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The degradation of the insulation system in liquid-filled power transformers is a serious concern for electric power utilities. The insulation system's ageing is accelerated by moisture, acids, oxidation products, and other decay particles (soluble and colloidal). The presence of these ageing by-products is detrimental to the insulation system and may further lead to premature ageing and serious consequences. The ageing mechanisms of oil-paper insulation are complex, highly interrelated, and strongly temperature-dependent. The operating temperature of the transformer insulating system has a direct relationship with the loading profile. The major aspect that is witnessed with the fluctuating temperatures is moisture migration and subsequent bubble evolution. In other words, gas bubbles evolve from the release of water vapor from the cellulosic insulation wrapped around the transformer windings. The models presented in the existing standards, such as the IEC Std. 60076-7:2018 and the IEEE Std. C57.91:2011, are mainly based on the insulation temperature, which acts as a key parameter. Several studies have investigated the moisture dynamics and bubbling phenomenon as a function of the water content in the paper and the state of the insulation system. Some studies have reported different prototypes for the estimation of the bubble inception temperatures under selected conditions. However, there are various attributes of the insulation system that are to be considered, especially when expanding the models for the alternative liquids. This paper reviews various evaluation models reported in the literature that help understand the bubbling phenomenon in transformer insulation. The discussions also keep us in the loop on the estimation of bubbling behavior in alternative dielectric liquids and key attributable factors for use in transformers. In addition, useful tutorial elements focusing on the bubbling issue in transformers as well as some critical analyses are addressed for future research on this topic. [ABSTRACT FROM AUTHOR]

Dielectric testing techniques, in both time and frequency domains, are currently widely used by power utilities for assessment of the condition of transformer oil-paper insulation systems. However, it has been reported that results of these tests are highly influenced by the operating temperature during measurements. The distribution, migration and equilibrium of moisture between oil and paper in a complicated insulation system is highly temperature dependent. It requires adequate experience and proper understanding to interpret the dielectric response results in the presence of temperature variations and thermal instability. Proper analysis of the dielectric test result is only possible with an understanding of the physical behavior of the insulation system in response to temperature. A circuit model, which describes the dielectric behavior of the transformers main insulation system, has been investigated in this paper. The values of the parameters of the model have been identified from the dielectric tests. A correlation has been observed between the operating temperature and the equivalent model parameters that can be used as additional information for better interpretation of the dielectric test results. This paper thus reports a detailed study on the effects of temperature on dielectric measurements of a transformer under controlled laboratory conditions. Some results of practical on-site testing are also presented to demonstrate the possibility of errors that may be introduced in dielectric test results analysis unless temperature effects are taken into consideration. [ABSTRACT FROM AUTHOR]

This paper seeks to bring to the attention of regulatory economists and policymakers the existence of some approaches to utility regulation that have hitherto received little or no attention in the economics literature. It begins by noting that regulatory actions have discouraged certain kinds of retail contracts in the UK that are offered in Nordic countries, almost extinguished retail competition in Ohio, and distorted the market for merchant interconnectors in Australia. In contrast, Argentina secured an efficient appraisal and implementation of transmission expansions by empowering users and severely limiting the role for regulation. Some US and Canadian jurisdictions have fostered the emergence of negotiated settlements that exhibit considerable innovation, and greater benefits for all parties than would have been possible with the conventional regulatory approach. These examples of the actual regulatory world can inform the work of theorists and policymakers. [ABSTRACT FROM AUTHOR]

This article explores the impact of electric vehicle (EV) charging on power systems and emphasizes the importance of careful analysis and planning. It discusses various topics related to EV charging, including EV hosting capacity, optimal placement of EVs and charging stations, technical impacts of Vehicle-to-Grid (V2G) technology, shared vehicle rental, decentralised collaboration frameworks, intelligent energy management schemes, EV demand response, and optimal sizing and siting of public EV charging stations. The article provides valuable insights and solutions for utilities, academia, and policymakers to address the challenges and opportunities associated with EV charging. Additionally, the article introduces a new approach for determining the limits of transformers and overhead lines in relation to EV charging, using Monte Carlo simulations and proposing new parameters for evaluating maximum demands. The authors stress the need for further analysis and attention to the impact of EVs on power systems. [Extracted from the article]

Comments on writer Paul Joskow's paper about the reasons for restructuring an electricity sector, published in a 1998 issue of `The Energy Journal.' International forefront of the deregulation and privatization process; Increased competition in the electricity industries as the means to provide greater customer choice and reduce prices.

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]

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]

Comments on Paul Joskow's paper on the transition of the electricity sectors worldwide published in a 1998 issue of `The Energy Journal.' Concern expressed by certain states to preserve their energy independence and to develop their national infrastructure; Short-term competitiveness that will lead to declining investments by the power generation and power transmission industry.

Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers. [ABSTRACT FROM AUTHOR]

Increasing productivity without jeopardizing the network's operation, consumers' experience, and the safety and integrity of procedures, is a major goal for all utilities. Scheduling of work has a direct impact on productivity, especially in the case of utilities that cover a wide geographical area using a limited number of employees. In the case of power utilities, however, scheduling has to consider, apart from the location of issues, the type and technical characteristics of each issue as well as its priority has to be considered in order to produce an optimum schedule. This paper focuses on the impact on productivity of a geospatial ticket management system considering the experience from such a system applied on network studies performed by HEDNO, the Greek Distribution Network Operator, in Patras Area. The term "network studies" is used to describe the study of all expansion and alteration works, ranging from a single Low Voltage (LV) pole installation to major Medium Voltage (MV) network rearrangements/expansion, and includes visits and measurements on the actual location as well as in-door calculations. In Patras Area, the local HEDNO division implemented in 2021-2022 a geospatial ticket managing system, based on available network data and custom Google Maps, aiming to increase its productivity by optimizing the scheduling process. Initial results published in February 2022, showed a significant productivity increase (up to 42%). However, the initial results considered a time span of only one month and thus could easily be misleading. This paper revisits the issue considering a larger time span (more than two years) that should provide more trustworthy results. It also briefly presents the latest updates and improvements made to the system. Results show that the increase in the number of studies and their predicted costs are similar to the initial results, with the increase in productivity being around 41%. In September 2022, HEDNO set the very ambitious goal of significantly increasing the overall production of network studies (more than double in terms of predicted costs) and the use of such a system can provide valuable help towards achieving this goal. [ABSTRACT FROM AUTHOR]

One of the issues faced by asset managers in the electric power industry is the condition and remaining life assessment of the high voltage (HV) assets. In contrast to other power equipment, transmission power cables are mainly subjected to corrective maintenance. To support the asset management of service aged transmission power cable circuits, this paper discusses an approach of integrated knowledge rules for condition-based maintenance based on laboratory investigations and field examples. There are very few recent publications on this subject in spite of aging power cable infrastructure; therefore, this paper aims at showing practical applications of condition-based maintenance of this important component of the transmission and distribution systems. [ABSTRACT FROM AUTHOR]

This paper provides the electrical equivalent circuits of high-resistance grounded (HRG) power systems other than the delta–wye-connected utility power supply transformer included in Part I of this paper. This paper also provides the HRG of a medium-voltage generator for connection to an industrial power system and to a utility power system, and this paper discusses a line–ground fault with fault resistance. Three-line diagrams of ungrounded power systems are also included to illustrate the HRG application and its effect on the ground fault current during the line–ground fault. The damage at the fault location is based on the assumption that the fault remains a line–ground fault until it is cleared by appropriate ground fault protection relays. The fault resistance and the fault current that have an effect on the damage at the fault location are included. This paper provides guidance to update the current edition of the IEEE STD. 142, with respect to HRG systems. [ABSTRACT FROM AUTHOR]

Peak shaving techniques have become increasingly important for managing peak demand and improving the reliability, efficiency, and resilience of modern power systems. In this review paper, we examine different peak shaving strategies for smart grids, including battery energy storage systems, nuclear and battery storage power plants, hybrid energy storage systems, photovoltaic system installations, the real-time scheduling of household appliances, repurposed electric vehicle batteries, uni- and bi-directional electric vehicle charging, the demand response, the time-of-use pricing, load shedding systems, distributed generation systems and energy-efficient management. We analyze the potential of each strategy to reduce peak demand and shift energy consumption to off-peak hours, as well as identify the key themes critical to the success of peak shaving for smart grids, including effective coordination and communication, data analytics and predictive modeling and clear policy and regulatory frameworks. Our review highlights the diverse range of innovative technologies and techniques available to utilities and power system operators and it emphasizes the need for continued research and development in emerging areas such as blockchain technology and artificial intelligence. Overall, the implementation of peak shaving strategies represents a significant step toward a more sustainable, reliable and efficient power system. By leveraging the latest technologies and techniques available, utilities and power system operators can better manage peak demand, integrate renewable energy sources, and create a more reliable and secure grid for the future. By discussing cutting-edge technologies and methods to effectively manage peak demand and incorporate renewable energy sources, this review paper emphasizes the significance of peak shaving strategies for smart grids as a crucial pathway towards realizing a more sustainable, dependable and efficient power system. [ABSTRACT FROM AUTHOR]

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In interconnected power systems, operated by several system operators, the participating areas are strongly dependent on their neighbors. In order to identify the economically efficient generation dispatch in each area, a distributed multi-area optimal power flow (OPF) must be solved. Additionally, installation of an increasing number of HVDC lines to deal with increased power flows from renewable generation is expected to change the power system operation paradigm. The controllability introduced by the HVDC lines should be considered and incorporated in the OPF algorithm. In this paper we introduce a formulation for the distributed solution of the OPF problem in multi-area systems consisting of both HVAC and HVDC lines. We show the applicability of this formulation on two different operating schemes for HVDC grids and we compare their performance with a central solution for the mixed HVAC/HVDC grid. The proposed formulations are based on a linearized solution of the OPF problem. The only data to be exchanged between the areas pertains to the border nodes. [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]

Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study's contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions. [ABSTRACT FROM AUTHOR]

Governments, regulatory bodies, and manufacturers are proposing plans to accelerate the adoption of electric vehicles (EVs), with the goal of reducing the impact of greenhouse gases and pollutants from internal combustion engines on human health and climate change. In this context, the paper considers a scenario where ride-sharing enterprises utilize a 100%-electrified fleet of vehicles, and seeks responses to the following key question: How can renewable-based EV charging be maximized without disrupting the quality of the ride-sharing services? We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company. Our mechanism is inspired by a game-theoretic approach where the power utility company proposes incentives and the ride-sharing platform assigns vehicles to both ride and charge requests; the bargaining mechanism leads to prices and EV assignments that are aligned with the notion of Nash equilibria. Numerical results show that it is possible to shift the EV charging during periods of high renewable generation and adapt to intermittent generation while minimizing the impact on the quality of service. The paper also investigates how the users' willingness to ride-share affects the charging strategy and the quality of service. [ABSTRACT FROM AUTHOR]

In the electric power industry of Ukraine, the main system-forming objects are power transmission lines and substations with 330(220) kV switchgear. The most powerful sources of danger at substations are short circuits on the busbars, a direct lightning strike or the operation of overvoltage limiters and arresters.The purpose of the article is to develop a method for determining the resistance of the grounding system of substations with open switchgear 330(220) kV, aimed at increasing accuracy, taking into account the most influential independent factors. The provisions of the theory of experiment planning, the theory of electric circuits, mathematical modeling in the package Grounding 1.0, LiGro and Microsoft Excel were used to conduct the research. In the paper, a comparative analysis of the known formulas for determining the resistance of the grounding system is carried out: the formula of the GOST 12.1.30-81 standard, Ohlendorf-Laurant, Laurent and Neumann, Schwartz, Sverak formulas, as well as the formula of the international standard IEEE Std 80-2013. The results of multifactorial experiments to determine the dependence of the resistance of the grounding system on its area, the size of the grid cell, the perimeter of the cross-section of the grounding, the equivalent specific resistance of the soil, the short-circuit current and the depth of the grid location are given. A graphical comparison of the results of determining the resistance according to the considered formulas was carried out. In the paper, the method of determining the parameters of grounding system of 330(220) kV substations was further developed, aimed at ensuring standardized values of the resistance of the grounding system and the ground potential rise using formulas obtained from the results of multifactorial experiments. The results obtained in the work will make it possible to reduce the probability of damage to the insulation of 6- 10 kV cables, as well as cables of secondary circuits from low-frequency and high-frequency (pulse) voltages when large currents flow into the grounding system in the mode of a single-phase short-circuit to the ground in electrical grid with a grounded neutral. [ABSTRACT FROM AUTHOR]

These surrogate models replace the machine model and estimate the output parameters of the machine based on the input parameters. Induction machines are one of the most important technical applications for both the industrial world and private use. The main purpose of this paper was to develop methods to reduce the computational effort in the design and optimization of IMs using the finite element method or analytical methods. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among different IM conditions, i.e., a machine in good physical condition, a machine with one broken rotor bar, a machine with two broken rotor bars, a machine with damage on the bearing outer race, and a machine with an unbalanced mechanical load. [Extracted from the article]

Search for a reliable and efficient insulation diagnostic tool has always been the interest of power utilities. Today a large number of methods are available that can be used for insulation condition monitoring. These methods include both traditional and newer techniques. However due to complex aging process of oil paper insulation under the influence of different types of stresses, insulation condition assessment is generally performed by experts after carefully evaluating different measurement data. Furthermore, measurement data are influenced by various factors (like conductive aging byproducts, furanic compounds, paper and oil-moisture) in addition to measurement error (if any). This makes prediction of insulation condition based on single type of measurement rather difficult. This paper presents an Expert System designed to perform insulation diagnosis. The Expert System considers measurement data obtained using both traditional and newer techniques in order to come to a definitive conclusion. The Expert System extracts insulation condition sensitive information from data obtained using different techniques and then uses these to devise an optimized insulation model. This optimized model is used to predict papermoisture content and other insulation condition sensitive parameters. Since these values are predicted using optimized model, they are not dependent on a single type of measurement and hence are less likely to be affected by error of any specific measurement. The performance of the developed Expert System is first tested on a laboratory sample and then on several real life power transformers belonging to NTPC Ltd. [ABSTRACT FROM AUTHOR]

The South African government is navigating many basic municipal service delivery challenges, including a growing electricity supply deficit. Eskom Holdings SOC Limited, the state-owned power utility, is struggling to generate and supply a stable and uninterrupted flow of electricity through its grid system. The inadequate generation capacity results in rotating power outages, known as loadshedding, which occur when demand surpasses generating capability. This fundamental service delivery challenge, in conjunction with South Africa's climate change responses, including the decision to work towards energy efficiency, renewable energy and cleaner energy or a "just transition" from coal to clean energy, encourages many South Africans who have the necessary means to invest in off-grid energy solutions that operate alongside and at times independently of Eskom's grid. This paper considers from a legal perspective how and to what extent legislation on electricity supply and municipal by-laws empower household consumers to fulfil their right to electricity by going off-grid. The paper ultimately considers the import of this on South Africa's energy governance framework for electricity provision in the country. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis of decarbonizing the electricity sector in the Dominican Republic, pivotal for addressing climate change and fostering economic growth. Employing the robust-decision making methodology, we studied multiple scenarios via computational models, capturing inputs from stakeholders and evaluating each scenario across 1,000 futures to capture deep uncertainty. Four scenarios were examined: baseline, reference, natural gas, and renewable. The renewable scenario emerged as the most advantageous, proposing the replacement of coal-fired power generation with renewable sources, primarily solar and wind, coupled with batteries. A significant investment, averaging US$3.3 billion, is necessary for this shift toward renewable energy; however, these investments are overcompensated by savings in operational costs. Crucially, this transition promises substantial benefits by 2050: an estimated cumulative average net economic gain of US$2.7 billion, an 8% reduction in average generation costs in 2050, the creation of 160,000 direct jobs, and the avoidance of circa 140 million tons of C02. The findings underscore the feasibility and economic viability of transitioning to a 55% renewable energy generation by 2050. The study offers a critical roadmap for policymakers, highlighting renewable energy expansion, transmission grid strengthening, and strategic coal generation replacement, thus offering a comprehensive blueprint for the nation's energy transition. [ABSTRACT FROM AUTHOR]

This paper presents a building‐level post‐hazard functionality model for communities exposed to flood hazards including the interdependencies between the population, buildings, and infrastructure. An existing portfolio of building archetypes is used to model the post‐hazard physical flood functionality of different building typologies within the community with the goal of supporting resilience‐informed decision‐making. Specific fragility‐based flood functionality curves were developed for this portfolio to quantify the exceedance probability of a prescribed set of functionality states. While the physical functionality of buildings is significant to the total functionality of a building and community resilience assessment, the functionality of utilities such as power and water along with the accessibility of the household to essential services such as schools and hospitals is crucial to measure their total functionality. Therefore, functionality models for essential infrastructure were developed to assess housing unit‐ and building‐level functionality following flood hazards. This model also accounts for the functionality of the road network following a flood hazard to identify the level of accessibility of households to different services (e.g., schools, hospitals, gas stations, shopping centers, etc.). The main novelty of this paper is the ability to quantify the total functionality of buildings based on a socio‐physical formulation after including the interdependencies between the functionality of the physical systems and the subsequent functionality of the socio‐economic systems, which is key to measuring resilience at the community level. This was evident from the analysis results using a testbed community of Lumberton, NC, showing that the physical functionality is not sufficient to quantify the total post‐hazard functionality of buildings and that the functionality of other subsystems such as utilities and accessibility to essential services are also needed to quantify the total functionality of buildings and the livability of households. [ABSTRACT FROM AUTHOR]

Tokamaks are affected by two serious problems. The first problem is the radiation damage to the internal reactor structures by energetic neutrons carrying 80% of the total fusion energy that are generated by the deuterium–tritium fusion cycle that tokamaks are planning to use. The second problem is a loss of plasma control because of the onset of instabilities, causing the sudden shutdown of the tokamak itself. Certainly, no electric utility knowing about these disruptive events would dare to use a tokamak reactor for powering the turbines of its power plant! This paper describes a new fusion reactor based on the Model-C Stellarator that would not be affected by these fatal phenomena. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

Short-term electric load forecasting (STLF) plays a pivotal role in modern power system management, bolstering forecasting accuracy and efficiency. This enhancement assists power utilities in formulating robust operational strategies, consequently fostering economic and social advantages within the systems. Existing methods employed for STLF either exhibit poor forecasting performance or require longer computational time. To address these challenges, this paper introduces a hybrid learning approach comprising variational mode decomposition (VMD) and random vector functional link network (RVFL). The RVFL network, serving as a universal approximator, showcases remarkable accuracy and fast computation, owing to the randomly generated weights connecting input and hidden layers. Additionally, the direct links between hidden and output layers, combined with the availability of a closed-form solution for parameter computation, further contribute to its efficiency. The effectiveness of the proposed VMD-RVFL was assessed using electric load datasets obtained from the Australian Energy Market Operator (AEMO). Moreover, the effectiveness of the proposed method is demonstrated by comparing it with existing benchmark forecasting methods using two performance indices such as root mean square error (RMSE) and mean absolute percentage error (MAPE). As a result, our proposed method requires less computational time and yielded accurate and robust prediction performance when compared with existing methods. [ABSTRACT FROM AUTHOR]

Electric power distribution networks are exposed to both internal and external disturbances. Lightning strikes are among the latter and are responsible for a significant percentage of damage in distribution transformers, especially in rural areas. Electric utilities must pay special attention to prevent damage and service interruption due to these unforeseeable events. In this context, Surge Protection Devices (SPDs) combined with a series of external air gaps are designed to safeguard electric equipment and systems from transient over-voltages. There are several well-known models of SPDs in the specialized literature; nonetheless, few studies have been carried out with external gaps and multi-gaps. The main contribution of this paper is a methodology to model the disruptive effect in an external air gap by determining the parameters of Kind's and Chowdhuri's models using the integration method. The adjustment of the model parameters is carried out by a genetic algorithm (GA). The proposed model was tested and validated using experimental measurements, and its capability to predict the time-to-breakdown under different impulse voltages was verified. [ABSTRACT FROM AUTHOR]

Extensive efforts have been made for many years by the power generating industry to replace conventional solid and liquid insulation with synthetic materials. Those measures are aimed at increasing the load capacity, improved fire safety and extending transformer life during exploitation. Modern insulating materials include aramid fibre-based paper and insulating fluids made of synthetic and natural esters. The paper presents research results of the electrostatic charging tendency (ECT) of mixtures of fresh and aged mineral oil Trafo En with synthetic ester Midel 7131 and natural ester Midel 1204. The measurements were taken in a flow-through system using the pipes made of metal, cellulose and aramid paper. The influence of the liquid flow velocity, the type of material of the measuring pipe and the mixture content on the level of the streaming electrification current generation was determined. [ABSTRACT FROM AUTHOR]

More than thirty-six partial or whole blackouts, from 2016 to 2021, prove that neither the probability of blackouts can be ignored nor the required plans and the readiness of the utilities for power system restoration can be forgotten. To properly deal with blackouts and minimize the social and economic costs, utilities should prepare a suitable restoration plan in advance to quickly restore the system to the normal state and minimize the restoration time and loss of energy. In this paper, after summarizing major blackouts occurred from 2016 to 2021, the progress in the research reports, industrial documents, and books in the context of power system restoration is reviewed to show and classify the different aspects of power system restoration plans and strategies presented during these years. This review indicates that although there are many published methods and techniques which have tried to give more autonomous solutions, there are still more complexities in power system restoration. However, this paper respects all efforts and attempts and tries to illustrate the achievements, up to now, in power system restoration, as well as research gaps. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization approach for day-ahead reactive power planning to improve voltage security in transmission networks. The problem is formulated as a voltage-secure multi-period optimal reactive power dispatch (MP-ORPD) problem. The optimization approach searches for optimal set-points of dynamic and static reactive power (var) resources. Specifically, the output includes set-points for switching shunts, transformer taps, and voltage magnitudes at the regulated buses. The primary goal is to maximize the dynamic reactive power reserve of the system, by minimizing the reactive power supplied by synchronous generators. As the size of the MP-ORPD problem increases significantly with increasing number of contingencies and time periods, efficiency is crucial for practical applications. In this paper, a decomposition technique based on consensus and alternating optimization, where integer variable targets are obtained via MILP, is used to partition the MP-ORPD problem into a set of subproblems, which can be solved in parallel to reduce the computation time. The proposed MP-ORPD problem and its solution algorithm are integrated into the EPRI-VCA software. The results of various power networks of large electric utilities in the Eastern interconnection demonstrate the effectiveness of the proposed algorithm in providing preventive control schedules. [ABSTRACT FROM AUTHOR]

The need for economic, reliable, and effective delivery of electric power has lead to the search for fast, efficient, and effective methods for diagnosing the insulation of high-voltage (HV) equipment in the power industries. The recent dielectric techniques that have been carefully considered by major industries for transformer insulation condition assessment are the recovery voltage method (RVM) and the polarization and depolarization current (PDC) measurement. However, due to the complexity of the transformer insulation structure and various degradation mechanisms under multiple stresses, insulation condition assessment is usually performed by experts with special knowledge and experience. In this paper, an expert system (ES) is developed, which imitates the performance of a human expert, to make the complicated insulation condition assessment procedure accessible to plant maintenance engineers. The structure of the ES is described in detail including knowledge base, inference engine, and human-computer interface. Examples of the application of the ES are also presented to confirm that the system can provide accurate insulation diagnosis. [ABSTRACT FROM AUTHOR]

Energy crises and the growth of the energy demand have increased the interest in utilizing unconventional power sources. Thus, renewable energy sources have become a topic of interest to mitigate rising energy concerns and cope with increased electricity demand. With remarkable merits including cleanness and abundance, photovoltaic (PV) solar energy systems are a key to solving these issues. The employed inverters should effectively utilize the maximum available power from the PV solar system and transfer this power to the utility grid without posing any further limitations. However, the unequal power generation of different PV systems caused by partial shading (PS) and other PV panel degradation factors leads to a reduction in generation capacity. One of the relatively new solutions to mitigate the mismatch concerns between the PV modules and sub-modules is to extract the maximum power of each sub-module individually. The main objective of this paper is to present a comprehensive review of such PV grid-connected inverters topologies associated with sub-module connection and control. It will classify the PV grid-tied inverters in accordance with the level where the maximum power point tracking (MPPT) system is implemented. A special focus has been placed on sub-module microinverters (MI) in terms of circuit topologies, conversion efficiency, and controller design. This paper provides a comprehensive analysis of employing the distributed MPPT (DMPPT) approach to maximize the power generation of PV systems by mitigating the mismatch issues inside the PV module. The circuit topology, PV system configuration, and MPPT algorithms used for applying DMPPT solutions in PV SMs are discussed in detail in this study. [ABSTRACT FROM AUTHOR]

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44 × 10 − 12 , outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply. [ABSTRACT FROM AUTHOR]

In the analogy where achieving the "carbon peaking and carbon neutrality" goals (the "dual carbon" goals) is compared to a battle, energy is the main battlefield, and electric power the main force. The low-carbon transformation path of the electric power industry exerts fundamental impact on progress towards carbon peaking and carbon neutrality of the whole society. Therefore, this paper first analyzes challenges and opportunities faced by the electric power industry to achieve the "dual carbon" goals; secondly, on the basis of deep low-carbon, zero-carbon and negative-carbon scenarios of the electric power industry, this paper quantifies and delves into the consumption structure of primary energy and final energy, as well as the future development positioning and trend of different categories of energy. Based on the research results, this paper proposes low-carbon transformation path of the electric power system at the stages of carbon peaking, deep low-carbon and carbon neutrality, and analyzes the trend of power supply cost changes in different scenarios. On this basis, a series of implementation key points and measures are proposed to adapt to green and low-carbon transformation of the electric power industry, covering areas such as coal-fired electric power development positioning, new energy development and utilization, diversified supply and demand, new energy industry chain, and power supply costs, and so forth. [ABSTRACT FROM AUTHOR]

The lack of information regarding the systemic behavior of new software and hardware before introducing them for controlling and operating power networks consists of a major challenge for power distribution utilities towards a smart grid implementation. In this context, this paper describes an innovative and cost-effective test platform for advanced distribution automation (ADA) functionalities. The platform integrates a steady-state power system simulator, intelligent electronic devices, smart meters, communication infrastructure, and typical operation systems, using a cyber-physical system approach. The development of such platform originated a novel smart grid emulator, aiming at the execution of systemic tests and validation of ADA functionalities in a controlled environment, allowing systemic analysis rarely found in hardware-in-the-loop (HIL) testbeds. Through such an approach, it is possible to predict and solve typical issues that would be typically noticed only in field implementation. The study case presents tests of a specific algorithm for downed conductor location, considering the environment that the power distribution utility would have through the deployment of a smart grid infrastructure. The paper also discusses the robustness of the event-driven synchronization as an interface technique for HIL and software-in-the-loop environment, which is responsible for integrating operation systems, field equipment, and the power system simulator. [ABSTRACT FROM AUTHOR]

Power system planning faces various issues related to reliability and quality evaluation. The power system network planning is by nature a complex, huge-scale, and mixed-objective optimization problem, especially when concerning its non-linear behavior and the requirements of future unknown loads. In this regard, the electric power utilities attempt to maintain a balance between the generation energy, the transmission capacity, and the needed demand. The main purpose of the current paper is to utilize modern modeling techniques and computational procedures, including the advanced deficit transmission system evaluation method and sparse-matrix network analysis algorithms, in order to evaluate, with sufficient accuracy, the deficit and reliability levels in practical real-life large-scale power systems. The new evaluation methodology is based on three quantities representing the relationship between the generation push in the grid, the maximum limitation of the transmission capacity, and the needed load. The main contribution of the paper is assessing the deficit transmission system index with novel formulas. [ABSTRACT FROM AUTHOR]

In this paper we analyze the links between the agent's reservation utility, bargaining power, and risk aversion in terms of their simultaneous effects on the structure of optimal static contracts. We compare the following principal-agent models in the symmetric and asymmetric information environments: the standard approach, which includes a participation constraint, and a multi-objective (MO) optimization approach in which the objective function is a convex combination of the expected utilities of the principal and the agent. The MO model does not include a participation constraint, but it includes a parameter for the agent's bargaining power. We also study an Evolutionary Algorithm implementation of the static principal-agent model to support and extend our analytical results. We show that the numerical solution approximated by our implementation of an evolutionary algorithm is in line with the analytical solutions mentioned before. That is, for every admissible value of the agent's reservation utility, there is a corresponding admissible value of the agent's bargaining parameter, both in the MO approach and the EA implementation. [ABSTRACT FROM AUTHOR]

Electricity theft damages power grid infrastructure and is also responsible for huge revenue losses for electric utilities. Integrating smart meters in traditional power grids enables real-time monitoring and collection of consumers' electricity consumption (EC) data. Based on the collected data, it is possible to identify the normal and malicious behavior of consumers by analyzing the data using machine learning (ML) and deep learning methods. This paper proposes a deep learning-based meta-learner model to distinguish between normal and malicious patterns in EC data. The proposed model consists of two stages. In Fold-0, the ML classifiers extract diverse knowledge and learns based on EC data. In Fold-1, a multilayer perceptron is used as a meta-learner, which takes the prediction results of Fold-0 classifiers as input, automatically learns non-linear relationships among them, and extracts hidden complicated features to classify normal and malicious behaviors. Therefore, the proposed model controls the overfitting problem and achieves high accuracy. Moreover, extensive experiments are conducted to compare its performance with boosting, bagging, standalone conventional ML classifiers, and baseline models published in toptier outlets. The proposed model is evaluated using a real EC dataset, which is provided by the Energy Informatics Group in Pakistan. The model achieves 0.910 ROC-AUC and 0.988 PR-AUC values on the test dataset, which are higher than those of the compared models. [ABSTRACT FROM AUTHOR]

At present, the reform of electricity marketization has become a new trend in the development of the world's electric power industry and electricity retailers have a huge role in further activating power market competition. Simulation of the bidding behavior of electricity sales companies is helpful to analyze the rationality of the transaction behaviors of electricity sales companies and predict the potential risks of market operation. Considering the complex and diverse behaviors of electricity retailers and the immaturity of existing simulation technologies in the power sales market, this paper proposes a Q -learning algorithm-based simulation method for the bidding behavior of electricity retailers. Firstly, we analyze the factors influencing the bidding behavior of electricity retailers, then, we construct a simulation model of electricity retailers' bidding behavior based on the Q -learning algorithm, and finally, we simulate the bidding process of medium- and long-term trading in provincial power markets by using simultaneous learning from both sides of the generation and consumption. The results show that the behavior of different types of electricity retailers meets the expected level of the model and the simulation method has generalization and extension significance. On this basis, this paper also proposes a bid simulation system architecture based on the bid line simulation model of electricity retailers, which provides a certain reference value for market operation agencies to carry out the corresponding deduction work. [ABSTRACT FROM AUTHOR]

Economic models assume public utility commissions reform utility business models with revenue decoupling mechanisms primarily to remove the disincentive for demand-side management investment, which is expected to enhance social welfare. This paper tests that widespread assumption. We find some but limited support for commission responsiveness to avoided environmental costs. Instead, we find commission responsiveness to avoided political costs resulting from high prices of residential electricity compared to the regional average and high levels of partisan competition in the state legislature. Beyond questioning the primacy of the public interest rationale for regulation, our results give reason to reevaluate economic models of utility business model reform that do not explicitly consider commission interests in minimizing political risks. [ABSTRACT FROM AUTHOR]

With the growing requirements imposed by regulatory authorities, grid operators and power utilities firms are confronted with the challenging task of ensuring the reliability, safety, and resilience of distribution networks amid aging asset infrastructure and a lack of resources. Over the past 15 years, the health index (HI)-based analysis has become an increasingly popular asset management tool for several power utilities. This strongly focuses on HI-based analysis to consider not only factors such as the cable vintage, type, and operating conditions, but also maintenance testing data. In this regard, the best industry practices for cable maintenance testing, including VLF Tan-Delta, partial discharge (PD), and time domain reflectometry (TDR), are outlined. Moreover, online tests, such as infrared thermography, ultrasonic PD scan, and temperature monitoring, are discussed. This paper also summarizes the classic asset management strategies for underground (UG) distribution power cables. The paper offers a practical approach for cable fleet management based on authors' experience dealing with distribution power utility cable management for North American power utilities firms in the past 10 years. The proposed approach ensures reliable cable management at the lowest total life cycle cost. The topic of fleet management for UG power cables considering various condition parameters and an overall risk assessment is outlined. The fleet management guideline of UG power cables covers both cables and their accessories, such as terminations and joints. The main contributions of the paper are to: (1) determine the key parameters and testing factors for condition assessment of cables; (2) offer a practical approach to cable management that is not only based on technical issues, but also considers risk and impact costs, such as financial impact, reliability impact, etc.; and (3) propose a methodology for translating the HI/calculated risks into GIS, making it possible to identify major degradation patterns for fleet assessment. Considering budget and resource limitations around testing UG cable installations, this paper aims to assist asset managers, engineers, and asset owners in developing an effective cable fleet management strategy. [ABSTRACT FROM AUTHOR]

Technical and non‐technical losses in distribution circuits result in significant economic costs to power utilities. One type of non‐technical loss is energy theft by various means including illegal tapping of feeders, bypassing the meter, and billing fraud. These losses are usually hard to detect, and can remain undetected for long periods of time. Machine learning models have been proven effective in detecting these conditions, but rely on the availability of large, good‐quality training data sets. The problem is exacerbated by the imbalanced nature of data related to these conditions—energy theft, though costly, is very rare. The available data sets generally have very few samples of theft with most of the data pertaining to normal operation. Such data sets are generally not suitable to train machine learning models. In this paper, an overview of energy theft detection techniques, the challenges with their data needs, and the limitations of current techniques to bridge such data limitations is presented. A co‐simulation framework is proposed to generate reliable training data for machine learning algorithms for theft detection. An example scenario is presented and a machine learning model is built to detect certain kinds of energy theft. [ABSTRACT FROM AUTHOR]

Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), and (3) Recurrent Neural Networks (RNN). The models were tested, and the GRU model achieved the best performance in terms of accuracy and the lowest error. Results show that the GRU model achieved an R-squared of 90.228%, Mean Square Error (MSE) of 0.00215, and Mean Absolute Error (MAE) of 0.03266. [ABSTRACT FROM AUTHOR]

Due to the high risk factors in the electric power industry, the safety of power system can be improved by using the surveillance system to predict and warn the operators' nonstandard and unsafe actions in real time. In this paper, aiming at the real-time and accuracy requirements in video intelligent surveillance, a method based on edge computing architecture is proposed to judge unsafe actions of electric power operations in time. In this method, the service of unsafe actions judgment is deployed to the edge cloud, which improves the real-time performance. In order to identify the action being executed, the end-to-end action recognition model proposed in this paper uses the Temporal Convolutional Neural Network (TCN) to extract local temporal features and a Gate Recurrent Unit (GRU) layer to extract global temporal features, which increases the accuracy of action fragment recognition. The result of action recognition is combined with the result of equipment target recognition based on the yolov3 model, and the classification rule is used to determine whether the current action is safe. Experiments show that the proposed method has better real-time performance, and the proposed action cognition is verified on the MSRAction Dataset, which improves the recognition accuracy of action segments. At the same time, the judgment results of unsafe actions also prove the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The author comments on the article, "Incentives for Accruing Costs and Efficiency in Regulated Monopolies Subject to ROE Constraint," concerning implicit compensation contracts for executives in the electric utility industry. He states that the paper assumes that regulation of the electric utility industry is cost-plus pricing, which differs from traditional views of electric utilities as a natural monopoly. He mentions that if capital costs are sticky then there isn't an incentive to pad capital costs. He states that the paper shows that overcapitalization and padding of operating costs would lead to increased managerial compensation.

Professor Katz has opened a neglected area of study in the pricing processes of the securities markets with what is, on the whole, a very estimable first effort. The rash of bond reclassifications in recent years found us with little or no knowledge of their effects, particularly with respect to yields. In this regard Katz's paper is a needed and welcome one. It may also be that, as his paper says, investigation will reveal something of bond market efficiency, though this is much more problematic and conjectural than the more narrowly framed problem of repercussions on yield of rating change. [ABSTRACT FROM AUTHOR]

Electric power information has garnered particular interest in conventional electrical power systems because it plays a crucial role in the electric power industry. Additionally, electric power communication networks ensure the overall power system's reliability and safety. The concepts and techniques used in digital twin (DT)‐based online tools for electric power communication networks are introduced in this paper. Moreover, online DT tools are proposed as daily learning and training resources for both new staff and communication commissioners, which can be used for the internet and the company intranets. Users can control DT and physical equipment simultaneously, and device status can be seen in real time. The design, modelling, control principles, data collection, and visual display of the DT tools are all covered in this study. To validate the applicability of the proposed framework, a case study for optical fibre damage and the recovery process in a typical communication interruption malfunction is investigated. The research presented in this paper has the potential to provide useful tools and new paradigms for designing online DT training and education systems. [ABSTRACT FROM AUTHOR]

Concerns about the cybersecurity and resilience of power systems have heightened in electric utilities and regulatory agencies over the past decade mainly because of the unpredictable target, location and scale of cyberattacks and the potential severity of consequences. The cybersecurity of pilot protection is paramount in bulk power systems considering their prevalence and the crucial role they play in protecting critical assets and preventing large system disturbances and major blackouts. This paper investigates the resilience of pilot protection using a co-simulation platform based on OPAL-RT simulator and Riverbed Modeler. It is demonstrated that software-defined networking for operational technology (OT SDN) significantly improves the resilience of pilot protection to false data injection (FDI) attacks compared to traditional networks. Moreover, the resilience of OT SDN based pilot protection to denial of service (DoS) attack against the SDN controller is investigated both in the proactive and reactive modes of operation. The simulation results verified the resilience of OT SDN controller in OT SDN based pilot protection to DoS attacks in the proactive mode of operation. [ABSTRACT FROM AUTHOR]

One of the main objectives of electric power utilities keeping up a continuous and adequate power supply to the customers at a sensible cost. This paper contributes to the solution of the reliability and quality assessment problems in power systems, using the (N-2) outage contingency scenario to evaluate power system's reliability and quality levels. Therefore, the methodology presented in this paper is based on the integration of reliability measures, quality indices, and contingency analysis. While reliability formulas have traditionally been applied to small and illustrative power systems, large-scale reliability and quality assessment go far beyond direct implementation of formulas. Systems with hundreds of buses and tens of complex stations can only be analyzed using advanced and numerically effective large-scale algorithms for reliability and quality assessment as demonstrated in this paper. [ABSTRACT FROM AUTHOR]