]

Introduction. Solar photovoltaic (PV) has recently become very important especially in electrical power applications for countries with high luminosity because it is an effectively unlimited available energy resource. Depending on solar radiation and temperature, the PV generator has a non-linear characteristic with a maximum power point (MPP). The novelty is the efficiency improvement of a PV energy module, it is necessary to track the MPP of the PV array regardless of temperature or irradiation circumstances. Purpose. This paper presents the modeling and the digitally simulation under MATLAB/Simulink of a Fuzzy Logic Controller based on Voltage Perturbation Algorithm (FLC-VPA) applied to PV battery charging system, which consists of PV module, DC-DC boost converter, MPP tracking (MPPT) unit and battery storage. Methods. The DSP1104 is then used to experimentally implement this MPPT algorithm for real-time driving. The obtained results show the high precision of the proposed FLC-VPA MPPT around the optimal point compared to the conventional VPA under stable and changing meteorological conditions. Practical value. The experimental results approve the effectiveness and validity of the proposed total control system in the PV system. References 30, tables 3, figures 17. [ABSTRACT FROM AUTHOR]

In the field of statistics, uncertain regression analysis occupies an important position. It can thoroughly analyze data sets contained in complex uncertainties, aiming to quantify and reveal the intricate relationships between variables. It is worth noting that the traditional least squares method only takes into account the reduction in the deviations between predictions and observations, and fails to fully consider the inherent characteristics of the correlation uncertainty distributions under the uncertain regression framework. In light of this, this paper constructs a statistical invariant with symmetric uncertainty distribution based on the observations and the disturbance term. It also proposes the least squares estimation of unknown parameters and disturbance term in the uncertain regression model based on the least squares principle and, combined with the mathematical properties of the normal uncertainty distribution, gives a numerical algorithm for solving specific estimates. Finally, in order to verify the effectiveness of the least squares estimation method proposed in this paper, we also design two numerical examples and an empirical study of forecasting of electrical power output. [ABSTRACT FROM AUTHOR]

The widespread use of static converters for controlling electrical machines and the concern for electrical power quality in industrial environments provide an opportunity for utilizing these devices to enhance the power quality. In this context, this work presents a back-to-back converter model for driving induction machines. The converter is designed to correct the power factor of the point common coupling (PCC), compensate for harmonic currents (acting as an active filter), and withstand voltage sags. The necessary control system models were developed, and an alternative implementation for these functions in the converter was proposed. The results demonstrate the technical feasibility of this solution, as the converter operated within its nominal limits by compensating for harmonics and reactive power. Moreover, the equipment showed resilience to severe voltage sags. The contribution of this paper focuses on the multifunctionality of the frequency converter for driving induction machines. It emphasizes the advantage of the inverter in improving power quality in industrial environments through reactive power compensation and harmonic current compensation, thus functioning as an active power filter. Additionally, it is worth highlighting its ability to handle voltage dips. In this regard, this paper contributes by providing an operational strategy for driving the induction machine during such transients. [ABSTRACT FROM AUTHOR]

The paper addresses rare issue in cable ampacity calculations, namely the presence of discontinuities along the routes. One which occurs in almost all cable installations is the presence of joints. In a standard cable rating analysis, the joints are ignored, mostly because of difficulties in building analytical models that represent the heat transfer phenomena within them. However, they can be a limiting part of the cable rating and, therefore, there is a need to model them correctly. This paper introduces an analytical algorithm for cable rating calculations in the presence of discontinuities with an emphasis on cable joints. New developments are illustrated by several numerical examples. [ABSTRACT FROM AUTHOR]

Effective quality management plays a pivotal role in ensuring the smooth operation of smart city systems, which have significant implications for safety, accessibility, affordability, and maintainability. Dependability of autonomous systems is of utmost importance, as achieving satisfactory levels of availability and reliability poses considerable challenges. Smart cities are characterized by interconnected sub-architectures, encompassing vehicle monitoring, sidewalk monitoring, and building monitoring, all of which need to function efficiently. Analytical models such as Petri nets, Markov chains, and fault trees are well-suited for evaluating complex scenarios in the context of smart cities. This paper presents analytical models that utilize fault tree and Markov chain techniques to assess the availability and reliability of smart city monitoring systems. The model is divided into shared and non-shared components, with non-shared components being specific to certain contextual applications, while shared components, such as data processing and electrical power, are essential for all smart city monitoring and management systems. The study underscores the ease with which the fault tree model can enhance availability by modifying failure requirements and resources. Case studies provide concrete examples of how availability improved from 95.3 to 99.8% by varying a configuration known as "KooN" in multiple components. This paper takes a comprehensive approach to evaluating the dependability of smart city architectures and contributes advancements, such as hierarchical modeling, sequential sensitivity analysis, and the "KooN" analytic method. These contributions expand the existing knowledge and methodologies in smart city dependability analysis. Moreover, this work aims to serve as a practical tool to assist smart city managers in optimizing their proposals. All modeling aspects and parameters are detailed thoroughly to enable effective implementation of the proposed approach by anyone using it. [ABSTRACT FROM AUTHOR]

Dual-Active-Bridge (DAB) DC–DC converters are becoming increasingly favored for their efficiency in transferring electrical power across varying voltage levels. They are crucial in enhancing safety and reliability in various fields, such as renewable energy systems, electric vehicles, and the power supplies of electronic devices. This paper introduces a new control strategy for bidirectional isolated DAB DC–DC converters, implementing a Double Integral Super Twisting Sliding Mode Control (DI-STSMC) to accurately regulate the output voltage and current. The approach starts with a state-space representation to mathematically model the DAB converter. In light of model uncertainties and external disturbances, a robust DI-STSMC controller has been formulated to optimize the DAB converter's output performance. This method achieves zero steady-state error without chattering and provides a quick response to fluctuations in load and reference changes. The validity of the proposed technique is demonstrated through simulation results and a control hardware-in-the-loop (CHIL) experimental setup, using Typhoon HIL 606 and Imperix B-Box RCP 3.0 on a 230 W DAB converter. Furthermore, the paper offers a comparative analysis of the DI-STSMC with other control strategies, such as the proportional-integral (PI) controller, standard sliding mode control (SMC), and integral sliding mode control (ISMC). [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of the potential for integrating renewable energy sources to meet the growing electricity and hydrogen demand in the Tunisian Sahel region, focusing particularly on solar and wind energies. The feasibility of installing a hybrid solar-wind energy system capable of producing both electricity and hydrogen is evaluated. With the help of the available solar and wind resources combined, the system not only generates electric power, but also produces hydrogen gas through electrolyzation, hence offering a multipurpose solution in terms of storage and supply. This flexibility is crucial due to the variability of renewable resources, which change daily and seasonally. The paper outlines the optimization process for designing the hybrid system deploying HOMER Pro software, according to local climatic conditions and demand profiles. The economic analysis reveals that the system can produce an average of 101.8 kg of hydrogen daily with a total photovoltaic capacity of 3,000 kWp, resulting in a project net cost estimation of approximately 5,494,912 euros. This analysis provides valuable insights for stakeholders considering similar projects, including the costs associated with photovoltaic systems, electrolyzers, and hydrogen storage solutions. [ABSTRACT FROM AUTHOR]

This article discusses the role of power electronic converters in smart grids and electric vehicle applications. The authors highlight the technical connection between renewable energy, smart grids, energy storage, and electric vehicles. The special issue includes twelve accepted papers that cover topics such as high gain DC-DC converters, power converters for electric vehicle and motor drive applications, and power system applications. The papers present various novel topologies and control techniques, indicating the rapid growth and future potential of power converters in these fields. The authors express their appreciation to the contributing authors and anonymous reviewers for their valuable contributions to this special issue. [Extracted from the article]

In the not-too-distant future, humans will return to the Moon and step foot for the first time on Mars. Eventually, humanity will colonize these celestial bodies, where living and working will be commonplace. Energy is fundamental to all life. The energy that people use to sustain themselves on Earth, and in particular on these other worlds, is the integrated, safe production of electrical power, day and night. This paper proposes a radically new solution to this problem: Solar Tracking by day and a Solar Rechargeable Calcium Oxide Chemical Thermoelectric Reactor by night. Called the "Robotic End Effector for Lunar and Martian Geological Exploration of Space" (REEGES) Day/Night Power Generator Station, this form of thermoelectric power generation is mathematically modeled, simulation is performed, and a concept model design is demonstrated in this paper. The results of the presented simulation show the maximum total system output capability is 9.89 V, 6.66 A, and 65.9 W, with an operating time of up to 12 h, through a scalable design. This research provides instructions to the Space Research Community on a complete and novel development methodology for creating fully customized, configurable, safe, and reliable solar/thermoelectric day/night power generators, specifically meant for use on the Moon and Mars, using the Proportional-Integral-Derivative++ (PID++) Humanoid Motion Control Algorithm for its operation on a computationally lightweight microcontroller. [ABSTRACT FROM AUTHOR]

The aim of the electric tricycle is to bring increased mobility to impaired persons. Presently, hand-powered tricycles are used by numerous members of the impaired community, but some current users of hand-powered tricycles do not have the physical strength or collaboration to propel themselves on the tricycle with their arms and hands. The aim of the proposed paper is to add electric power to the current hand-powered tricycle to provide tricycle users with improved mobility, providing them with more freedom and making a donation to the community. This paper develops an inclusive and cost-effective electric tricycle designed specifically for individuals with mobility challenges. The proposed tricycle is equipped with a 350-watt motor and has a cargo capacity of over 100 kg. Using IoT technology, the proposed system includes features similar to real-time position shadowing, on/off announcements through mobile and dispatch, and clear on/off suggestions. This innovative result addresses the unique requirements of hindered individuals, promoting availability, autonomy, and enhanced mobility in a socially conscious manner. [ABSTRACT FROM AUTHOR]

This document discusses recent advances and challenges in emerging power systems, specifically focusing on the integration of renewable energy sources with conventional power networks. The development of renewable energy solutions has been driven by concerns about diminishing fossil fuels, increasing energy demand, pollution, and global warming. The integration of renewable energy sources presents challenges in terms of operation, control, and protection practices. The document highlights 16 research papers that cover various topics related to power systems and the challenges faced by emerging power systems. These papers explore concepts such as floating wind farms, coordinated control of fast charging stations and distributed energy resources, AI tools for voltage analysis, open power meters, fault location methods, load forecasting, inductive compensation, blockchain-based energy markets, vibration suppression in smart structures, and more. The document concludes by inviting further papers for a second volume on recent advances and challenges in emerging power systems. [Extracted from the article]

Full- and reduced-order observers have been used in many engineering applications, particularly for energy systems. Applications of observers to energy systems are twofold: (1) the use of observed variables of dynamic systems for the purpose of feedback control and (2) the use of observers in their own right to observe (estimate) state variables of particular energy processes and systems. In addition to the classical Luenberger-type observers, we will review some papers on functional, fractional, and disturbance observers, as well as sliding-mode observers used for energy systems. Observers have been applied to energy systems in both continuous and discrete time domains and in both deterministic and stochastic problem formulations to observe (estimate) state variables over either finite or infinite time (steady-state) intervals. This overview paper will provide a detailed overview of observers used for linear and linearized mathematical models of energy systems and review the most important and most recent papers on the use of observers for nonlinear lumped (concentrated)-parameter systems. The emphasis will be on applications of observers to renewable energy systems, such as fuel cells, batteries, solar cells, and wind turbines. In addition, we will present recent research results on the use of observers for distributed-parameter systems and comment on their actual and potential applications in energy processes and systems. Due to the large number of papers that have been published on this topic, we will concentrate our attention mostly on papers published in high-quality journals in recent years, mostly in the past decade. [ABSTRACT FROM AUTHOR]

In recent years, Poland has seen a rapid increase in installed capacity in the renewable energy sources (RES) sector. This increase mainly concerns weather-dependent sources such as PV and wind installations, whose intermittent operation destabilizes the Polish power system (PPS). The solution to fluctuations in PPS operating parameters are stable and controllable RES sources, among which demand-driven biogas plants (with an energy storage in the form of biogas) have a special development potential. The aim of this paper was to describe the construction and principles of operation of peak biogas plants and to analyze the possibilities of including them in the operation of PPS. The paper shows the fundamental differences between the operation of typical biogas plants (producing electricity and heat continuously) and demand-driven biogas plants, in which biogas production takes place continuously, while electricity production occurs periodically, during the highest demand for power during the day. It was found that demand-driven biogas plants are a promising alternative to the previously used coal-fired units, battery energy storage units and pumped-storage power plants and in the future they may act as a stabilizer of the National Power System, achieving available electrical power of up to 11.1 GW. [ABSTRACT FROM AUTHOR]

Batteries have been integral components in modern vehicles, initially powering starter motors and ensuring stable electrical conditions in various vehicle systems and later in energy sources of drive electric motors. Over time, their significance has grown exponentially with the advent of features such as "Start & Stop" systems, micro hybridization, and kinetic energy regeneration. This trend culminated in the emergence of hybrid and electric vehicles, where batteries are the energy source of the electric traction motors. The evolution of storage for vehicles has been driven by the need for larger autonomy, a higher number of cycles, lower self-discharge rates, enhanced performance in extreme temperatures, and greater electrical power extraction capacity. As these technologies have advanced, so have they the methods for their disposal, recovery, and recycling. However, one critical aspect often overlooked is the potential for battery reuse once they reach the end of their useful life. For each battery technology, specific regeneration methods have been developed, aiming to restore the battery to its initial performance state or something very close to it. This focus on regeneration holds significant economic implications, particularly for vehicles where batteries represent a substantial share of the overall cost, such as hybrid and electric vehicles. This paper conducts a comprehensive review of battery technologies employed in vehicles from their inception to the present day. Special attention is given to identifying common failures within these technologies. Additionally, the scientific literature and existing patents addressing regeneration methods are explored, shedding light on the promising avenues for extending the life and performance of automotive batteries. [ABSTRACT FROM AUTHOR]

Science and technology, with their miraculous advancements, have fascinated human life to such an extent that imagining a world without these innovations is hardly possible. While technology is on the rise, an increase in immoral activities should also be noted. From a technical perspective, "power theft" is a non-ignorable crime that is highly prevalent and directly affects the economy of a nation. Today's life is unimaginable without electricity, almost all things, equipment, and appliances used daily rely on electric power to run smoothly. Solar energy is an alternative but has limitations, such as environmental changes and high initial costs. From rural to urban areas and from domestic to industrial sectors, the use of electricity has increased, but so has power theft. Detecting and eradicating such crimes with the help of the developing scientific field is the "need of the hour." With these views, the paper was conceived and designed. The paper provides a complete and comprehensive tool to stop power theft, which is extremely simple to understand and easy to implement. [ABSTRACT FROM AUTHOR]

The generation of renewable energy encounters numerous obstacles, chiefly the unpredictability of renewable sources. When new energy generation prediction is lower than expected, it needs to be supplemented by other energy sources, which may lead to instability in the power grid if the deviation is large. When the prediction of new energy generation exceeds expectations, it will lead to energy waste. To address these issues, this paper proposes a Deep Neural Network-based fusion framework, which can improve the prediction accuracy of new energy and achieve a low-carbon, economical, and stable power grid. Within this structure, feature engineering is conducted initially. Subsequently, a combination of traditional tree algorithms like the Gradient Boosting Decision Tree, linear approaches such as the Least Squares Method, and nonlinear neural networks, for instance, Recurrent Neural Networks, are employed for individual model regression purposes. In the final step, both the original time-series data and the outcomes from the individual models are integrated into a deep neural network to derive the ultimate forecasting outcomes. By using our method, the electricity cost has been reduced by 26.5% and the carbon emissions have been decreased by 14.2%. Experiments have been carried out using actual community data, confirming the effectiveness of the proposed approach. The findings indicate that the integration of DNN with traditional and modern machine learning techniques can significantly improve the forecasting of renewable energy generation. This advancement contributes to the creation of a more sustainable, economical, and stable power grid. [ABSTRACT FROM AUTHOR]

Park microgrids, valued for their efficiency and flexibility, require privacy-conscious energy management to ensure a trusted scheduling and trading environment. This paper, focusing on park microgrids with shared energy storage, designs an energy management strategy that comprehensively considers shared energy storage, scheduling transparency, and privacy security. First, a blockchain-based energy management platform is established, forming an energy dispatch consensus committee to execute decentralized scheduling management and decision-making. Next, an optimized energy scheduling smart contract for park microgrids is designed, considering Time-of-Use (ToU) pricing and storage arbitrage to formulate the day-ahead electricity purchase and sales plans as well as the shared energy storage operation plans. Then, a privacy protection strategy based on the Shamir secret sharing scheme is proposed, effectively preventing data leakage during blockchain interactions. Finally, through case analysis, the superiority of the proposed method in microgrid optimized scheduling, data tamper-resistance, and privacy protection is demonstrated. [ABSTRACT FROM AUTHOR]

Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy. [ABSTRACT FROM AUTHOR]

Hydraulic actuation systems are widely used in industries such as aerospace, the marine industry, off-highway vehicles, and manufacturing. There has been a shift from the hydraulic distribution of power from a centralized supply to electrical power distribution, to reduce the maintenance requirements and weight and improve the efficiency. However, hydraulic actuators have many advantages, such as power density, durability, and controllability, so the ability to convert electrical to hydraulic power locally to drive an actuator is important. Traditional hydraulic pumps are inefficient and unsuitable for low-power applications, making piezopumps a promising alternative for the conversion of electrical to hydraulic power in the sub-100 W range. Currently, the use of piezopumps is limited by their maximum power (typically a few watts or less) and low flows. This paper details the design, simulation, and testing of a multi-cylinder piezopump designed to push the envelope of the power output. The simulation results demonstrate that pumps with two or three cylinders show increasing benefits in terms of hydraulic and electrical performance due to the reduced flow and current ripple compared to a single-cylinder pump. The experimental results from a two-cylinder pump confirm this, and the effect of the phase relationship between the drive signals is investigated in detail. The experimental pump has fast-acting disc-style reed non-return valves, allowing piezostack drive frequencies of up to 1.4 kHz to be used. Custom power electronics tailored to the pump are developed. These features are critical in demonstrating the potential for multi-cylinder piezopumps to play an important role as a future actuation solution. [ABSTRACT FROM AUTHOR]

The green and low carbon transition and development of the electricity industry is the most crucial task in realizing the "dual-carbon target", and it is urgent to explore the incentive and subsidy mechanism to promote green electricity consumption and the cost-sharing strategy of carbon reduction, to alleviate the pressure of carbon abatement cost of each subject of the electricity supply chain. Against this background, this paper takes into account the low-carbon subsidies provided by the government and the incentive subsidies for users, and studies the optimal decision-making of each subject in the electricity supply chain, so that each of them can obtain the optimal profit and achieve carbon emission reduction at the same time. Firstly, taking into account the direct power purchase mode of large users and the electricity-selling companies emerging after the reform of the power sales side, we have established a cooperative mechanism for sharing the cost of carbon emission reduction in the electricity supply chain and clarified the relationship between the supply and demand of electricity among the main parties. Subsequently, considering government low-carbon subsidies and user incentive subsidies, the optimal decision-making model is established under two scenarios of decentralized and centralized cooperative games in the supply chain, respectively, with the objective of maximizing profits and carbon reduction rates. Solving for the optimal proportion of carbon abatement costs shared by each participant in the electricity supply chain in achieving game equilibrium. Finally, we analyze the role of the government's low-carbon subsidies, users' incentive subsidies, and other factors on the profit and carbon reduction effect of the electricity industry through the example analysis and further analyze the impact of carbon abatement cost-sharing measures to provide recommendations for the electricity industry to realize low-carbon abatement and make decisions. [ABSTRACT FROM AUTHOR]

The electric distribution sector is facing significant challenges in integrating distributed energy resources (DERs), with most assessment methodologies being primarily tested on standard feeders. However, given the nature of electric distribution systems, each feeder is unique and constantly changing, emphasizing the need for customized and updated feeder models for local quantitative studies. These studies encompass network operation, expansion planning, and DER integration. In response to this context, this paper proposes a methodology for extracting and processing information from public data to model up-to-date electric feeders. As these feeder models represent real operating circuits, they can be utilized by utilities, investors, and consumers. The proposed approach involves processing data from the Brazilian utilities' geographic database (BDGD, in Portuguese) to develop structured simulations of distribution feeders using OpenDSS software. The BDGD standards are established by the Brazilian National Electric Energy Agency (ANEEL). In order to validate the methodology, two case studies are presented evaluating the impacts of distributed generation on real feeders from the Brazilian utilities: Minas Gerais Electric Power Company (CEMIG) and Bahia State Electricity Company (Neoenergia COELBA). [ABSTRACT FROM AUTHOR]

The exploitation of nature to convert energy to electrical power is the most important rule in power generation. Wind energy is one of the most important of those energies that are widely available, and its use does not affect the environment significantly compared to fossil energy. On the other hand, and in recent times, researchers have made great efforts in the field of intelligent control and optimization, which has led to great leaps in the development of these sciences. In this paper, a detailed study is proposed for filling the gaps and conducting an updating state-of-arts of the last pitch control methods in the wind turbine systems. The review is conducted by comparing the key requirements related to control, complexity, stability and speed rangeability. Furthermore, a new classification for the general controller is introduced according to the techniques. Several recommendations for future research related to the control and technical evaluation of wind energy are presented. In sum, the appropriate classification of such important issues and identification of their advantages and drawbacks may greatly contribute to find better solutions. [ABSTRACT FROM AUTHOR]

Electric power is crucial for economic growth and the overall development of any country. The efficient planning of distribution system is necessary because all the consumers mainly rely on the distribution network to access the power. This paper focuses on addressing distribution system challenges and meeting consumers' fundamental needs, such as achieving an improved voltage profile and minimizing costs within an environmentally sustainable framework. This work addressed the gap in the existing research by analysing the performance of both balanced and unbalanced systems within the same framework, specifically using the IEEE 33-bus and IEEE 118-bus test systems. Unlike prior studies that focused solely on either balanced or unbalanced systems, this work redistributed balanced loads into three-phase unequal unbalanced loads to create a more challenging unbalanced distribution network. The primary objective is to compare the effects of balanced and unbalanced loads on system the performances and to identify strategies for mitigating unbalanced load issues in each phase. Six optimization methods (PSO, TLBO, JAYA, SCA, RAO, and HBO) were employed to minimize losses, voltage variations, and other multi-objective function factors. Additionally, the study compared the cost of energy loss (CEL), emission factors, costs associated with distributed clean energy resources (DCER), and active and reactive power losses. Phase angle distortions due to unbalanced loads were also analysed. The results showed that among the optimization techniques tested (PSO, TLBO, JAYA, SCA, RAO, and HBO), the HBO method proved to be the most effective for the optimal allocation of distributed clean energy resources, yielding the lowest PFMO values and favourable outcomes across the technical, economic, and environmental parameters. [ABSTRACT FROM AUTHOR]

Inter-Turn Short Circuit (ITSC) faults do not necessarily produce high temperatures but exhibit distinct heat distribution and characteristics. This paper proposes a novel fault diagnosis and identification scheme utilizing an improved You Look Only Once Vision 8 (YOLOv8) algorithm, enhanced with an infrared image slicing-aided hyper-inference (SAHI) technique, to automatically detect ITSC fault trajectories in dry-type transformers. The infrared image acquisition system gathers data on ITSC fault trajectories and captures images with varying contrast to enhance the robustness of the recognition model. Given that the fault trajectory constitutes a small portion of the overall infrared image and is subject to significant background interference, traditional recognition algorithms often misjudge or omit faults. To address this, a YOLOv8-based visual detection method incorporating Dynamic Snake Convolution (DSConv) and the Slicing-Aided Hyper-Inference algorithm is proposed. This method aims to improve recognition precision and accuracy for small targets in complex backgrounds, facilitating accurate detection of ITSC faults in dry-type transformers. Comparative tests with the YOLOv8 model, Fast Region-based Convolutional Neural Networks (Fast-RCNNs), and Residual Neural Networks (Retina-Nets) demonstrate that the enhancements significantly improve model convergence speed and fault trajectory detection accuracy. The approach offers valuable insights for advancing infrared image diagnostic technology in electrical power equipment. [ABSTRACT FROM AUTHOR]

Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, China, 2Guangdong Power Grid Co., Ltd., Guangzhou, China, 3China Southern Power Grid Co., Ltd., Guangzhou, China To achieve load management optimization and timely failure warning for power transformers, as well as improve the reliability of the power network, this paper proposes a multiple time scale prediction method for top oil temperature (TOT) based on an adaptive extended Kalman filter (AEKF) algorithm. This method combines the Kalman filter (KF) algorithm and the D. Susa thermal model. The TOT, oil exponent and oil time constant are taken as state variables, while the ambient temperature and load current are used as input variables. The iterative optimization of the oil exponent and oil time constant is realized by comparing the estimated and observed TOT values. Moreover, the proposed method utilizes an adaptive noise estimator to correct the noise statistics parameters, which simplifies the initial noise setting and thus further improves the TOT prediction accuracy. A case study is conducted with two 110 kV transformers. The results show that comparing the thermal equivalent circuit model and the extended KF algorithm, the proposed method has a higher accuracy in the intraday ultra-short-term prediction on a 15-min time scale and day-ahead short-term prediction on a 24-h time scale for the TOT. [ABSTRACT FROM AUTHOR]

Active power filters (APFs) play a vital role in reducing the current harmonics and improving power quality. This work studies a shunt APF (SAPF) based on the threephase voltage source converter (VSC). This paper investigates the new robust control approach using error dynamics. Stable first-order error dynamics are considered when designing the control inputs. Three control inputs are obtained to control the currents at any phase by choosing and optimizing the appropriate parameters. This strategy is also simple to implement in practical applications because it is the same as the proportionalderivative controller design. In addition, the new control method can be utilized for any system with low dynamic information so that the destructive effects of lumped uncertainties in the output channels of the SAPF can be alleviated. The minimum voltage and current measurements are used to control the SAPF, and the grid current harmonics will be reduced by achieving the stabilization of tracking error dynamics. Some numerical simulations are performed by MATLAB software to confirm the proposed method. [ABSTRACT FROM AUTHOR]

This paper describes the use of an electric drive as an acoustic actuator for active noise cancellation (ANC). In the presented application, the idea is to improve the noise, vibration, harshness (NVH) characteristics of passenger cars without using additional active or passive damper systems. Many of the already existing electric drives in cars are equipped with the required hardware components to generate noise and vibration, which can be used as compensation signals in an ANC application. To demonstrate the applicability of the idea, the electrical power steering (EPS) motor is stimulated with a control signal, generated by an adaptive feedforward controller, to reduce harmonic disturbances at the driver's ears. As it turns out, the EPS system generates higher harmonics of the harmonic compensation signal due to nonlinearities in the acoustic transfer path using a harmonic excitation signal. The higher harmonics impair an improvement in the subjective hearing experience, although the airborne noise level of the harmonic disturbance signal can be clearly reduced at the driver's ears. Therefore, two methods are presented to reduce the amplitude of the higher harmonics. The first method is to limit the filter weights of the algorithm to reduce the amplitude of the harmonic compensation signal. The filter amplitude limitation also leads to a lower amplitude of the higher harmonics, generated by the permanent magnet synchronous machine (PMSM). The second method uses a parallel structure of adaptive filters to actively reduce the amplitude of the higher harmonics. Finally, the effectiveness of the proposed ANC system is demonstrated in two real driving situations, where in one case a synthetic noise/vibration induced by a shaker on the front axle carrier is considered to be the disturbance, and in the other case, the disturbance is a harmonic vibration generated by the combustion engine. In both cases, the subjective hearing experience of the driver could be clearly improved using the EPS motor as ANC actuator. [ABSTRACT FROM AUTHOR]

Energy harvesting is a clean technique for obtaining electrical energy from environmental energy. Mechanical vibrations are an energy source that can be used to produce electricity using piezoelectric energy harvesters. Vibrations and wind in bridges have the potential to produce clean energy that can be employed to supply energy to electronic devices with low consumption. The purpose of this paper was to validate the functioning of an energy harvester and test the electrical power generation potential of a system based on the oscillation of a rod with a tip mass to stimulate piezoelectric transducers by impact. The obtained results showed the electric energy productions for different test conditions. Experimentally, the proposed structure produced 0.337 µJ of energy after 14 s of testing. In addition, after one hour of operation, an estimated production of 10.4 mJ was obtained, considering four stacks of 25 piezoelectric disks each when periodic impacts of 50 N at 5.7 Hz stimulated the transducers. In future work, we will focus on taking advantage of the vibrations produced in the proposed structure induced by the mechanical vibration of bridges and vortex-induced vibration (VIV) through interaction with wind to produce clean energy that is useful for low-power applications. [ABSTRACT FROM AUTHOR]

High-speed electric drive units promise improved power density and, theoretically, driving range of battery electric vehicles. An essential step of the development process is extensive testing of the drive unit on a test rig. In particular, at a high rotational speed level, experimental testing can be challenging. This paper describes a test rig for investigating the overall function of a high-speed drive unit and the transmission's efficiency and dynamics. The high-speed drive unit developed in the Speed4E research project was the reference drive unit. The test rig is based on the concept of electrical power circulation. Thus, the test rig can be used universally for different drive unit designs and operating modes. A reaction torque measurement unit was developed to enable measurements at high rotational speeds. Simultaneously, this unit allows robust measurements at low costs. The expected measurement uncertainties of torque, rotational speed, transmission efficiency, and power losses were calculated using the Monte Carlo method. The results demonstrate that the developed torque measurement unit combines precise torque measurement with a robust design and low costs, making it competitive with state-of-the-art solutions for torque measurement at high speeds. [ABSTRACT FROM AUTHOR]

As electric vehicle (EV) adoption increases, transportation services will shift dependence from liquid fuel infrastructure to electric power infrastructure. Since transportation plays a major role in disaster response and recovery, this shift in dependence has important implications for coupling electrical grids and transport resilience. The implications for the electrical grid for southwestern British Columbia, Canada are examined, motivated by this region's high EV adoption rate and the potential for a catastrophic magnitude 9.5 earthquake. A comparison of the resilience of the electrical infrastructure compared to the fuel infrastructure is provided and approaches for increasing resilience in the region for EV power supply are discussed. This paper compiles lessons learned from past large earthquakes in Chile, Japan, and New Zealand with consideration given to successes and failures. While this paper was written with a focus on southwestern British Columbia, many of the suggestions for increasing power system resilience could be applied in other seismically active locations during the transition to EVs. This paper also considers integrated disaster resilience planning for the changing transport landscape from fuel vehicles to EVs. [ABSTRACT FROM AUTHOR]

This paper is devoted to improve the design solution of combined thermo-photovoltaic systems which works in concentrated solar radiation. The main problem is ensuring the efficient heat removing from the photoreceiving surface of such systems and transferring this heat energy to liquid heat carrier. In case of using concentrated solar radiation with high density of energy onto the photoreceiving surface the problem of intensifying the heat exchange processes is critical for ensuring the efficient and long-term work of combined thermo-photovoltaic systems. Based on the analysis of thermal processes, the design of a heat exchange unit for a combined photovoltaic plant is proposed. The main design feature of proposed solution is microchannels made on the back plate of photoreceiving surface. It is shown that this design creates a transient regime of coolant flow, which allows efficient cooling of solar cells under conditions of concentrated solar radiation and potentially will allow to receive record values of heat exchange coefficient between the coolant and the upper plate of the radiator. An analysis technique using criterial hydrodynamic equations and numerical modeling were used; the results obtained in both cases are in good agreement. The efficiency, thermal and electrical characteristics of a combined photovoltaic system with solar radiation concentration have been assessed. Preliminary estimations have been made for the specific parameters of installed energy characteristics of a heat-electric power plant, which under AM1 conditions and 400 times the concentration of solar radiation for solar cells with an efficiency of 30 % have the following values: electrical power 650 W, thermal power 2370 W, total installed net power 3020 W, overall efficiency 87 % which are significantly higher than parameters achieved for existing solar power plants. [ABSTRACT FROM AUTHOR]

Photovoltaic technology has become one of the major renewable ways to generate electric power. However, the mismatch between the incident solar spectrum and photo-electric response efficiency of solar cells severely constrains their performance. Hence, spectral modification technologies, e.g., up-conversion (UC), down-conversion (DC), and luminescent down-shifting (LDS) technologies have been applied widely in the photovoltaic field to reform the incident spectrum to match the best response band possible. In this paper, we review the latest developments of the three technologies above in terms of material selection, optical characteristics, and photovoltaic performance. It is found that the three most popular materials for conversion are NaYF4: Er3+, Yb3+, and Yb3+. The excitation bands for the three technologies are 800–1550 nm, 250–488 nm, and 250–488 nm, respectively, while the emission bands are 523–669 nm, 520–1031 nm, and 490–1010 nm, respectively. Furthermore, issues hindering the development of spectral modification technologies are pointed out, e.g., low absorption efficiency, poor quantum conversion efficiency, and hurdles in commercialization. Finally, suggestions and solutions to address the above-mentioned issues are provided. [ABSTRACT FROM AUTHOR]

This research proposes the utilization of Peltier modules to convert electrical energy from thermal energy to show its potential as a renewable energy source for residential and commercial application. The study, whose results are presented in this manuscript, was conducted in the city of Monterrey, located in the northeast of Mexico. The energy source was tested and analyzed utilizing a set of statistical metrics and further comparison against experimental test results. The Distrito Tec area in Monterrey city is an academic complex and it was chosen for this study, which consisted of the indirect measurement of the average annual heat energy stored within the buildings' cement structure. The aim was to obtain the annual accumulated electrical power in Wh per year that Peltier modules could provide in Distrito Tec, which is located in a city with solar irradiation levels above the world average. The proposal in this paper could encourage further investigation regarding this energy that is currently waste heat. More specifically, the results of this research highlight the importance of thermoelectric modules and seek to motivate research to improve their properties and make them more efficient and more viable as well. Thermoelectric modules have the potential to be part of the solution to sustainable development as presented in the United Nations SDG-7—ensure access to affordable, reliable, sustainable and modern energy for all. [ABSTRACT FROM AUTHOR]

It is essential to protect control, regulation and data storage units used in automated robotic manufacturing from the consequences of harmful electrical grid harmonics. In this paper, the effectiveness of active/passive harmonic filters built into LED lamps, test benches, and industrial robots was investigated (in the latter two cases, light and heavy load states were applied). Based on network analysis, it was found that the built-in passive harmonic filters of the luminaries were ineffective because the THD of the current was approximately two or three times the permissible value according to the IEEE 519-2022 standard. It was proved that the built-in active harmonic filter of the test bench at a heavy load worked properly, but at a light load it was not effective, while the magnitude of harmonic distortion of the industrial robot exceeded the allowed level (three times the limit at a light load, seven times the limit at a heavy load). Further measurements were also performed at three locations: an engineering room, a gearbox house machine tool, and an office room (where there were no built-in filters). The results proved that in each case, there was a significant exceedance of the limit value. Therefore, protection against harmonics needs to be installed, and then the measurements must be repeated. [ABSTRACT FROM AUTHOR]

In this paper, a new linear-based closed-loop control method for a Solid-State Transformer (SST) has been proposed. In this new control method, individual current and voltage loops for each of the power conversion stages (AC-DC, DC-DC, DC-AC) are implemented. The feedback between the input and output control signals for each loop is achieved through the voltage on the DC link capacitors and the current transferred between the converters. This enables the SST to be controlled easily in a linear-based closed-loop manner without the need for complex computations. In order to evaluate the performance analysis of the proposed control system, a simulation of an SST with approximately 10 kVA apparent power was performed. Based on the obtained simulation results, the response time of the proposed control method for dynamic load variations was proved to be in the range of 40 milliseconds, and it has been observed that this method allows electrical power to be transferred from the load to the grid. The power factor value of SST under inductive load is measured to be approximately 99%, and the overall system efficiency is 96% and above, indicating that this proposed new control method has very high performance. [ABSTRACT FROM AUTHOR]

A grid-forming voltage source converter with an integrated step-down transformer could be a promising solution for supplying low-voltage alternating current loads from a medium-voltage direct current supply. However, it may require a control system that gathers feedback signals from both the primary and secondary sides of the transformer, which in turn complicates the derivation of a standard form linear model. The absence of such a model complicates control tuning, as well as the assessment of dynamics and stability of the converter system. The objective of this paper is to address this gap in knowledge. For the case study, a conventional H-bridge converter with a step-down transformer and an α β -frame dual-loop grid-forming controller is considered. Initially, comprehensive guidelines on deriving a standard form linear model for this converter system are presented. Then, the impact of controlling the VSC in a d q frame and the changes in the transformer vector group on the small-signal model of the VSC are analyzed. The aspects of control tuning are also discussed in detail, and the model's accuracy and efficacy are validated both theoretically and through control hardware-in-the-loop (C-HIL) tests using a Typhoon HIL setup. [ABSTRACT FROM AUTHOR]