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High charging loads associated with fleets of commercial electric vehicles (EVs) are expected to significantly stress electric power distribution networks, leading to high costs seen by fleet operators. To address these challenges, this report presents a highly flexible smart charging (SC) algorithm for managing EV fleets that arrive and depart from a common depot on a schedule. The algorithm features (i) primary consideration of multiple fleet operator preferences (e.g. minimizing cost, using carbon-free energy), (ii) secondary consideration of grid impact that leverages the existence of multiple optimal (or near-optimal) ways to satisfy fleet operator preferences, and (iii) automatic detection and handling of infeasibility due to large energy demands (characteristic of fleet charging). Provided in this document are two numerical impact assessment studies in which the SC algorithm is shown to be superior to conventional rapid charging, and conventional ‘smart’ charging solutions on the market. These studies utilize a set of synthetic, but realistic fleet charging requirements, a physics-based model of a real feeder and one year of real, hourly load data for that feeder. The first numerical study shows that the proposed SC algorithm can lead to significant (up to 44%, but scenario-dependent) reductions in a fleet operator’s annual electricity bill. The second numerical study shows that significant transformer overloading and voltage drop issues can be associated with conventional fleet charging methods, and that the proposed SC algorithm eliminates these issues, thereby enabling higher EV penetration levels and offsetting infrastructure upgrades. View the NCST Project Webpage

The paper was the first to analyze the possibility of using small fuel cells to power automation devices, analyze their disadvantages and advantages, and their demand in modern electric power industry, taking into account the processes characteristic of low-voltage control circuits. The possibilities of using an economical current source as a current source for emergency power supply of automated control system devices are considered, which can create an effective commercial basis for the implementation of this solution in an industrial production area in conditions of possible power outages (Far North, Far East, conditions of remoteness of the central power supply), An analysis of the areas of application of fuel cells was carried out. The technical characteristics of elements for powering automation devices have been determined, and chemical and electrical calculations of the fuel cell under study have been carried out. [ABSTRACT FROM AUTHOR]

The vehicle-to-grid (V2G) service enables electric vehicles (EVs) to discharge battery power to the utility grid for power system stabilization. This study proposes a V2G acceptance model by integrating the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) with two additional constructs: perceived risks of V2G technology and attitude toward V2G service. The model aims to understand individuals' acceptance of V2G services in Taiwan, where the technology is still in the planning phase. Using 365 survey samples (250 EV users and 115 non-EV users) analysed using the structural equation modeling (SEM) method, results show that attitude and price value positively affect V2G usage intention while perceived risks of V2G technology negatively do. Performance expectancy and effort expectancy of V2G services positively influence attitudes. However, the effects of both facilitating conditions of V2G services and social influence on V2G usage intention are found insignificant. Discussions and implications support pilot initiatives for performance evaluation and marketing development, especially for the government, electric bus operators, and aggregators. • The initial research output, utilising an integrated UTAUT2 model, explores the intention to use V2G services in Taiwan. • Attitude and price value positively impact the intention to use V2G services. • Perceived risks negatively affect the intention to use V2G services. • Performance expectancy and effort expectancy of V2G services positively influence attitudes. • Facilitating conditions and social influence have an insignificant impact on the intention to use V2G services. [ABSTRACT FROM AUTHOR]

In this paper, we present a study on forecasting hourly electricity spot prices for India. Forecasting electricity prices is challenging for its special characteristics. Based on hourly data covering several years, ARMAX models are estimated for forecasting for individual regions. Along with ARMA, intra-day, inter-day and hourly variations and seasonalities on weekdays, holidays and festive days are incorporated. Combinations of static and dynamic forecasts are also examined. The results indicate that prices for Sundays and holidays differ significantly from weekdays; also, a clear region-specific difference between prices in winter and summer months is observed. Price behaviour in the Southern region is different from other regions. The fitted models perform well for forecasting horizons of hourly prices of up to five days. The fitted ARMAX models have an easily implementable simple structure. Such forecasts provide valuable insights for policymakers and market participants, as they incorporate several features of the Indian electricity market, including various dimensions of seasonality of demand. JEL Classifications : C53, Q47, C51 [ABSTRACT FROM AUTHOR]

The aim of this article is to analyze theoretically whether changes in an access charge pricing policy can promote green electricity use. The model considers two hypothetical firms: One firm offers green electricity product, and the other firm provides conventional electricity. Each firm pays an access charge to the natural monopoly in order to have access to the transportation network. Through using a vertical differentiation model, we try to analyze the link between the access charges paid by the two firms and the qualities offered. We also attempt to explain why the consumers do not often choose green electricity. Finally, we suggest a new policy that can lead the consumers to choose the green electricity product instead of conventional electricity. Our model shows that changes in the access charge pricing policies can constitute a relevant component in the reforms of the electricity sector and can have impacts on the consumption of the green electricity. A differentiating access charge policy could allow a better penetration of the green electricity product and an increase in the social welfare when consumers have greater preferences in environmental qualities. Otherwise, their preservation at a uniform level is desirable in order to maintain the competition. JEL Classifications : B21, C72, L13, Q21, P28 [ABSTRACT FROM AUTHOR]

The article reveals the specifics of criminal liability for intentional destruction or damage to someone else's property provided for in Article 194 of the Criminal Code of Ukraine. The problematic issues of the composition of the criminal offense of intentional destruction or damage to someone else's property are analyzed, the difference between this composition and the intentional destruction or damage to property that has special features (objects of the electric power industry, nature reserve fund, archaeological heritage, weapons, military supplies, official documents, military property, etc.). Intentional destruction or damage to someone else's property, which has special features to be qualified according to the special norms provided for by articles: 113, 1941, 252, 258, 292, 298, 338, 357, 360, 378, 399, 411, 437, 433, 438 of the Criminal Code, is proposed. of Ukraine. On the basis of the analysis of judicial practice, it was proposed that in the case of using an explosive device, for the intentional destruction or damage of someone else's property, such actions of the perpetrator must be additionally qualified under Part 1 of Art. 263 of the Criminal Code of Ukraine. The imperfection of the provisions of the current criminal legislation in terms of the formulation of the qualifying signs of intentional destruction or damage to property was revealed. It has been established that the current legislation of Ukraine on criminal responsibility does not include the concept of a generally dangerous method, arson, explosion, the content of the death of people, serious consequences is not disclosed. The concept of a generally dangerous method and its varieties is proposed. The concept of a generally dangerous method and its varieties is proposed. A generally dangerous method of intentional destruction or damage to property is a method that causes property, non-property, physical damage to an individual or legal entity or creates a real danger of causing such damage to two or more people. In order to avoid unnecessary differentiation of criminal law norms, it is proposed to exclude from the Criminal Code of Ukraine articles 347, 3471, 352, and part 2 of Art. 194 of the Criminal Code of Ukraine to add the sign of intentional destruction or damage to someone else's property in connection with the person's performance of official powers or professional duties. [ABSTRACT FROM AUTHOR]

The text discusses the impact of electric vehicles (EVs) on the power grid and the potential for Vehicle-to-Grid (V2G) services to mitigate these effects. It highlights the need for incentives to encourage user participation in V2G services, categorizing users based on their attitudes towards V2G participation. The document emphasizes the importance of addressing technical challenges and developing flexible, reliable, and scalable computing architectures to coordinate EV charging and grid requirements. Additionally, it suggests strategies to stimulate user participation, such as emphasizing sustainability, customization, simplicity, and developing smart charging algorithms for different user groups. [Extracted from the article]

Introduction. Recently, photovoltaic (PV) systems are increasingly favored for converting solar energy into electricity. PV power systems have successfully evolved from small, standalone installations to large-scale, grid-connected systems. When the nonlinear loads are connected to a grid-tied PV system, the power quality can deteriorate due to the active power supplied by the PV array, there’s a noticeable decline in the quality of power delivered to consumers. Its combination with the shunt active power filter (SAPF) enhances system efficiency. Consequently, this integrated system is adept at not only powering local loads but also at compensating for reactive power and filtering out harmonic currents from the main grid. The novelty of the work describes how an operation of a small scale PV system connected to the low voltage distribution system, and nonlinear load can be achieved, the investigation aims to analyze the system’s behavior and elucidate the advantages of employing various control algorithms. These proposed algorithms are designed to ensure a unity power factor for the utility grid while prioritizing high convergence speed and robustness against load power fluctuations across different levels of solar irradiation affecting the PV modules. The purpose of this work is to enhance the dynamic performance of the SAPF by cooperatively using a self-tuning filter (STF) based instantaneous active and reactive power method (PQ) with a novel predictive current control, enhance the system resilience, ensure optimal management of the total active power between the PV system, the electrical network and the non-linear load by integrating the functionalities of the SAPF under different levels of solar irradiation and maintain the DC-link capacitor voltage constant. Methods. A novel predictive current controller is designed to generate the switching signals piloted the three phase source voltage inverter, also a novel algorithm of instantaneous active and reactive power is developed, based on STF, to extract accurately the harmonic reference under non ideal grid voltage, also the perturb and observe algorithm is used to extract, under step change of solar irradiation, the maximum power point tracking of the PV module and the PI controller is used to maintain constant the DC-link capacitor voltage of the SAPF. Results. The efficacy of the proposed system is primarily centered on the grid side, and the performance evaluation of the control system is conducted using the STF based PQ algorithm and predictive current control. In addition, comprehensive testing encompasses all modes of operation, including scenarios involving distorted voltage sources, step changes in solar radiation, and variations in nonlinear loads. Results highlight superior performance in both transient and stable states, affirming the robustness and effectiveness of the proposed controllers. Practical value. The total harmonic distortion value of the grid current for all tests respects the IEEE Standard 519-1992. [ABSTRACT FROM AUTHOR]

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Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable. [ABSTRACT FROM AUTHOR]

In this paper we study optimal trading strategies in a financial market in which stock returns depend on a hidden Gaussian mean reverting drift process. Investors obtain information on that drift by observing stock returns. Moreover, expert opinions in the form of signals about the current state of the drift arriving at fixed and known dates are included in the analysis. Drift estimates are based on Kalman filter techniques. They are used to transform a power utility maximization problem under partial information into an optimization problem under full information where the state variable is the filter of the drift. The dynamic programming equation for this problem is studied and closed-form solutions for the value function and the optimal trading strategy of an investor are derived. They allow to quantify the monetary value of information delivered by the expert opinions. We illustrate our theoretical findings by results of extensive numerical experiments. [ABSTRACT FROM AUTHOR]

With increasing interest in integrating solar power into the utility grid, multilevel inverters are gaining much more attention for medium- and high-power applications due to their high-quality waveform, low voltage stress across active components, and low total harmonic distortion in output voltage. However, to achieve these benefits, a large number of active and passive components are required. A transformer is also required to provide galvanic isolation, which increases its size and weight and reduces its power density and efficiency. In order to overcome the disadvantages posed by transformer-based inverters, research is being conducted on the transformerless topology of multilevel inverters. The first aim of this review article is to summarize traditional transformerless multilevel inverters (TMLIs) considering both single- and three-phase topologies. Secondly, the main aim of this article is to provide a detailed overview of the hybrid topologies of TMLIs that employ fewer components for photovoltaic applications. In addition, this study compares traditional and hybrid single-/three-phase topologies in terms of component count and performance factors, which will be useful to researchers. [ABSTRACT FROM AUTHOR]

Environmental pollution and the rapid decline in the availability of fossil fuels are the major concerns of the electric power industry in recent years. Effective utilization of electrical energy is the need of the hour in building the nation towards sustainable development. This study presents the energy consumption analysis and management performed in a diary firm located in Tamil Nadu, India. Initially, the energy usage of the firm is estimated based on its electricity bills, meter readings, and survey followed by detailed energy consumption analysis and evaluation. The study employs guidelines provided by the Indian Bureau of Energy Efficiency for energy management. The detailed analysis performed finds scopes for improvement in efficient energy usage. Numerical analysis shows that implementation of proposed action plans that include installation of automatic power factor correction, replacement of overheating equipment, and replacement of lighting systems with energy-efficient lighting would save annual energy of 123.96 kWh which in turn results in the reduction of annual CO2 emission by 117.76 tonnes. Furthermore, this paper also proposes a novel optimal process scheduling which optimally schedules different processes in the firm without affecting productivity. This makes an additional savings of INR 4,23,360 per annum in the energy cost by minimizing the maximum demand of the firm with zero investment. A comparison of the proposed approach with existing methodologies available in the literature shows the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

One of the crucial issues for power grids in strengthening the urbanization around the world is imbalance between supply and demand, which leads the users to consume electricity in an anomalous manner without paying for it. Electricity theft plays a pivotal role in cutting down on the electricity bills. The existing data-oriented approaches for electricity theft detection (ETD) in the smart cities have limited ability to handle noisy high-dimensional data and features' associations. These limitations raise the misclassification rate, which makes some of the approaches unacceptable for electric utilities. A new twofold end-to-end methodology is proposed for ETD. In the first fold, it groups the similar electricity consumption (EC) cases through grey wolf optimization (GWO)-based clustering mechanism; clustering by fast search and find of density peaks (CFSFDP), we named it GC. In the second fold, a new relational stacked denoising autoencoder (RSDAE)-based semi-supervised generative adversarial network (GAN), termed as RGAN, is used for ETD. The combined methodology is named as GC-RGAN. In the methodology, RSDAE acts as both feature extraction technique and generator sub-model of the proposed RGAN. The proposed methodology utilizes the advantages of clustering, adversarial learning and semi-supervised EC data. Besides, to validate the effectiveness of the proposed solution, extensive simulations are performed using smart meter data. Simulation results validate the excellent ETD performance of the proposed GC-RGAN against existing ETD schemes, such as random forest and semi-supervised support vector machine. In comparison, GC-RGAN covers the ETD score of 98% that shows its suitability for real-world scenarios. The proposed solution has extraordinary performance for ETD as compared to traditional solutions, which shows its superiority and usefulness for real-world applications. [ABSTRACT FROM AUTHOR]

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today's computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system. [ABSTRACT FROM AUTHOR]

Flow ultrasound generators are devices that emit high-frequency sound waves due to the hydrodynamic instability of the supersonic flow. In the electric power industry, such generators are used in arc quenching systems of high-voltage gas blast circuit breakers. The design of the flow ultrasound generator includes a nozzle and a hollow cylindrical resonator. Self-oscillations of the sealing waves occur when a supersonic gas jet collides with a resonator. This article is devoted to the analysis of the arrester operation, which has design features of flow ultrasound generators. The paper includes both experimental investigations of the proposed spark gap arrester design and mathematical modeling of the processes occurring in it. A description of the methods used is presented, and a comparison of the time dependences of currents and voltages obtained as a result of experimental studies and as a result of calculations is performed in the next section. The calculation results include oscillograms of the voltage and current, and the plasma temperature distribution in the arrester chamber at different moments of time. The investigations show that the presence of a nozzle and resonator leads to an intensification of the gas dynamic effect on the electric arc. [ABSTRACT FROM AUTHOR]

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system's resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries. [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]

Growing uncertainty in supply and demand in power systems causes significant challenges in maintaining supply reliability at affordable costs. Power grids are expected to undergo substantial transformations to address these challenges with upgrades and integration of emerging smart technologies that require significant investment costs. A value-based reliability assessment of these grid technologies is necessary to justify the worth of these investments. A key parameter required in such an assessment is the cost of power interruptions originating from transmission system failures. The interruption cost data available in published reports and past surveys relate to generation inadequacy since generation facilities comprise the most capital-intensive investment of an electric utility. Customer interruptions due to a lack of generation mainly occur due to generation failures during the peak demand period, whereas interruptions due to transmission component failures can occur at other periods with specific probabilities. This paper presents a methodology to estimate the cost of outages originating from transmission asset failures, which proposes a sector period model for each customer sector to obtain associated demand-normalized interruption costs. The proposed method can also be used to decide investment in grid resiliency enhancement against extreme weather that mainly impacts the grid network facilities. [ABSTRACT FROM AUTHOR]

Major power outages have risen over the last two decades, largely due to more extreme weather conditions. However, there is a lack of knowledge on the distribution of power outages and its relationship to social vulnerability and co-occurring hazards. We examined the associations between localized outages and social vulnerability factors (demographic characteristics), controlling for environmental factors (weather), in Washington State between 2018–2021. We additionally analyzed the validity of PowerOutage.us data compared to federal datasets. The population included 27 counties served by 14 electric utilities. We developed a continuous measure of daily outage burden using PowerOutage.us data and operationalized social vulnerability using four factors: poverty level, unemployment, disability, and limited English proficiency. We applied zero-altered lognormal generalized additive mixed-effects models to characterize the relationship between social vulnerability and daily power outage burden, controlling for daily minimum temperature, maximum wind speed, and precipitation, from 2018 to 2021 in Washington State. We found that social vulnerability factors have non-linear relationships with outages. Wind and precipitation are consistent drivers of outage occurrence and duration. There are seasonal effects that vary by county-utility area. Both PowerOutage.us and federal datasets have missing and inaccurate outage data. This is the first study evaluating differential exposure to localized outages as related to social vulnerability that has accounted for weather and temporal correlation. There is a lack of transparency into power outage distribution for those most vulnerable to climate impacts, despite known contributions by electric utilities to climate change. For effective public health surveillance of power outages and transparency, outage data should be made available at finer spatial resolution and temporal scales and/or utilities should be required to report differential exposure to power outages for socially vulnerable populations. [ABSTRACT FROM AUTHOR]

A basic principle of virtually every regulation to improve grid reliability and reduce power sector emissions is that market participants change their behavior when regulations make it more expensive to engage in socially harmful activities. To give a concrete example, a carbon tax assumes that increasing the costs of emitting carbon dioxide will lead market participants to reduce energy consumption and switch to less carbon-intensive resources. But this assumption does not apply to large parts of the electricity industry, where investor-owned utilities are often able to pass the costs of climate and reliability rules on to captive ratepayers. The underlying problem, I argue, is that the U.S. legal system outsources investment and market design decisions to private firms that will be financially harmed if state and federal regulators pursue deep decarbonization or take ambitious steps to improve grid reliability. At the state level, this occurs because utilities propose new infrastructure in integrated resource plans that authorize cost recovery from captive customers. At the federal level, this occurs because the Federal Power Act gives incumbent utilities "filing rights" that authorize them to submit, or "file," regulations and rates related to their assets. Utilities use their filing rights both to propose favorable market rules and to design governance structures that allow them to control the multimember organizations that plan grid infrastructure and ensure resource adequacy. Given that regulatory environment, it is little surprise that incumbent utilities design electricity market rules that counteract climate and reliability regulations. These observations underscore that structural changes such as full corporate unbundling, market liberalization, and aggressive governance reforms are needed to make climate and reliability policies more effective and easier to administer. [ABSTRACT FROM AUTHOR]

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]

Problem definition: More than 99% of the new power generation capacity to be installed in the United States from 2023 to 2050 will be powered by wind, solar, and natural gas. Additionally, large-scale battery systems are planned to support power systems. It is paramount for policymakers and electric utilities to deepen the understanding of the operational and investment relations among renewable, flexible (natural gas-powered), and storage capacities. In this paper, we optimize both the joint operations and investment mix of these three types of resources, examining whether they act as investment substitutes or complements. Methodology/results: Using stochastic control theory, we identify and prove the structure of the optimal storage control policy, from which we determine various pairs of charging and discharging operations. We find that whether storage complements or substitutes other resources hinges on the operational pairs involved and whether executing these pairs is constrained by charging or discharging. Through extensive numerical analysis using data from a Florida utility, government agencies, and industry reports, we demonstrate how storage operations drive the investment relations among renewable, flexible, and storage capacities. Managerial implications: Storage and renewables substitute each other in meeting peak demand; storage complements renewables by storing surplus renewable output; renewables complement storage by compressing peak periods, facilitating peak shaving and displacement of flexible capacity. These substitution and complementary effects often coexist, and the dominant effect can alternate as costs change. A thorough understanding of these relations at both operational and investment levels empowers decision makers to optimize energy infrastructure investments and operations, thereby unlocking their full potential. Funding: This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute. This research was also supported by Kelley School of Business, Indiana University, and Haslam College of Business, University of Tennessee. O. Q. Wu thanks Grant Thornton for their generous support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0068. [ABSTRACT FROM AUTHOR]

I examine developments in the application of performance-based regulation (PBR) to electricity distribution and transmission in the United States. Applications of comprehensive PBR to electricity distribution had been slow to diffuse in the U.S. prior to roughly 2000. PBR mechanisms are now being applied more frequently to electricity distribution, which reflects the changing structure of the electric power industry and the increasing obligations that are being placed on electric distribution companies. The new obligations are a consequence primarily of aggressive targets for decarbonizing the electricity sector in nearly half the states and the goal of using "clean" electricity to electrify transportation, buildings, and other sectors. PBR should be viewed as a set of "building blocks" that can be adopted in various combinations and should recognize that PBR and traditional cost-of-service regulation (COSR) are properly viewed as complements rather than substitutes. Recent reforms in the regulation of distribution companies in Great Britain—"RIIO"—have been influential in the U.S. The main reforms contained in RIIO are discussed. There has been essentially no application of PBR by the Federal Energy Regulatory Commission (FERC) to owners of transmission assets or to independent transmission operators. FERC has applied targeted incentives to encourage investment in transmission facilities and membership in independent system operator organizations. However, the regulation of transmission rates relies primarily on COSR in the form of formula rates and has poor incentive properties. Regulation of independent system operators is a challenge because they are non-profit organizations with no equity to put at risk. Reforms here are suggested. [ABSTRACT FROM AUTHOR]