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This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It emphasizes the need to improve the reliability and resilience of modern power systems in the face of various threats. The editorial highlights the importance of modeling, assessing, and enhancing the reliability and resilience of power grids that are increasingly incorporating grid-edge resources. The document also provides a summary of the papers included in a special issue of the journal, which cover topics such as planning reliable energy systems, analyzing power grid operations, and enhancing service restoration and grid-support load response. Additionally, the text discusses the issue of equity in power distribution system restoration processes and proposes a model called the equity-aware power distribution system restoration model. The model aims to balance equity and resilience in restoring power after outages by focusing on fair distribution of energy resources while maintaining operational efficiency. The text also includes brief biographies of several authors who have contributed to the field of power electronics and power system reliability. [Extracted from the article]

Municipal water supply system (WSS) consist of different pumping stages viz. intake, water treatment plant (WTP) and intermediate pumping station (IPS). Usually, the power supply for WSS is obtained through public power tapping sources. However, this often leads to load shedding and disruption of the water supply. This paper focuses on the concept, considering WSS as a multi-source multi-area microgrid scheme, this includes renewable energy sources (RES) such as solar, wind, etc. Moreover, the study incorporates a Battery Energy Storage System (BESS) and a Diesel Engine Generator (DEG) to provide power supply during peak demand at each pumping station. Frequency control is essential for optimizing system performance. This paper proposes Enhanced Harris Hawks Optimization Algorithm (EHHO) based PID controller for regulating the frequency in the multi-microgrid-based water supply system. The proposed controller is implemented in MATLAB simulation software, and its response is compared with other optimization methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Moreover, implementation and comparison of higher degree order controller such as 3DOF-FOPIDN controller and 3DOF-TIDN controllers are tested under PSO method to observe the performance as well as robustness of the controller. The results indicate that the proposed controller provides better performance in controlling the load frequency deviation, thus improving the efficiency and reliability of the multi-microgrid system for consideration of municipal water supply. [ABSTRACT FROM AUTHOR]

With the continuous breakthrough of natural language processing, the application of intelligent question-answering technology in electric power systems has attracted wide attention. However, at present, the traditional question-answering system has poor performance and is difficult to apply in engineering practice. This paper proposes an improved BERTserini algorithm for the intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text-segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is the answer-generation and source-retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for the manual organization of professional question–answer pairs, thereby effectively reducing the manual labor cost compared to traditional question-answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers. [ABSTRACT FROM AUTHOR]

Inter-turn short circuit fault (ISCF) is the main fault type of dry-type air-core reactors (DARs), and the reactor burnout event caused by winding ISCF seriously threatens the safe and stable operation of the electric power system. In order to effectively curb the further worsening of ISCF in DARs, a fault detection method based on frequency response analysis was proposed. First, the effect of ISCF on the frequency response curve of the windings was investigated based on the distributed parameter model of DARs. Then, four sets of eigenvalues were proposed for ISCF diagnosis based on the influence law and curve data analysis theory. Finally, based on the eigenvalues, the support vector machine model optimized by the sparrow search algorithm was used to effectively classify the degree of ISCF of the reactor, and the accuracy reached 96%. Furthermore, a test platform was built based on the theoretical analysis for experimental verification, and the test results are consistent with the simulation results. Hence, the method proposed in this paper could effectively classify the ISCF and its severity in DARs. The research in this paper could provide effective guidance for the diagnosis of ISCF in DARs. [ABSTRACT FROM AUTHOR]

The methods of artificial intelligence (AI) have been used in the planning and operation of electric power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI, including the use of machine learning (ML) in the electric power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way, considering the supervised and unsupervised learning methods. Based on this, three complex application examples, being wind power generation forecasting, smart grid security assessment (using two methods), and automatic system fault detection are presented and discussed in detail. A summary and outlook conclude the paper. [ABSTRACT FROM AUTHOR]

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system. [ABSTRACT FROM AUTHOR]

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode. Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line. Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets. [ABSTRACT FROM AUTHOR]

Pre-disaster protection strategies are essential for enhancing the resilience of electric power systems against natural disasters. Considering the budgets for protection strategies, the dependency of other infrastructure systems on electricity, and the uncertainty of disaster scenarios, this paper develops risk-neutral and risk management models of strategies for pre-disaster protection. The risk-neutral model is a stochastic model designed to maximize the expected value of resilience (EVR) of the integrated system. The risk management model is a multi-objective model prioritizing the minimization of risk metrics as a secondary goal alongside maximizing the EVR. A case study conducted on the energy infrastructure systems in the Greater Toronto Area (GTA) validates the effectiveness of the models. The findings reveal the following: (i) increasing the budget enhances the EVR of the integrated system; however, beyond a certain budget threshold, the incremental benefits to the EVR significantly diminish; (ii) reducing the value of the downside risk often results in an increase in the EVR, with the variation in Pareto-optimal solutions between the two objectives being non-linear; and (iii) whether for the risk-neutral or risk management protection strategies, there are reasonable budgets when considering disaster intensity and the cost of protection measures. The models can help decision-makers to select effective protection measures for natural disasters. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrated a distributed feedback fiber-laser- (DFB-FL) based ultrasound detection system with a high signal-to-noise ratio (SNR), high sensitivity and wide frequency response range. The DFB-FL was fabricated by UV-inscribing a π phase-shifted grating in the Erbium–Ytterbium co-doped fiber using the phase mask method. The theory of DFB-FL and the sensing principle has been discussed and analyzed. The sensing signal was demodulated via an unbalanced Mach–Zehnder interferometer (MZI) system. The experimental results showed that the sensitivity of the sensor reached 75.18 mV/kPa and the flat frequency response range covered over 30 MHz, which covered the ultrasonic detection frequency range of abnormal discharge in the electric power system. The SNR of this DFB-FL was 42.9 dB, and the corresponding noise-equivalent pressure was calculated as 0.12 kPa. [ABSTRACT FROM AUTHOR]

Power system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying system analysis, the differential Riccati equation (DRE) plays a vital role when the power system is subjected to a severe disturbance. The Möbius transformation is proposed in this paper to solve the DRE with singularity issues. It is shown that the solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. The proposed method can be used in large‐signal stability analysis to predict instability and make the stability analysis more efficient. Additionally, the vector‐DRE is proposed to generalize the index in a large‐scale power system. Results show that analyzing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system. [ABSTRACT FROM AUTHOR]

This paper was conceived to investigate some central issues related to the upheaval of current energy scenarios in Sicily. New power connection lines that are about to be built in the Mediterranean area, planned with a view to a constantly increasing renewable generation, encourage the carrying out of analyses on how the Sicilian electric power system will be able to make itself ready to support large power injections, especially due to new renewables plants that will be established in the region soon. This study, carried out in close collaboration with the Italian TSO Terna S.p.A and the University of Palermo, defines what the impacts of new renewable power plants will be on the Sicilian power transmission grid under intact and non-intact grid conditions. This study consists of steady-state simulations carried out using WinCreso® software version 7.62.1-3 in two energy scenarios estimated for the years 2024 and 2027, based on real connection requests by producers to Terna, and allows one to go beyond the studies conducted so far on a 2030 basis through the precise identification of network nodes or lines in difficulty. Finally, as well as presenting an interesting case study due to Sicily's strategic position in the Mediterranean Sea, this article proposes a methodological approach that can easily be adopted in other contexts and by other TSOs to analyze similar situations. [ABSTRACT FROM AUTHOR]

For wide-body all-electric aircraft (AEA), a high-power-delivery, low-system-mass electric power system (EPS) necessitates advanced cable technologies. Increasing voltage levels enhances power density yet poses challenges in aircraft cable design, including managing arc-related risks, partial discharges (PDs), and thermal management. Developing multilayer multifunctional electrical insulation (MMEI) systems for aircraft applications is a feasible option to tackle these challenges and reduce the size and mass of cable systems. This approach involves selecting layers of different materials to address specific challenges. Our prior research concentrated on the modeling and simulation-based design of MMEI systems for MVDC power cables. Experimental tests are essential for determining the behavior of PDs under varying pressure conditions. Also, the dielectric strength and time to failure of the designs need to be assessed. In this work, the fabrication process of a down-selected MMEI flat configuration is discussed and analyzed. This paper analyzes the fabrication process of power cables employing MMEI configurations and evaluates the PD characteristics of down-selected ARC-SC-T-MMEI cable samples. This study presents a detailed analysis of the characteristics of PD under atmospheric and low-pressure conditions, which will provide essential insights into the design of MVDC cables for future AEA applications. [ABSTRACT FROM AUTHOR]

Air-source heat pumps (ASHPs), functioning as thermally controlled loads, possess significant adjustable capabilities and controllability when aggregated, establishing them as premium resources for demand-response engagement. This paper proposes a control strategy for the aggregation of ASHP loads to participate in demand response across multiple scenarios, framed within a three-tier architecture: electric power system, Load Aggregator (LA), and thermal load. Load Aggregators, considering the user-comfort temperature ranges and the thermal storage characteristics of buildings, aim to minimize heating costs through time-of-use electricity pricing, while assessing the adjustability of the load. Upon receiving control directives from the power system's dispatch department, the strategy allocates load adjustments by considering user comfort and system regulatory needs, thereby addressing issues like aggregated power oscillations and significant rebound loads. The effectiveness of the proposed strategy is corroborated through simulation, demonstrating its potential to enhance demand-response participation and ameliorate associated power stability challenges. [ABSTRACT FROM AUTHOR]

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems. [ABSTRACT FROM AUTHOR]

Addressing the issue of reduced system inertia and significantly increased risk of system frequency deviation due to high penetration of renewable energy sources, this paper proposes a power system disturbance frequency trajectory prediction method based on light gradient boosting machine (LightGBM) Spearman to provide data support for advanced system stability judgment and the initiation of stability control measures. Firstly, the optimal cluster is determined by combining the K-means clustering algorithm with the elbow method to eliminate redundant electrical quantities. Subsequently, the Spearman coefficient is used to analyze feature correlation and filter out electrical quantities that are strongly correlated with frequency stability. Finally, a frequency trajectory prediction model is built based on LightGBM to achieve accurate prediction of disturbed frequency trajectories. The method is validated using a case study on the New England 10-machine 39-bus system constructed on the CloudPSS 4.0 full electromagnetic cloud simulation platform, and the results show that the proposed method has high accuracy in frequency trajectory prediction. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a challenging optimization problem with a large number of variables and constraints. To overcome the OPF issue, high-performance optimization algorithms are needed. In this direction, this paper has been centered on the optimization of the OPF with the circulatory system-based optimization (CSBO) algorithm. The performance of the algorithm was evaluated on the IEEE 57- and 118-bus power networks for the optimization of non-convex OPF objectives, i.e., fuel cost, power loss, voltage deviation, and enhancement of voltage stability. The solution quality of CSBO is compared with state-of-theart metaheuristic algorithms such as Artificial Rabbits Optimization (ARO), African Vultures Optimization Algorithm (AVOA), and Chaos Game Optimization (CGO). Based on the OPF results, it is seen that the best fuel cost and voltage deviation results are calculated to be 41666.2344 $/h and 0.5871 p.u with the CSBO algorithm for the IEEE 57-bus power system. The CSBO algorithm obtained the best objective function results for the IEEE 118-bus power network with a fuel cost of 134934.3140 $/h and a power loss of 16.4688 MW. In conclusion, the present paper reports that the CSBO is a powerful and efficient metaheuristic algorithm to solve the OPF problem. [ABSTRACT FROM AUTHOR]

The optimal operation of power systems is one of the most critical issues for operators. Optimizing the operation of power systems while accommodating the increasing penetration of RES, owing to the intermittent and variable nature of these resources, stands as one of the most critical challenges for power systems. Numerous primary challenges, including fluctuations in load and nonlinear constraints, further complicate the issue, given its substantial dimensions. Therefore, this paper introduces an approach for improving the management of optimal generation and the associated carbon emissions costs of traditional power plants, which is achieved through integrating wind farms and incorporating battery energy storage. This enhancement is achieved by integrating wind farms and utilizing battery storage systems while considering the costs associated with traditional units using fossil fuels and the expenses related to carbon emissions. The simulation incorporates seven load customer types and five traditional generation sources integrated with wind farms and battery storage devices. The simulation results demonstrate that the new problem formulation has significantly improved the ability and performance of optimizing the scheduling of power plant generations. [ABSTRACT FROM AUTHOR]

The electric power system is rapidly transforming to address the urgent need for decarbonization and combat climate change. Integration of renewable energy sources into the power grid is accelerating, creating new challenges such as intermittency and uncertainty. To address these challenges, this paper proposes a new design of automatic generation control (AGC) ancillary service cost allocation based on the causer-pays rule. The proposed design treats reserves as inventory and aims to minimize them by allocating costs among consumers based on the causative factors for AGC operation. Two cost-allocation methods based on the causer-pays principle are introduced. The first method distributes costs according to the changes in loads causing ancillary service operation, while the second method considers opportunity costs. The case study on the IEEE 39 Bus System demonstrates that the proposed methods incentivize consumers to minimize volatility, resulting in reduced reserve requirements for system operation. In particular, the opportunity cost-based approach encourages loads and variable renewable energy (VRE) to actively reduce volatility, resulting in more efficient power system operation. In conclusion, the novel AGC ancillary service cost allocation methods offer a promising strategy for minimizing spinning reserves, increasing the power system's efficiency, and incentivizing consumers to actively participate in frequency regulation for a more sustainable and reliable electricity market. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic power prediction is of great significance to the stable operation of the electric power system with renewable energy as the main body. In view of the different influence mechanisms of meteorological factors on photovoltaic power generation in different irradiation intervals and that the data-driven algorithm has the problem of regression to the mean, in this article, a prediction method based on irradiation interval distribution and Transformer-long short-term memory (IID-Transformer-LSTM) is proposed. Firstly, the irradiation interval distribution is calculated based on the boxplot. Secondly, the distributed data of each irradiation interval is input into the Transformer-LSTM model for training. The self-attention mechanism of the Transformer is applied in the coding layer to focus more important information, and LSTM is applied in the decoding layer to further capture the potential change relationship of photovoltaic power generation data. Finally, sunny data, cloudy data, and rainy data are selected as test sets for case analysis. Through experimental verification, the method proposed in this article has a certain improvement in prediction accuracy compared with the traditional methods under different weather conditions. In the case of local extrema and large local fluctuations, the prediction accuracy is clearly improved. [ABSTRACT FROM AUTHOR]

The Economic Load Dispatch (ELD) problem is an essential aspect of power system planning and operational scheduling. Different techniques and algorithms have been recommended to solve it, aiming to minimize the cost of power generation with satisfying the load requirements. In this paper, a new algorithm called Electric Fish Optimization (EFO) is used to solve the ELD problem by considering the line losses, ramp rate limits, maximum and minimum capacities of the generators and prohibited operating zones (POZ). The algorithm has been utilized in test systems consisting of 6 and 15 units and its outcomes have been compared to those from previous research studies. The proposed algorithm has been shown to achieve minimum cost, indicating its superiority and effectiveness in addressing power system planning challenges. It is evident that the presented algorithm offers a valuable solution for optimizing ELD problems. [ABSTRACT FROM AUTHOR]

Electromagnetic transients program (EMTP) is widely used to analyze transients in power systems. With the increased interest in integrated energy systems (IESs), it would be beneficial to extend the application of EMTP to multi-physics transients in integrated electrical and heating networks. In this paper, an accurate and efficient lumped-element circuit model of the heating district pipe is developed in EMTP. The pipe is split into segments using spatial discretization. The application of the numerical discretization to the energy conservation equation gives the discretized pipe equation which is expressed in the form of a companion model of EMTP. In order to reduce the computational effort, the time-varying terms in the admittance matrix of the companion model of pipe are eliminated. The modification of the admittance matrix is avoided. Furthermore, the internal nodes resulting from spatial discretization are eliminated, resulting in a lumped-element circuit model with only two external nodes. Case studies are carried out to validate the proposed model in terms of accuracy and efficiency. The 2-norm error of the proposed model is 0.0153%. With a pipe length of 100 m, the computational speed of the proposed model is increased by a factor of 2.94 compared with the existing distributed-element circuit model. The implementation of the proposed pipe model in the EMTP-type simulator enables the analysis of multi-physics transients in a multi-carrier energy system. [ABSTRACT FROM AUTHOR]

During the electric power system (EPS) modelling with massive use of distributed energy resources (DERs) - distributed generation (DG), storage and other distributed technologies such as electric vehicles - simplified and ideal conditions are assumed for the active distribution network. From the grid side, these elements are modelled as absorption and injection of power and/or current. In this paper, using the model MV-Benchmarck System CIGRE Task Force C6.04, a comparative analytical straightforward algorithm of convergence limits on load flow based on sum of powers and sum of currents along the topological matrix has been simulated. The convergence sensitivity analysis was examined for 3 system characteristics: radial and meshed Configuration, DG penetration and R/X ratio, finding percentage differences of up to 6% convergence sensitivity by power hosting capacity between two-non-linearmethods used for load flow. [ABSTRACT FROM AUTHOR]

A modern power system demands an open communication channel to support the vast number of real-time data exchanges, which may introduce time delays and communication failures thus creates new challenges in power systems. To cope up with these issues, the paper proposed an Internal Model-Based Robust Controller (IMBRC) and IMBRC-PID controller designs for the decentralized LFC (Load Frequency Control) of the modern power system. Initially, a finite-ordered linear model of the power system integrated with RES (Renewable Energy Sources) and aggregated Electrical Vehicles (EV) has been developed. Later the full-order model was employed in the proposed design to achieve complete decentralized, robust, more reliable, and effortless control performances. The Internal Model Compensator (IMC) filter time constant is tuned using Artificial Bee Colony (ABC) optimization algorithm. The objective function considered was the scalarized integral of squared and absolute errors with various weighting factors. The Least-Square Model (LSM) approximation of the IMBRC transfer function determines the PID controller gains. The controller's robustness is verified for the power system components affected by structured and unstructured uncertainties. The error performance indices and simulation results convey that the suggested design keeps the system robustly stable even when subject to varying time delays and uncertainties. [ABSTRACT FROM AUTHOR]

• Challenges that mass amounts of electric vehicles (EVs) impose to power system operations. • Opportunities of intelligently managing the flexibility of smart charging of EVs are discussed. • Hybrid DERMS concept is proposed as a key tool for safe integration and proper management of emerging distribution grids with high amounts of EVs. • A real-life case study of integrating two large-scale EV charging stations into an existing feeder is presented. Global initiatives are actively progressing to integrate large numbers of electric vehicles (EVs) as part of efforts to electrify and decarbonize the transportation sector. This approach plays a crucial role in addressing global greenhouse emissions and pollution problems. However, mass integration of EVs imposes unprecedented challenges to electrical power systems, especially to their distribution grids. These challenges range from planning issues, where the traditional panning procedures are not adequate for integration of EVs and their charging stations, to operational challenges, where a huge number of dispersed EVs can cause peak load increase, voltage violations, congestions, feeder overloads, etc. Hence, despite the significant importance of maximizing the deployment of EVs, their stochastic integration can give rise to critical challenges in electric power systems, thereby impeding their subsequent expansion. Therefore, in order to properly integrate mass amounts of EVs, without causing technical violations in the electrical power system, intelligent software tools are required, to aid distribution system operators and engineers in the transition to a new era of electrified transportation. In this paper, we argue that novel software solutions called Distributed Energy Resource Management Systems (DERMSs) are a key solution for enabling a safe integration of mass amounts of EVs into emerging distribution grids. Specifically, we introduce a novel Hybrid DERMS framework, as an integration of multiple hierarchical levels of DER management solutions, and propose this solution as a suitable instrument for distribution system operators and grid engineers to safely integrate and manage high volumes of EVs within the grid infrastructure. The hypothesis is validated through a real-life use case for integration and management of high amounts of EVs into an existing distribution grid, using an industrial DERMS solution. Specifically, an in-depth analysis of adding two large charging stations, boasting capacities of 5 MW each, has been performed through all stages of integration, from the planning stage, up to using their flexible output to manage technical constraint violations in real time. The influence of these EV charging stations on both the feeder where they are planned to be integrated, as well as on the entire supply substation, have been analyzed in detail. The resultant findings, especially on successfully managing overloads, peak load increases, as well as voltage violations through intelligent use of aggregated EVs through DERMS, have proven to be notably encouraging, further substantiating the paper's hypothesis. [ABSTRACT FROM AUTHOR]

The paper introduces a robust methodology for evaluating complex electricity system efficiency, making several contributions. It systematically decomposes the electric power system into various processes, including power generation, transmission, distribution, and power consumption, with parallel sub-processes for production and domestic power consumption. Employing data envelopment analysis (DEA), the paper develops a network directional slacks-based (DSB) model to evaluate the whole system and the internal processes. The model is theoretically proven to be feasible, providing efficiency scores and identifying inefficiencies in the system and its processes. A noteworthy addition is the introduction of the DSB green Luenberger productivity index and its decomposition, offering an analysis of productivity changes in electricity systems. Empirical analysis covering 30 Chinese provinces from 2013 to 2019 uncovers the performance variations among provinces and processes, pointing out inefficiencies and changes in technology and efficiency over time. The empirical findings aim to support efforts in enhancing the overall efficiency of China's interprovincial power system. • We decompose the complex electric power system into four processes with dependence. • We build a network directional slacks-based (DSB) model for the complex power system. • We give a DSB green Luenberger productivity index (DPI) for our network model. • The efficiency score and DPI of the system are decomposed into those of processes. • We apply the model and indexes to electric power systems in Chinese provinces. [ABSTRACT FROM AUTHOR]

Nowadays, the high penetration of inverter-interfaced distributed energy resources (DERs) has raised a well-founded concern regarding the interaction of these devices with the upstream grid. The harmonic interaction phenomenon between these players can be described by time-domain simulations, using high computational effort switched and/or average models often impractical for large power systems. This paper proposes an innovative low-computational-burden model named passive impedance network (PIN), capable of accurately representing DERs in electric power systems (EPSs). Through passive RLC components, the PIN model can describe DER dynamic behavior at a certain frequency of interest, making it able to represent harmonic flow within the EPS nodes. The proposed model is compared with the switched model, average model, and an experimental setup under different grid distortions and injected current profiles. A comparison of the total demand distortion (TDD) between the proposed PIN model and the experimental results is carried out. The effect of DER parameter deviations on PIN model accuracy is addressed, in which the absolute error of the PIN model TDD values are 0.65% and 0.06% for the experimental and switched model, respectively. Besides, the PIN model presents an improved computational performance compared to the time-domain-based switching model, requiring 99% less computational burden. • Modeling grid interaction of distributed energy resources. • Low computational burden model based on the Norton model. • Introducing inverter representation for harmonic interactions. • Mathematical representation of a single-phase inverter in a steady state. • Grid connected inverter model with large simulation step size. [ABSTRACT FROM AUTHOR]

In the realm of complex and evolving Multi-Agent Systems (MAS), ensuring system reliability and safety is paramount. These systems, characterized by their partial observability and intricate inter-agent interactions, pose significant challenges for fault management. This paper introduces a novel framework that leverages model checking and timed automata to address these challenges, providing a rigorous solution for fault management in MAS. We formalize the problems of fault identification, diagnosability, and tolerance within the context of partially observable MAS, laying a solid foundation for systematic analysis and interpretation. The framework utilizes timed automata to model the dynamic and non-deterministic nature of MAS, while model checking is employed to ensure comprehensive and rigorous fault management results. Through a case study on an Electric Power System, we demonstrate the framework's efficacy in providing both offline design insights and online decision support, enhancing the system's resilience to faults. The results underscore the framework's ability to navigate the complexities of partially observable MAS, offering clear and actionable guidance for fault mitigation. • Introduces a model-checking based framework for fault management in Multi-Agent Systems (MAS). • Proposes a decision support system for both offline analysis and online assistance in MAS. • Demonstrates fault identification, diagnosability, and tolerance using an Electric Power System example. • Enhances fault identifiability and provides practical guidelines for system resilience. [ABSTRACT FROM AUTHOR]

The accurate and efficient measurement of flicker severity levels is crucial for evaluating and compensating power system flicker in electric power systems. This paper proposes a simple and fast measurement algorithm for power system flicker severity evaluation. The algorithm utilizes an equivalent measurement model and employs the Hilbert transform for effective envelope extraction. Subsequently, the extracted envelope is processed using windowed interpolation fast Fourier transform to accurately determine the flicker parameters. Finally, flicker assessment indicators are obtained. By simplifying the measurement procedure recommended by IEC Standard 61000-4-15, the proposed algorithm reduces the calculation process of flicker indicators, making it suitable for real-time flicker analysis in embedded applications. The accuracy and effectiveness of the algorithm are validated through simulation and practical test results. [ABSTRACT FROM AUTHOR]

Dielectric capacitors are highly desired for electronic systems owing to their high-power density and ultrafast charge/discharge capability. However, the current dielectric capacitors suffer severely from the thermal instabilities, with sharp deterioration of energy storage performance at elevated temperatures. Here, guided by phase-field simulations, we conceived and fabricated the self-assembled metadielectric nanostructure with HfO2 as second-phase in BaHf0.17Ti0.83O3 relaxor ferroelectric matrix. The metadielectric structure can not only effectively increase breakdown strength, but also broaden the working temperature to 400 oC due to the enhanced relaxation behavior and substantially reduced conduction loss. The energy storage density of the metadielectric film capacitors can achieve to 85 joules per cubic centimeter with energy efficiency exceeding 81% in the temperature range from 25 °C to 400 °C. This work shows the fabrication of capacitors with potential applications in high-temperature electric power systems and provides a strategy for designing advanced electrostatic capacitors through a metadielectric strategy. Dielectric capacitors known for high-power density and fast charging/discharging suffer from thermal stability and failure at high temperatures. Here, a metadielectric strategy is used to fabricate thermally stable high temperature film capacitors. [ABSTRACT FROM AUTHOR]

• The robust coordinated planning model of integrated natural gas and electric power systems are established. • The robust optimization based planning method considering uncertainties is proposed. • The new power loads and gas loads are modeled in the planning method. • Both of the optimal locations and sizes of sources can be obtained. The optimal operation of integrated energy systems is affected by the network structure and source configuration in planning process. With the rapid developments of renewable energies, the optimal size and location of sources under uncertainty conditions should be considered in the planning. A robust optimization based coordinated network and source planning method of integrated energy systems is proposed in this paper. The coordinated network and sources planning model of integrated energy systems determining both the locations and sizes of the sources simultaneously is established. For the newly increased power loads, the constraint of gas and power supply necessities for new loads is added in the model. Additionally, to deal with the uncertainties of renewable energies, the robust optimization algorithm is applied to solve the planning problem, in which the random distribution parameters are unnecessary. The planning method is applied on an integrated energy system composed by a 20-node natural gas pipeline network and IEEE 33-bus testing system under 4 uncertainty scenarios. The detailed optimal sizes of sources at every bus can be obtained, and the total costs of the proposed robust planning method are at least 20.73% lower than the deterministic method under the 4 scenarios and different robust parameters. The simulating results show that for the most conservative condition, the proposed robust optimization based planning method can decrease 0.87% of the total costs under uncertainty conditions compared with the deterministic method. [ABSTRACT FROM AUTHOR]

• A method for new power electronic equipment is proposed. • Novel software for the developed hardware is proposed. • The device decentralises the aircraft's power system. • This solution increases overall efficiency and safety. The More Electric Aircraft concept has arisen in an attempt to achieve the goals stated by different international organisations in terms of the concept of future aircraft. While some of these requirements are related to reducing the negative effects of airfreight such as CO2 emissions or fuel consumption, other requirements focus on aircraft safety enhancement. Nevertheless, if the way to achieve the vast majority of these goals is based on increasing electrification level of aircrafts, then their electric power system must be reviewed and reconsidered. Currently, literature proposes device redundancy as the main option for meeting aircraft safety requirements. This paper proposes alternative architecture, which consists of merging a new source code and recently developed topology, which makes it possible to decentralise the aircraft's DC bus, in order to increase its safety and meet the stated goals. While the new topology has been validated through small-scale prototyping, proposed software strategy has been validated in a virtual environment using simulations. The architecture developed enables a path to be selected in the electric power system: examining different options in the system, analysing which of those options are the most efficient and, finally, sending signals to configure the power system connections to power the load. Based on the results of those tests, the alternative proposed satisfactorily powers selected loads. [ABSTRACT FROM AUTHOR]

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers' financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid's low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers' distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms. • Data Empowered Appliance-level Utility Modeling. • Demand Response Design to Offer Appliance-level Incentive. • Heterogeneity Characterization to Improve Scalability and Efficiency. [ABSTRACT FROM AUTHOR]

Electric power systems with a high penetration of photovoltaic generation and a relevant fleet of electric vehicles face significant stability challenges, particularly in mountainous areas where the variability of photovoltaic resources is pronounced. This study presents a novel methodology to strategically place electric vehicle aggregators along a feeder. This approach considers electrical variables and the dynamics of cloud movements within the study area. This innovative methodology reduces the substation's power load demand and significantly improves the end user's voltage levels. The improvements in voltage regulation and reduced demand on the substation provide clear benefits, including increased system resilience, better integration of renewable energy sources, and enhanced overall efficiency of the electric grid. These advantages are particularly critical in regions with high levels of photovoltaic generation and are important in promoting sustainable electric vehicle charging infrastructure. When analyzing different load scenarios for the IEEE European Low Voltage Test Feeder system, the consideration of distributed aggregators based on cloud movements decreased the power required at the substation by 21.25%, and the voltage drop in loads was reduced from 6.9% to 4.29%. This research underscores the critical need to consider both the variability and geographical distribution of PV resources in the planning and operation of electrical systems with extensive PV generation. [ABSTRACT FROM AUTHOR]

There is growing consensus that gas-fired generators will play a crucial role during the transition to net-zero energy systems, both as an alternative to coal-fired generators and as a flexibility service provider for power systems. However, malfunctions of gas networks have caused several large-scale power blackouts. The transition from coal and oil to gas fuels significantly increases the interdependence between gas networks and electric power systems, raising the risks of more frequent and widespread power blackouts due to the malfunction of gas networks. In a coupled gas–electricity system, the identification and transmission of gas network malfunction information, followed by the redispatch of electric power generation, occur notably faster than the propagation and escalation of the malfunction itself, e.g., significantly diminished pressure. On this basis, we propose a gas-electric early warning system that can reduce the negative impacts of gas network malfunctions on the power system. A proactive control strategy of the power system is also formulated based on the early warning indicators. The effectiveness of this method is demonstrated via case studies of a real coupled gas–electricity system in China. Gas-fired generators play crucial role in the transition to net-zero energy systems as an alternative to coal-fired. But malfunctions of gas networks can cause large-scale power blackouts. Here, authors apply the early warning concept to the coupled gas-electric system towards enhanced power supply security. [ABSTRACT FROM AUTHOR]

The systems of policies impacting climate change mitigation are complex. Yet, to date, we have limited conceptual and empirical knowledge on the dynamics within these. We address this gap by employing a systems lens to untangle the interactions between the policies affecting climate change mitigation in the electricity generation space. We conceptualise climate policyscapes for electricity generation as systems populated with policies whose means impact decarbonisation in the electricity generation space. The impacts under analysis include both support and obstruction of climate change mitigation. We analyse the evolution of the UK climate policyscape from 1956 to 2022. Methodologically, we combine qualitative content analysis and network analysis. We populate the policyscapes with pieces of legislation in the electricity generation space and employ qualitative content analysis to identify the policy means affecting climate change mitigation. Our network analysis of the 2022 climate policyscape reveals that policies hindering climate mitigation remain largely present, which renders the climate policyscape incoherent. We show that policies supporting mitigation are more likely to behave as a group than policies hindering climate mitigation. Climate policies tend to be adopted as packages, whilst fossil policies remain a steady process throughout the history of the UK climate policyscape. [ABSTRACT FROM AUTHOR]

State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network. [ABSTRACT FROM AUTHOR]

The intensive development of renewable energy sources and the decreasing efficiency of conventional energy sources are reducing the flexibility of the electric power system. It becomes necessary to develop energy storage systems that allow reducing the differences between generation and energy demand. This article presents a multivariant analysis of an adiabatic compressed air energy storage system. The system uses a post-mining shaft as a reservoir of compressed air and also as a location for the development of a heat storage tank. Consideration was given to the length of the discharge stage, which directly affects the capital expenditure and operating schedule of the system. The basis for the analyses was the in-house numerical model, which takes into account the variability of air parameters during system operation. The numerical model also includes calculations of Thermal Energy Storage's transient performance. The energy efficiency of the system operating on a daily cycle varies from 67.9% to 70.3%. Various mechanisms for economic support of energy storage systems were analyzed. The levelized cost of storage varies, depending on the variant, from 75.86 EUR/MWh for the most favorable case to 223.24 EUR/MWh for the least favorable case. [ABSTRACT FROM AUTHOR]

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers. [ABSTRACT FROM AUTHOR]

As the landscape of electric power systems is transforming towards decentralization, small-scale electric power systems have garnered increased attention. Meanwhile, the proliferation of artificial intelligence (AI) technologies has provided new opportunities for power system management. Thus, this review paper examines AI technology applications and their range of uses in small-scale electrical power systems. First, a brief overview of the evolution of small-scale electric power systems and the importance of AI integration is given. The background section explains the principles of small-scale electric power systems, including stand-alone systems, grid-interactive systems, microgrids, hybrid systems, and virtual power plants. A thorough analysis is conducted on the effects of AI technologies on power system aspects such as energy consumption, demand response, grid management, operation, energy generation, and storage. Based on this foundation, AI Acceleration Performance Indicators (AAPIs) for small-scale electric power systems are developed to establish a standardized framework for evaluating and comparing different studies. AAPI framework considers a binary scoring for five quantitative Key Performance Indicators (KPIs) and five qualitative KPIs examined through a three-tiered scale – established, evolved, and emerging.

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations. • Detailed power and water operations are included in a hydrothermal scheduling model. • It incorporates short-term storage, reserve requirements, and irrigation effects. • The uncertain hydro inflows are represented by vector autoregressive models. • A calibration method captures the main effects induced by operational constraints. • Extensive experiments are analyzed on a detailed Chilean hydroelectric representation. [ABSTRACT FROM AUTHOR]

The growing diversity in energy demand has led to an increasingly intertwined relationship between the electric power system (EPS) and hydrogen energy system (HES). However, these systems are presently managed by entities with distinct interests, resulting in competition and privacy concerns during the scheduling of integrated electricity and hydrogen systems (IEHSs). To address this issue, this paper proposes a multi-objective IEHS scheduling model with the aim of minimizing costs for electricity and hydrogen suppliers, taking into account bi-directional energy transactions between EPS and HES. Furthermore, we present an innovative approach based on alternating direction multiplier method (ADMM) for distributed ξ-constraint optimization. This approach efficiently captures the optimal Pareto frontier while maintaining the privacy of EPS and HES. The proposed methodology is validated on an IEHS composed of an IEEE 33-bus EPS and a 20-node HES. The results demonstrate that no matter how many solution sets, a more uniform pareto front can be obtained than the traditional method. Furthermore, it simplifies the multi-objective optimization problem and effectively protects the privacy of participants at the expense of acceptable solution time. • Multi-objective optimal scheduling model of IEHSs is proposed. • Distributed ξ-constraint algorithm is designed to obtain the optimal Pareto frontier. • The privacy protection of different subjects is effectively protected. [ABSTRACT FROM AUTHOR]

A transition to renewable energy is increasing the long-distance export of power, with reduced spinning inertia and small stability margins. In this work, we apply higher-order variants of a data-driven technique, the dynamic mode decomposition (DMD), for stability analysis and short-term prediction of power systems with renewable sources of energy. In the present paper we study the sampling duration for extraction of physically relevant dynamics suitable for prediction using noisy historical measurements of power system disturbance events. Trends in behaviour of the dominant mode are studied to estimate the singular value threshold and the delay embedding for prediction. In a sampling window around ten times the dominant periodic interval, the higher-order Kalman filtering DMD and extended Kalman filtering DMD (filtering for system identification) are newly applied to predict an historical resonance and a possible near-resonance events of single dominant frequency. In turn, multiple frequencies of a wide-area oscillatory disturbance with low-level non-Gaussian noise are best captured and predicted using the total-least-squares higher-order DMD. • We study data-driven models for prediction of power systems. • We allow the input data to be noisy. • We apply and compare different noise reduction techniques. • We study the effect of sampling window on the accuracy of prediction. • We consider the effect of non-Gaussian noise. [ABSTRACT FROM AUTHOR]

Smart, Micro and Nano-grids are the future of electric power systems. In parallel with this future, electric vehicles (EVs) take a great deal of attention, as they are one of the important elements in these systems. Also, with the passage of time, the development continues in the technology of manufacturing EVs. Since the electric motor is the main component in these vehicles, research and development is continuing to find the most suitable motors that can work in such vehicles. The synchronous reluctance motor (SynRM) is the newest motor used in this industry. SynRMs faces different challenges, one of which is the torque ripples. Different trials were used to reduce or eliminate or minimize these ripples. Most of previous work focuses on the SynRM design parameters to reduce torque ripples. In this paper, the control methodology and the strong action controller (SAC) are used as a tool to reduce these ripples. The simulation results showed the effectiveness of the application of the proposed SAC in ripples factor reduction. This reduction represents a new contribution over the already existed techniques. [ABSTRACT FROM AUTHOR]

• A T-VESS is considered in real time P2P energy trading. • A two-stage P2P energy trading method is proposed based on MPC. • Trading actions are implemented considering multiple trading preference grades. • The proposed method is effective and utilities for prosumers are much improved. With the increasing penetration of distributed energy resources (DER) in the electric power system, Peer-to-Peer (P2P) energy trading has become a promising paradigm for future electric power systems. Building thermal load, which is an important demand side resource, should be considered carefully in the design of a P2P trading method. In this paper, we investigate the application of building thermal energy storage capability in P2P energy trading. We aggregate and model building thermal loads as a virtual energy storage and derive a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. Key parameters of T-VESS, including charging/discharging rate, energy capacity, and state of charge (SOC), are analyzed so that T-VESS can be embedded in the building prosumer model to participate effectively in electricity-oriented P2P energy trading. We propose a real time P2P energy trading method of prosumers based on model predictive control (MPC). This method consists of an energy supply and demand quantification stage and a transaction price optimization stage, which can effectively capture the time-varying nature of T-VESS. To preserve the trading preference for prosumers, we propose a distributed P2P energy trading actions implementation method based on the continuous double auction (CDA) considering multiple trading preference grades. Numerical results demonstrate that the proposed P2P energy trading method considering T-VESS can reduce the operational cost of prosumers by 3.7%, while also promoting the local integration of renewable energy by 3.1%. Furthermore, compared to existing P2P trading methods considering single preference grade, the proposed method greatly enhances the utilities of both individual prosumers and the overall community. [ABSTRACT FROM AUTHOR]

One of the tasks in the operation of electric power systems is the correct functioning of the protection system and emergency automation algorithms. Instrument voltage and current transformers, operating in accordance with the laws of electromagnetism, are most often used for information support of the protection system and emergency automation algorithms. Magnetic core saturation of the specified current transformers can occur during faults. As a result, the correct functioning of the protection system and emergency automation algorithms is compromised. The consequences of current transformers saturation are mostly reflected in the main protections of network elements operating on a differential principle. This work aims to consider the analysis of current transformer saturation detection methods. The problem of identifying current transformer saturation is reduced to binary classification, and methods for solving the problem based on artificial neural networks, support vector machine, and decision tree algorithms are proposed. Computational experiments were performed, and their results were analyzed with imbalanced (dominance of the number of current transformer saturation modes over the number of modes with its normal operation) and balanced classes 0 (no current transformer saturation) and 1 (current transformer saturation). [ABSTRACT FROM AUTHOR]

This study introduces the Bi-Level Source-Grid-Load Synergistic Optimization (BL_SGLSO) model, which effectively balances the competing objectives of water conservation and carbon emission reduction in the power industry. The model aims to establish a clean and low-carbon electric power system by harmoniously reconciling these two critical goals. Through the application of bi-level programming, the BL_SGLSO model adeptly manages the preferences and conflicts of decision makers at various levels while capturing regional interactions and the intricacies of electricity transmission. Key findings reveal that non-fossil energy conversion technologies are poised to become the dominant force in electricity generation, accounting for an impressive 89.34% share by 2050. To mitigate the spatial mismatch between power load and resource allocation, the development of new transmission pathways and the expansion of the "power transmission from west to east" initiative are paramount. Furthermore, the implementation of a carbon-reducing power system offers significant potential for conserving water resources and alleviating water stress. These insights provide invaluable guidance for decision makers seeking to optimize multi-regional electric power systems for both water efficiency and low-carbon outcomes while simultaneously promoting the adoption of renewable energy sources and fostering synergistic development across regions. [ABSTRACT FROM AUTHOR]

During geomagnetic storms, which are a result of solar wind's interaction with the Earth's magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long, high-voltage electrical networks on the Earth's surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its effects on transformers, a GIC monitoring system was created in 2011. The system consists of devices that are installed in the neutrals of autotransformers at five substations of the Kola–Karelian power transit in northwestern Russia. Considering the significant amount of data accumulated over 12 years of operating the GIC monitoring system, manual analysis becomes impractical. To analyze the constantly growing volume of recorded data effectively, a method for the automatic classification of GICs in autotransformer neutrals was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. The classifier's performance is evaluated using accuracy and binary cross-entropy loss metrics. As the result of comparing four CNN architectures, a model that showed high GIC classification performance on the validation set was chosen as the final model. The proposed CNN model, in addition to the main layers, includes pre-processing layers and a dropout layer. [ABSTRACT FROM AUTHOR]

The problems associated with the greenhouse effect have increased the desire to limit carbon dioxide (CO2) emissions into the atmosphere, including emissions produced by shipboard electrical power systems. This has led to a further search for solutions in this area, such as shipboard hybrid electric power systems (SHEPSs). These systems do not yet have a wide application compared with traditional shipboard electrical power systems for several reasons, including the lack of studies establishing the fault tolerance of such systems. Therefore, in this work, problems related to generated power quality deterioration under large disturbances are studied. To achieve the fault-free operation of SHEPS, an emergency prevention control (EPC) system based on controlled parameter forecasting, along with a system structure and operation algorithm, is developed. The goals of improving electrical power quality are achieved by increasing the control efficiency of the power system's generating sources. To validate the feasibility of the proposed control system, a computer simulation was carried out after developing a mathematical model of the SHEPS under study. The results of the study show that the use of the proposed EPC system will improve power quality when the controlled parameters are within acceptable limits. At the same time, further research is needed, as the problem of false control action as a consequence of EPC system hardware or software faults remains unstudied. [ABSTRACT FROM AUTHOR]

The article, titled "Taking the Longer Road," reflects on the collaboration between the author, Eric Monteiro, and his colleague Ole in the field of information infrastructure (II). The article discusses their alternative conceptualization of IT as a socially informed and interconnected system, contrasting it with the prevailing view of IT as an artifact. The author highlights Ole's approach to research, which involves seeking inspiration from competing perspectives and exploring analogous thinking. The article also emphasizes the importance of empirical research and criticizes the market-oriented approach to academia. [Extracted from the article]