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Taking into account the multiple demand differences and the existence of distribution network faults, the study conducts a multiple demand-side resource regulation capability assessment for distribution network safety and security as a way to achieve distribution network resource regulation optimization. The study considers multiple demands and adopts the correlation depth wandering algorithm to assess node faults in the distribution network, and proposes a quantitative assessment strategy for multiple demand-side resource regulation capability, and constructs an index system for assessing the resource regulation capability of the distribution network, and carries out the assessment of the resource regulation capability of the multiple demand-side resources. The results show that the regulation efficiency of wind power, thermal power, solar power and hydro power is above 20%, i.e., the combined regulation efficiency of the four energy sources is able to reach above 80%. The results show that the multi-demand-side resource regulation capability for distribution network security can be significantly improved, and the findings of the study have an important practical value for the electric power industry in terms of resource regulation and security, as well as providing a strong support for the sustainable development of the industry. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a low‐voltage ride‐through capability (LVRT) solution for the photovoltaic (PV) grid‐connected inverters compliant with the Indian grid code. The LVRT strategy ensures the uninterrupted connection of solar energy to the grid, safeguarding against system shutdown in the event of various grid faults. During this time, the inverter supplies reactive power to the utility grid, effectively utilizing converter capacity and aiding the restoration of the voltage profile at the point of common coupling (PCC). The system used in this study involves a two‐stage grid‐interfaced solar PV setup with a boost converter that tracks the maximum power point (MPP) and an inverter that maintains the DC‐link voltage. A modified dynamic braking chopper control (MDBCC) has also been implemented to minimize the DC‐link voltage oscillations during asymmetrical faults while simultaneously injecting the sinusoidal current into the grid. To improve grid synchronization in the presence of unbalanced abnormalities, a modified dual second‐order generalized integrator phase‐locked loop (DSOGI‐PLL) has been employed to track the grid voltage angle precisely. Furthermore, a new control system has been developed in conjunction with the LVRT approach. The suggested system is simulated in MATLAB/Simulink to validate the efficacy of the LVRT control method during symmetrical and asymmetrical faults using a 100‐kW two‐stage grid‐interfaced solar PV facility. Finally, for experimental validation, a real‐time test bench (RTS) OPAL‐RT 4510 is used to implement the setup with its control, and the practical results are compared with the simulation outcomes. [ABSTRACT FROM AUTHOR]

The electric power industry is a high-risk industry with frequent accidents. To ensure the life safety of employees and reduce the probability of accidents, it is necessary to utilize certain technical means to identify the main causative factors in electric fatal accident reports. This research proposes an accident causation identification method that combines text categorization and catastrophe association analysis. First, an improved text feature extraction method (TF-IDF-GloVe-LDA) is proposed by fusing term frequency-inverse document frequency (TF-IDF), global vectors for word representation (GloVe), and latent dirichlet allocation (LDA) algorithms. Then, an SVM classifier was used to categorize the 473 accident report texts into five categories, ACCIDENT, PROCESS, CAUSE, PROBLEM, and RESPONSE, based on the extracted text feature vectors. Second, for the 461 CAUSEs categorized from the accident report texts, the TF-IDF algorithm is utilized to mine them to obtain 62 accident causative factors. Using the word cloud map and semantic network, a visualization analysis is performed to reveal the intrinsic connection between causative factors. The human factor analysis and classification system (HFACS) is combined to construct a framework and a Boolean dataset for electric power fatal accident causative factors. Finally, the catastrophe progression method is utilized to improve the FP-growth algorithm and propose a catastrophe association analysis method. It is utilized to mine the Boolean dataset to obtain 28 association rules and construct the accident causal network. The main causative factors were ultimately identified by analyzing the critical causal chains within the network and examining the frequency and sensitivity of each node. The findings of this study indicate that a lack of effective management, supervision, and training serves as the fundamental cause of fatal accidents in the electric power industry when compared to equipment and environmental factors. This paper offered a fresh perspective on identifying primary accident causative factors in non-standardized text and exploring their intricate mechanisms of interaction, which holds significant implications for the pre-control management of accidents in the power industry. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a low‐voltage ride‐through capability (LVRT) solution for the photovoltaic (PV) grid‐connected inverters compliant with the Indian grid code. The LVRT strategy ensures the uninterrupted connection of solar energy to the grid, safeguarding against system shutdown in the event of various grid faults. During this time, the inverter supplies reactive power to the utility grid, effectively utilizing converter capacity and aiding the restoration of the voltage profile at the point of common coupling (PCC). The system used in this study involves a two‐stage grid‐interfaced solar PV setup with a boost converter that tracks the maximum power point (MPP) and an inverter that maintains the DC‐link voltage. A modified dynamic braking chopper control (MDBCC) has also been implemented to minimize the DC‐link voltage oscillations during asymmetrical faults while simultaneously injecting the sinusoidal current into the grid. To improve grid synchronization in the presence of unbalanced abnormalities, a modified dual second‐order generalized integrator phase‐locked loop (DSOGI‐PLL) has been employed to track the grid voltage angle precisely. Furthermore, a new control system has been developed in conjunction with the LVRT approach. The suggested system is simulated in MATLAB/Simulink to validate the efficacy of the LVRT control method during symmetrical and asymmetrical faults using a 100‐kW two‐stage grid‐interfaced solar PV facility. Finally, for experimental validation, a real‐time test bench (RTS) OPAL‐RT 4510 is used to implement the setup with its control, and the practical results are compared with the simulation outcomes. [ABSTRACT FROM AUTHOR]

In this paper, we select the China Carbon Market Price Index, which reflects the overall price changes in China's carbon market (CCM), and employs the TVP- VAR-BK model to examine the risk spillover effects between the carbon market and high-carbon-emission industries in China from a frequency domain viewpoint. Employing the nonparametric quantile Granger causality test, it delves further into the effects of economic policy uncertainty (EPU) in China on the degree of risk spillovers between the carbon market and high-carbon- emission industries. There are significant risk spillover effects between the carbon market and high-carbon-emission industries. During the short term, the carbon market affects the cement industry more than the electric power and steel industries. However, the carbon market is affected by the volatility of the high-carbon-emission industries over the long term. In addition, the effect of EPU on the magnitude of risk spillovers between the carbon market and high- carbon-emission industries is nonsignificant at extreme quartiles and significant at the middle quartile level, which is typically asymmetric. [ABSTRACT FROM AUTHOR]

Fault detection of electrical poles is part of the daily operation of power utilities to ensure the sustainability of power transmission. This paper develops a method for intelligent detection of fallen poles based on the improved YOLOX. The hyper-parameters in this method are optimized automatically by Particle Swarm Optimization (PSO) including batch size and input resolution. During parameter optimization, a specific comprehensive evaluation metric is presented as the fitness function to obtain optimal solutions with low labor cost and high method performance. In addition, virtual pole images are generated by 3D Studio Max to overcome the imbalance problem of normal and fault data. The results show that the proposed method can achieve 95.7% of recall and 98.9% of precision, which demonstrates the high accuracy of the method in fallen pole detection. In the comparative experiment, the proposed PSO-YOLOX method is superior to the existing methods including original YOLOX and Faster R-CNN, which verifies the effectiveness of automatic optimization and virtual data augmentation. [ABSTRACT FROM AUTHOR]

Construction of relay protections of electrical installations without the use of bulky metal- and insulation-intensive measuring current transformers (CTs) was repeatedly discussed at Conferences of the International Council on Large Electric Systems (CIGRE) as a challenge for the electric power industry. In the article, the authors present resource-saving reed switch current protection for 6–10-kV electrical installations connected to switchgear cells, which is an alternative to traditional protections. The scientific novelty of the work lies in (a) reed switch protection design on the basis of measuring the magnetic induction in different modes and at different points inside a cell, and we prove that inductance values are sufficient to detect phase short-circuits in electrical installations fed from this cell and derive the dependence of the induction on the position of an inductance coil inside the cell; (b) proven possibility of using the simplest formula of the Biot–Savart law for the calculations if the experimentally obtained coefficients are introduced into it; (c) development of a technique for calculating the parameters of the overcurrent protection based on reed switches. Experimental results are presented in graphical form and clearly show the points of EMS maxima and minima. Settings for resource-saving current protection are selected and a feasibility study of the effectiveness of resource-saving overcurrent protection is carried out. A technical and economical assessment of the efficiency of the suggested overcurrent protection is conducted. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid type-2 fuzzy sliding mode control approach for regulating active and reactive power exchanged with the utility grid by a doubly-fed induction generator in a wind energy conversion system. The main objective of this hybridization is to eliminate the steady-state chattering phenomenon inherent in sliding mode control while improving the transient delays caused by type-2 fuzzy controllers. In addition, the proposed control approach has proven to be successful in coping with varying generator parameters and exhibited good reference tracking. An in-depth comparative study with state-of-the-art advanced control techniques is also the focus of the present paper. The comparative study has three objectives, namely: a qualitative comparative study that aims to compare response times and reference tracking capabilities; a quantitative evaluation that takes into account time-integrated performance criteria; and finally, robustness capabilities. The simulation results, carried out in the Matlab/Simulink environment, have demonstrated the effectiveness and best performance of the proposed hybrid type-2 fuzzy sliding mode control with respect to other advanced techniques included in the comparison study. [ABSTRACT FROM AUTHOR]

This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company's coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas. [ABSTRACT FROM AUTHOR]

High-temporal resolution and timely emission estimates are essential for developing refined air quality management policies. Considering the advantages of extensive coverage, high reliability, and near real-time capabilities, in this work, electric power big data (EPBD) was first employed to obtain accurate hourly resolved facility-level air pollutant emissions information from the cement industries in Tangshan City, China. Then, the simulation optimization was elucidated by coupling the data with the weather research and forecasting (WRF)-community multiscale air quality (CMAQ) model. Simulation results based on estimated emissions effectively captured the hourly variation, with the NMB within ±50% for NO2 and PM2.5 and R greater than 0.6 for SO2. Hourly PM2.5 emissions from clinker production enterprises exhibited a relatively smooth pattern, whereas those from separate cement grinding stations displayed a distinct diurnal variation. Despite the remaining underestimation and/or overestimation of the simulation concentration, the emission inventory based on EPBD demonstrates an enhancement in simulation results, with RMSE, NMB, and NME decreasing by 9.6%, 15.8%, and 11.2%, respectively. Thus, the exploitation of the vast application potential of EPBD in the field of environmental protection could help to support the precise prevention and control of air pollution, with the possibility of the early achievement of carbon peaking and carbon neutrality targets in China and other developing countries. [ABSTRACT FROM AUTHOR]

Peak load forecasting plays an important role in electric utilities. However, the daily peak load forecasting problem, especially for holidays, is fuzzy and highly nonlinear. In order to address the nonlinearity and fuzziness of the holiday load behaviors, a grammatical evolution-based fuzzy regression approach is proposed in this paper. The proposed hybrid approach is based on the theorem that fuzzy polynomial regression can model all fuzzy functions. It employs the rules of the grammatical evolution to generate fuzzy nonlinear structures in polynomial form. Then, a two-stage fuzzy regression approach is used to determine the coefficients and calculate the fitness of the fuzzy functions. An artificial bee colony algorithm is used as the evolution system to update the elements of the grammatical evolution system. The process is repeated until a fuzzy model that best fits the load data is found. After that, the developed fuzzy nonlinear model is applied to forecast holiday peak load. Considering that different holidays possess different load patterns, a separate forecaster model is built for each holiday. Test results on real load data show that an averaged absolute percent error less than 2% can be achieved, which significantly outperforms existing methods involved in the comparison. [ABSTRACT FROM AUTHOR]

There is an increasing trend among customers of an electrical distribution utility to adopt grid-tied solar photovoltaic systems. This shift offers multiple benefits to consumers, including lower monthly electricity bills and a contribution to the development of green energy. For the electrical distribution utility, various impacts may arise due to varying levels of solar energy penetration. This study investigates the effects of integrating varying levels of solar photovoltaic penetration into the commercial consumer network of Cagayan de Oro Electric Power and Light Company (CEPALCO) in the Philippines. Utilizing PowerWorld simulator, the research evaluates 11 different scenarios with solar penetration levels adjusted according to the percentage of load demand. Key findings include alterations in solar megavolt ampere of reactive power output, bus voltage levels, transformer power loading, and transmission line ampacity, with frequency levels remaining stable across scenarios. The optimal solar penetration level was identified at 70%, balancing the benefits of solar energy integration with the need to maintain grid stability and operational limits. This optimal level ensures the effective utilization of renewable energy sources without compromising the performance of CEPALCO's electrical infrastructure. The research concludes with recommendations for maintaining grid stability and operational limits at the optimal solar penetration limits. [ABSTRACT FROM AUTHOR]

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability. [ABSTRACT FROM AUTHOR]

A continuous energy supply to the load side is required by modern power systems. This calls for a sound understanding of how to forecast load demand in the present and the future with the least degree of inaccuracy. Typically, a sequential method with two steps—forecasting and optimization—is used to derive judgments from data. For achieving this goal, optimized power flow is focused in this paper through load forecasting, mode selection, and optimization of power forecasting. Firstly, load forecasting is implemented using time series, and economic and weather-related information for the different consumer's load. Then mode selection is implemented using Hidden Markov Model that determines the requested load for grid-connected or RES mode. When composite RES is developed, the percentage of serviced load rises as more renewable energy sources are added. Following the implementation of the consumer load and mode selection, optimization is used to improve the power flow. The empirical findings show enhanced prescriptive performance when compared to answers found in single- and multi-household contexts. Also, we offer insightful information on how explaining performance is described. [ABSTRACT FROM AUTHOR]

Lock-in to existing assets and business practices is a major obstacle to the global objective of phasing out coal-fired power to accelerate decarbonization towards Paris Agreement goals. Japan has attracted attention in previous literature, with several studies demonstrating how market and political conditions have propelled investments in new coal-fired power plants while raising barriers to achieving a phase-out. But in addition to market-level conditions, firm-level characteristics – including assets, commitments, investment behaviour and future-oriented innovation activities – are also critical determinants of lock-in vulnerability. To deepen understanding of how such firm-level dynamics might affect the power industry, this empirical study takes the case of ten incumbent utilities in Japan to identify and compare potential sources of coal lock-in. We leverage diverse quantitative and qualitative data to assess the state of: (1) management, assets and operational factors; and (2) the development and deployment of next-generation coal technologies. Through this two-fold approach, we demonstrate how current conditions and future-oriented activities are likely to reinforce tendencies to continue using coal in coming decades, thereby hampering the emergence of phase-out ambitions. These findings harbour important implications for climate policy and the urgent need to end coal burning in the power sector. Japan's major power utilities all intend to maintain coal in power mixes while transitioning to net-zero by 2050, aiming to eliminate carbon emissions rather than coal-fired power per se. Ambitions to reduce dependence on coal are weakened by a multitude of potential lock-in sources across individual utilities. All utilities are attempting to commercialize next-generation technologies such as co-firing with ammonia and biomass, carbon capture and coal gasification. Phasing out coal in Japan thus requires climate policies that incentivize the development and diffusion of non-emitting power sources that offer faster emissions reduction than expensive and unproven technologies. [ABSTRACT FROM AUTHOR]

Following the rise of innovation ecosystems (IEs) in various sectors around the world, we observe a corresponding increase in IE-related studies. And while the existing body of knowledge provides a rather solid conceptualisation of the phenomenon and its distinctive characteristics, we still lack an understanding of its structure, the behaviour of participants, governance, and regulation. At the same time, most of the IE-related studies are conducted within developed contexts, which limits the generalizability of available evidence. Our study aims to fill the abovementioned gaps in the literature and makes several contributions. First, our results shed more light on the structure of IE and the role of the focal firm. Second, the study provides important empirical evidence on peculiarities of behaviour among IE participants and the composition of various IEs thus enhancing our understanding of IE development processes. Third, this research provides insights into tactics and governance mechanisms within the studied IEs, therefore, expanding the current body of knowledge on IE governance and regulation. Finally, we complement the existing body of literature with additional empirical evidence from an emerging market context. Results of this study also include valuable empirical implications for managers and policymakers in the context of the expanding IE-related activities. [ABSTRACT FROM AUTHOR]

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads with a similarity score higher than 90 move to the second stage to be evaluated. Loads that do not pass the first stage in all features are not considered for the next one. In the second stage, the scores from each feature of the loads passed to this stage are summed, and the load with the highest score is determined. It is experimentally validated that the method significantly detects correct load or load combinations for six residential appliances. Fifty-six type-tests are performed, and each type-test contains ten measurements. As a result, a total success rate of over 97 percent is obtained in all metrics. [ABSTRACT FROM AUTHOR]

Objective: This study aims to design a lean distribution model tailored for the electric power distribution network in Tehran, integrating global best practices and expert insights to address the specific challenges and opportunities within this sector. Methodology: The study employed a mixed-method approach, combining qualitative and quantitative techniques. A comprehensive literature review was conducted to identify key factors influencing lean distribution. Expert interviews with managers and industry specialists in Tehran's electric power distribution sector provided additional insights. The factors were structured and categorized using Interpretive Structural Modeling (ISM) and MicMac analysis to establish their interrelationships and influence. Findings: The study identified 16 critical factors influencing lean distribution, with "World-Class Lean Distribution in the Electric Power Industry" emerging as the most influential. Other key factors included "Optimal Distribution of Reactive Power in Power Systems," "Estimation of Load Uncertainty," "Reduction of Loss Levels," and "Use of Fixed and Switchable Capacitors." The findings highlight the importance of infrastructure, ISO 9000 implementation, technological integration, internal capacity, and resilience. Conclusion: The research provides a comprehensive framework for implementing lean distribution practices in the electric power industry, emphasizing the need for global standards, technological advancements, and resilience. The findings suggest that adopting these practices can lead to improved reliability, reduced operational costs, and enhanced customer satisfaction, contributing to sustainable development and energy security. [ABSTRACT FROM AUTHOR]

China is constantly seeking rapid, high-quality growth in order to meet its carbon peaking and neutrality goals. Approximately 40% of China's carbon emissions come from the electric power industry, which is beset by issues of poor efficiency and excessive emissions. Thus, it is essential to determine if environmental restrictions increase economic benefits total factor productivity while still preserving the environment. We use the Quasi-DID method to examine the impact of carbon emissions trading scheme on firm-level total factor productivity of electric power companies. The findings demonstrate the following: (1) carbon emissions trading scheme considerably impedes total factor productivity development; (2) the primary cause of this detrimental impact is the need for additional improvements in marketization since green innovation is still in its infancy; (3) additional study indicates that law enforcement's heterogeneity is what affects this restriction. Our research may both enhance the Chinese carbon emissions trading scheme's effectiveness assessment framework and point out several potential avenues for high-quality growth. [ABSTRACT FROM AUTHOR]

This intervention considers uneven development and social reproduction within racial capitalism. Social reproduction refers to the range of practices that form the conditions of possibility for the life of capital, as well as life and death within racial capitalism. This spans a range of institutions and networks within households, communities, states and across national borders as well as the labour practices, relations and organization that reproduce racial capitalism. Here, we examine the extraction of time, taking up theorizations across carceral geographies, postcolonial theory and Caribbean studies to demonstrate how coercive relations of social reproduction contribute to uneven development. In particular, we look at the role of the state in racial capital's capture of reproductive activities across our work on electric utilities in Atlanta, Georgia and extralegal land tenure on Jamaica's north coast. In bringing these distinct sites into conversation, we re-affirm the need to study uneven development by understanding how the circulation and accumulation of capital is imbricated with the production of hierarchies of all kinds of difference. We show how a conjunctural countertopography can reveal how state practices advance accumulation under conditions of widespread surplus lives, as capital wagers on captive life and premature death. [ABSTRACT FROM AUTHOR]