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This paper presents a detailed model of low-frequency oscillations and their damping within the Electric Power System (EPS) of Bosnia and Herzegovina (B&H). The system is modeled using MATLAB software, analysing the steady state and dynamic responses. This research highlights the challenges and impacts of integrating renewable energy sources, such as wind farms, on grid stability and oscillation damping. The paper utilizes eigenvalue analysis to investigate the dynamic characteristics of the system, emphasizing the need for efficient damping strategies to maintain system stability. The methodology includes a comprehensive review of existing literature, the creation of a detailed EPS model of B&H, and the application of eigenvalue and oscillation amplitude analysis to determine damping ratios. The dynamic responses of hydro power plants, HPP Mostar and HPP Jablanica, to transient disturbances are analysed to validate the model and refine damping strategies. The results indicate that the B&H EPS is well-damped, with all eigenvalues possessing negative real parts, and demonstrate the system's resilience to small disturbances. The results are compared with the technical report on the integration of the wind power plant WPP Podveležje. This comparative analysis shows consistent patterns between the modeled calculations and empirical data, confirming the robustness of the EPS model. This alignment underscores the effectiveness of current damping mechanisms and provides a foundational strategy for enhancing system stability with increasing renewable energy penetration. The findings highlight the importance of developing advanced control strategies to sustain system stability as the integration of variable renewable energy sources continues to grow. [ABSTRACT FROM AUTHOR]

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]

Harmonic state estimation (HSE) is an useful technique for determining harmonic voltages and currents in electric power systems based on synchronized measurements and network impedances parameters. However, measurements and parameters data can be corrupted by gross errors yielding unsatisfactory estimation results. Despite recent methods have been proposed for HSE in power systems, there are few works considering both harmonic state variables and parameters estimation. In order to solve this problem, this paper presents a generalized harmonic state estimation considering uncertain network parameters and synchronized measurements. In the proposed approach, harmonic voltage and current phasors are obtained by measurement units allocated along the system. Network parameters, such as line and load impedances, are treated as inequality constraints in an optimization problem formulation considering both system state variables and parameters to be determined by the proposed HSE. The main contribution of this research paper is the use of a deterministic approach for estimating both harmonic state variables and parameters with reduced estimation errors when compared with the traditional weighted least squares (WLS) technique. Computational simulations are carried out using the IEEE test systems and results are also validated using RSCAD (Real-Time Simulation Software Package) software. [ABSTRACT FROM AUTHOR]

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

With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint. Second, a convolutional neural network (CNN) combined with an attentionmechanism is proposed for training performance improvement. At last, an experiment is carried out to evaluate the estimation performance. Compared with the support vector machine method, CNN method and long short-term memory method, the proposed algorithm has the best load estimation performance. [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]

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]

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]

Summary: In the era of big data, massive amounts of data hold great value. However, much data exists as isolated islands, and the maximum value of the data cannot be fully utilized. Federated learning allows each client to train local data and then share the training model parameters securely, which can address the isolated data island problem and exploit data value while ensuring data privacy and security. Accordingly, in order to securely complete the electric power load forecasting using existing data, this paper constructs a federated learning‐based privacy‐preserving scheme to support electricity load forecasting in edge computing scenarios. To address the problems of the data‐isolated islands and data privacy in electric power systems, this paper proposes a decentralized distributed solution based on the federated learning technique. Our scheme achieves electricity load forecasting for power systems through the federated learning‐based framework and uses edge computing architecture to improve real‐time data capability and reduce network latency. For the hierarchical scheduling structure in power systems, we divide the system into a cloud‐side‐device three‐layer architecture, which achieves structural coordination and balance, and each layer collects information according to the scheduling control tasks, promoting scheduling effectiveness. Finally, different privacy protection methods are used on the cloud‐edge and edge‐device sides to significantly enhance data security. Moreover, We have conducted extensive experimental simulations for our proposed scheme. The experimental results show that the relative error of electricity load forecasting is around 1.580%. Meanwhile, our scheme achieves high accuracy and low memory usage. The security analysis proves the feasibility and security of our scheme. [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]

The transmission line is one of the most crucial electric power system components, demanding special attention since they are subject to failures that can cause disruptions in energy supply. In this scenario, the fault location emerges as a fundamental task, providing an approximate position where the failure occurred in the line. This paper presents a method for fault location using a novel dynamic regressor selection (DRS) framework, in which we introduce embedded normalization, incorporating the data scaling process inside the framework. The DRS aims to select the most accurate models from a pool of regressors to predict the fault distance. Moreover, since there is a lack of a public dataset, this paper presents and makes a new fault dataset available to the scientific community with several features extracted from current and voltage signals of representative failure events. In our experiments, we demonstrate the importance of this embedded normalization as well as the significance in the variation of critical hyperparameters of the DRS strategy, such as the distance metric used to define the region of competence and the criterion to select the best regressors from the pool of predictors. This work also presents the definition of the oracle concept in DRS, which represents the ideal predictor selection scheme. The results demonstrate the effectiveness of the proposed method with a mean error of 0.7086 km ± 0.0068 km, representing 0.1712% ± 0.0016% of the transmission line extension (414 km). [ABSTRACT FROM AUTHOR]

This paper presents a control scheme for virtual synchronous generators (VSGs) in PV inverters, designed to enhance grid frequency and voltage. Through the skillful management of active and reactive power, this control scheme enables PV inverters to interact seamlessly with the main grid in response to grid events, including voltage and frequency fluctuations. The VSG controller is implemented using Matlab Simulink, and its effectiveness is rigorously assessed under various scenarios in a case study involves a 50 kVA rated PV inverter, a 50 kW rated PV system, and a 220 V grid phase voltage. In conditions of low power generation from the PV system (solar radiation of 200 W/m²) and high load power (120 kW and 37.5 kVAr), the load voltage drops to 202.4 V. The VSG controller successfully raises the grid voltage by 17.6 V, stabilizing it at 220 V. Conversely, in scenarios of high PV power generation (solar radiation of 1000 W/m²) and low load power (20 kW and 7.5 kVAr), the grid voltage surges by 10 V, reaching 230 V. The proposed control strategy adeptly fine-tunes the voltage, ultimately stabilizing at 220 V. Additionally, when the frequency deviates within ±0.4 Hz from the nominal frequency of 50 Hz, the proposed control effectively restores the frequency to its nominal value. [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]

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

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]

Uncertain grid impedance is often common in power distribution networks; therefore, it is crucial to design an efficient controller in this situation. An issue that frequently occurs is the problem of unpredictable grid impedance, which can cause voltage fluctuations, power quality problems, and potential damage to equipment. This work provides a systematic control strategy to tackle these issues by supplying well-regulated power from a DC source to an AC power grid. A linear matrix inequality (LMI)- based robust optimal control is proposed in this paper to provide stability to the inverter system without offset error at the output side. The convergence time to steady state is minimized by solving the LMI problem to maximize the eigen value of the closed-loop system with the inclusion of the uncertainty of the filter parameter and grid impedance. Furthermore, the uncertainties in this study include the potential variation of values for the filters and the grid's impedance. These uncertainties occur because the grid impedance can fluctuate fast in the event of a fault or termination of a transmission line, while the filter's impedance can also be affected by changes in operating temperature. The simulation study of this proposed control includes a comparison between wide and narrow uncertainty ranges, as well as a performance comparison under uncertain parameters. Furthermore, this approach exhibits a lower power ripple in comparison to existing PI control method. [ABSTRACT FROM AUTHOR]

Equipping renewable energy resources generation units in the distribution network to reduce economical and emission concerns are the examples of active distribution network(ADN). The other advantages of utilizing distributed generators (DGs) are improving technical constraints of ADN. In this paper multibenefit functions are defined as main functions. Each of functions illustrates the positive impacts of utilizing wind turbines in the improving technical constraints of the ADN. Voltage stability (VS) is one of the main technical indices of the ADN. Several VSIs are defined to evaluate voltage stability of the ADN. The previous indices could not give the proper results about allocating DGs and accurate evaluating of voltage stability of ADN. This work proposes the new VSI. To this aim active power loss (APL), reactive power loss (RPL) and voltage stability index (VSI) are considered as technical constraints. In order to evaluate the presence of WT on improving APL and RPL, WTs are considered in two operational modes; unified power factor (UPF) and (APF). The main benefit function is solved by implementing genetic algorithm (GA). Multiplying weights to the APL, RPL and VSI (which are improved by attendance WTs) in benefit function formulation, make the multi-criteria decision formation to the proposed optimization problem. By employing analytical hierarchy process (AHP) technique and considering each technical constraints as main criteria, the obtained solutions are arranged. To verify the positive effectiveness of the proposed VSI, its results are compared with the results of other VSIs in the 33, 67 and 118 bus IEEE radial DN. [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]

Nowadays, renewable energy sources are integral to programs for the development of electric power systems. It is established that modern renewable generation units and energy storage systems utilize power electronic-based interfaces, such as voltage source converters, to convert power. On the one hand, the utilization of a voltage source converter provides certain advantages, such as the absence of a direct link to the network, the ability to operate on all quadrants of the PQ diagram, and the possibility of connecting renewable generation units to a weak network. On the other hand, the widespread use of renewable generation units utilizing voltage source converters reduces the overall inertia of the electric power system. However, excessive use of power electronic converters in the network leads to a significant increase in harmonic pollution, thereby introducing fresh challenges to ensuring safe and stable operation of the electric power system. To ensure the dependable operation of an electric power system with renewable energy sources units and energy storage systems based on the voltage source converter, it is essential to update the control system of the voltage source converter to meet the requirements for traditional generation as part of the electric power system. This paper presents the outcome of the power oscillation damping controller application implemented in the voltage source converter, which enhances the electric power system's stability. [ABSTRACT FROM AUTHOR]

Electricity consumption is increasing rapidly and many countries are looking for ways to cope with the energy crisis. Morever, the world is facing the problem of global warming caused by emissions. Therefore, it is of great importance to operate power systems efficiently. Energy Hub (EH) represents a versatile energy system capable of providing efficient and optimal solutions for the operation of power systems across multiple carriers. This paper examines the optimization of an EH encompassing renewable energy systems (RES) like wind and photovoltaic, combined heat and power (CHP), transformer, absorption chiller, energy storage system (ESS) and furnace with aiming at minimizing the cost. Demand response is an energy sector strategy that entails modifying electricity consumption patterns in reaction to fluctuations in electricity supply or pricing. The objective of demand response programs (DRP) is to curtail or shift electricity consumption during periods of elevated electricity prices. Therefore, Electrical Demand Response Program (EDRP) for electrical demand and the Thermal Demand Response Program (TDRP) for heating demand are incorporated into the EH. The optimization problem is formulated as Mixed Integer Linear Programming (MILP) and solved with CPLEX solver in GAMS. The outcomes of various case studies are compared to ascertain the model's efficiency. [ABSTRACT FROM AUTHOR]

The joint consideration of hydrogen supply chain networks and electric power systems can promote hydrogen-fueled transportation and the penetration of renewable energy. This paper develops a low-carbon scheduling approach for an Integrated Hydrogen Transport and Energy System (IHTES). To reduce carbon emissions, the environmental cost from the hydrogen supply chain network and electric power system is jointly minimized with the operational cost of IHTES. To consider the timeliness of hydrogen deliveries, hydrogen transport via tube trailers is innovatively modeled as an extended vehicle routing problem with time windows, and delays are penalized. The scheduling problem is formulated as a mixed-integer linear programming problem so that it can be efficiently solved using the branch-and-cut method. Case studies illustrate and validate the proposed model, and demonstrate the computational efficiency of our approach. Carbon emissions are reduced by 47.8% when the emission cost is co-optimized with the operational cost. The operational cost of the hydrogen supply chain network is reduced by 7.3% when the timeliness of hydrogen deliveries is modeled. • Emission-operation optimization of Integrated Hydrogen Transport and Energy System. • The timeliness of hydrogen deliveries is modeled with delays penalized. • The scheduling problem is formulated as a mixed-integer linear programming problem. [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]

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 optimal operation model is developed for the smart distribution network. • Demand response schemes and electric vehicles are intended for flexibility. • Polyhedral and box methods are applied to model uncertainties. • Environmental responsibilities are taken into account by adopting renewables. • Degradation limitation is intended for the optimal operation of battery storage units. By using advanced automation services, distribution systems have been transformed from passive networks to smart and active infrastructures. In this regard, due to the complex interaction of different entities, operators have faced multifaceted challenges in securely exploiting the electric power system. However, optimal planning and operation of smart distribution networks have made the reach of a more stable and flexible electricity network possible. To this end, this study proposes a new operational model of effectively integrating demand response and battery energy storage systems (BESS) to reduce the power exchange with the sub-distribution substation. To optimize the BESS charging and discharging cycles, the degradation limit is considered for its optimal operation in the electric system. Herein, locally controllable generators are taken into account to support reactive power as well as assist with the flattening of the voltage profile. This paper considers emission cost in the performance of local controllable generators (LCG), which adds a critical layer of optimization to distribution network management. This enables the system to balance grid stability, affordability, and environmental responsibility. Moreover, box and polyhedral methods have been investigated and compared for effective and uncertain-aware optimization of the system. Findings show that the polyhedral method provides more robust and confident results than the box method. [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]

• 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]

Climatic phenomena, particularly hydrological droughts, have led to significant changes in reservoir operation strategies. Multipurpose reservoir operations are essential for effectively managing stored water resources for various activities like electricity generation and agricultural irrigation. Despite considerable efforts to support decision making for each economic activity, there remains a weak integration across these sectors in joint analyses. To address this, an integrated approach combining a model of a large power system and a model at the basin scale is proposed to analyze the operation of both power and agricultural systems. This approach allows evaluation of the operating policies of a multipurpose reservoir and its performance at both the local and regional scales under different hydrological scenarios. A modification is implemented whereby the priority of water extraction to agricultural users is increased. Its effects are assessed for different hydrological trajectories in a case study in the Laja Lake basin in southern Chile, the biggest Chilean basin with a capacity of up to 5,500 Hm3. The Laja Lake, a multipurpose reservoir with substantial hydroelectric generation capacity and extensive agricultural areas plays a crucial role in the operation of the national power system. Based on an analysis of 2025, it is demonstrated that hydrological changes directly impact electrical and agricultural performance. Drought conditions increase thermal generation, costs, emission intensity, and water deficits. Furthermore, the policy modification reveals tradeoffs between the power sector's emissions and agricultural water deficits. For drier scenarios, increasing agricultural extraction priority results in low additional operational costs and emissions from the power system, which supports adopting a policy aligned with netzero objectives. [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]

Gears are the basic units in modern power systems. The detection and diagnosis of surface defects and faults in gears are conducive to improving product quality, ensuring the safety of mechanical equipment, and reducing maintenance costs. However, the accuracy of manual and traditional automated target detection algorithms is not satisfactory. Therefore, this research uses the R‐CNN algorithm for gear detection, improves its non‐maximum suppression algorithm and multi‐task loss function, and obtains the improved Faster R‐CNN algorithm. The test was carried out on the built data set. The actual measurement shows that the recall rate of the improved Faster R‐CNN is up to 0.951 and the lowest is 0.816. Its AP value is as low as 0.677 and as high as 0.858, and the mAP value is 0.843. Horizontal comparison, the comparison results show that the mAP of Faster R‐CNN is 0.80 1, second only to R‐CNN among the tested algorithms, and 8.83% higher than the original Faster R‐CNN. Under the condition of AP@0.5:0.95, among all the tested algorithms, its AR index is the highest at 54.3, and the detection speed is 18 FPS/s. Although the detection speed has decreased, the detection and recognition accuracy has been significantly improved, which provides feasibility for the R‐CNN series of algorithms new optimization directions. The research provides a better automatic detection method for product quality inspection in gear manufacturing industry. [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]