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Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in the process of diagnosing the health of power transformer insulation, which involves the science of analysing the gases dissolved in power transformer oil for effective identification of faults. An accurate diagnosis of incipient faults is favourable to sustainable development and necessary to maintain a reliable supply of electricity. The methods presented for fault diagnosis in mineral-oil-immersed power transformers are divided into analytical and graphical methods and have been found to be simple, economical and effective. After describing the methods, both their strengths and weaknesses were identified, and over the years, the methods were complemented to provide highly accurate information, validated by field inspections. This paper focuses on practical information and applications to manage maintenance based on accurate and up-to-date data. The contents of this paper will be of particular use to engineers who manufacture, monitor and/or use high-power transformers in the energy sector, as well as to undergraduate, master's and PhD students interested in such applications. [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]

The ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings. [ABSTRACT FROM AUTHOR]

During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered. [ABSTRACT FROM AUTHOR]

Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC, the specifications and existing studies fall short in offering guidance on their efficient usage. This paper presents the first comprehensive system-level evaluation of mURLLC, leveraging insights from 3GPP specifications. It points out (i) how mURLLC differs from traditional multicast broadband wireless communications, and (ii) which approaches to provide mURLLC require changing the paradigm compared with the existing solutions. Finally, the paper provides recommendations on how to satisfy strict mURLLC requirements efficiently, i.e., with low channel resource consumption, which increases the capacity of 5G systems for mURLLC. Simulation results show that proper configuration of multicast mechanisms and the corresponding algorithms for mURLLC traffic can reduce resource consumption up to three times compared to the baseline solutions proposed for broadband multicast traffic, which significantly increases the system capacity. [ABSTRACT FROM AUTHOR]

Line loss refers to the electrical energy that is dissipated as heat during the transmission and distribution of electricity through power lines. However, unusual causes, such as grid topology mismatch and communication failure, can cause abnormal line loss. Efficient abnormal line loss detection contributes not only to minimizing energy wastage and reducing carbon emissions but also to maintaining the stability and reliability of the entire distribution network. In actual situations, the cause of abnormal line loss is not labeled due to the expensive labor cost. This paper proposes a hierarchical abnormal line loss identification and category classification model, considering the unlabeled and unbalanced sample problem. First, an abnormal line loss identification model-based random forest is established to detect whether the line loss is abnormal. Then, an abnormal line loss category classification model is developed with semi-supervised learning for line loss abnormal category classification, considering the unlabeled samples. The real dataset in China is utilized to validate the performance of the proposed model. Its reliability implies the potential to be applied to real-world scenarios to improve the management level and safety of the power grid. [ABSTRACT FROM AUTHOR]

The aim of this study was to study the security problems faced by an Radio Frequency Identification (RFID) system in the whole process management of intelligent power distribution system equipment. Based on information security risks, the defects are analyzed, and how to protect distributed energy stations from information security threats in the energy Internet environment is discussed. This paper analyzes the new business communication requirements of distribution communication network and the security risks. The results are as follows: in the classification accuracy experiment, the accuracy of all kinds of classification is obtained through three algorithms [DAG-SVM based on minimum coupling degree, DAG-SVM based on maximum separability, and double-decision hyperplane (DDH)]. Among them, the accuracy of the optimized DDH algorithm in this paper is 98.65%, which is the highest of the three, and the time is the shortest, saving about 40%. A security protocol is proposed which has good protection against several attacks. Deep neural network was used to mine the power terminal attack by means of feature engineering and data enhancement, and the network-level security monitoring of power terminals is realized. The feasibility of the proposed method is verified by building a physical simulation platform, and the experimental results show that the proposed strategy can improve the security of power terminals. [ABSTRACT FROM AUTHOR]

Synchronous generator-based power stations, with their inherent inertia, can maintain frequency stability during sudden load switching, while distributed generating station-driven microgrids suffer from a lack of natural inertia. Cascaded power, voltage, and current controllers are a widespread control strategy used to regulate the power output of distributed generating stations to maintain frequency and voltage within stable limits. Virtual synchronous generator (VSG) control for the power controller is used as a potential solution to emulate inertia. To derive maximum benefit from VSG, proper tuning of its multiple parameters is required. In this direction, earlier works proposed the equivalence between the droop and VSG schemes, which suggested that the droop coefficient value could be directly used in the design of VSG. As an improvement to these conventional works, the proposed work in this paper identifies that VSG delivers a better response when an equalizing constant is used to adjust the droop coefficient value than using it directly. This paper proposes implementing the VSG with an equalizing constant as a new design parameter. A description of designing the parameters of this improved VSG considering the equalizing constant is also discussed in this paper. The performance of the conventional VSG and the proposed improved VSG are compared. From the results, it is observed that, at load switching, the output frequency of the proposed method in all test cases has settled at less than 3 s, while the conventional method took a maximum of 6 s in critical cases. Further, the output frequency's maximum peak with the proposed method is 3 Hz less than the conventional method. These, along with other metrics, validate the importance of the proposed improved VSG-based control scheme for the enhancement of transient responses in microgrids. [ABSTRACT FROM AUTHOR]

In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a new comprehensive load restoration (LR) method for electrical distribution networks. Since two main technologies of switching equipment are there in the modern distribution networks, namely manual switches (MSs) and remote‐controlled switches (RCSs), this article has benefited from this concept effectively. A two‐stage algorithm that provides the system operators with the ability to recover part of the loads in the shortest possible time by RCSs is proposed. After this step, the remaining loads will be restored by a combination of MSs and RCSs. The other strength of this algorithm is to provide accurate and practical solutions so that the sequence of switching actions is clearly defined. Also, using an innovative index called expected weighted energy not supplied as the objective function of the main problem will ensure the operators recover the maximum amount of load in the shortest time possible. This novel method was applied on a sample standard IEEE distribution test network. The simulation results proved the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

Transmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing popularity, machine learning models are used in fault detection and classification, whose performances can be severely affected by cyber‐attacks due to their data dependency, posing a critical concern. Hence, this paper introduces false data injection attacks to address the vulnerability of machine learning‐based fault detectors and classifiers. A comparative study of 9 detection models and 6 classification models under normal conditions and during a combination of two models of false data injection attacks is presented to evaluate the severity of cyber‐attacks. Experimental results show that highly accurate models in normal conditions are more susceptible to cyber‐attacks, with up to 69% and 28% degradations in accuracy for fault detectors and classifiers, respectively. Furthermore, the detection models are found to be more vulnerable to cyber‐attacks than the classification models. With no robust detectors and classifiers being found, this work addresses the importance of developing attack‐resilient fault detection and classification schemes considering their academic and industrial significance. [ABSTRACT FROM AUTHOR]

This paper explores the complex interactions that exist between national ancillary service markets and local flexibility in Türkiye's changing power landscape. The significance of grid flexibility is escalating, especially with the country's transition from a predictable energy generation model to one predominantly fueled by intermittent renewable sources, coupled with the imminent integration of nuclear power. Ancillary services, encompassing functions such as frequency control, voltage regulation, and system restart, play a pivotal role in upholding grid stability. The study particularly underscores the significance of frequencyrelated ancillary services in preserving grid equilibrium. Moreover, it emphasizes how important electricity distribution businesses are as major players in improving grid stability. These businesses can actively participate in ancillary service markets and support grid stability by utilizing local flexibility solutions like distributed generation, energy storage, and demand response. This benefits the grid as well as consumers. The central objective of the study is to explore the synergies between local flexibility solutions and national ancillary markets. A case study is employed to evaluate the potential benefits and profitability associated with the integration of these components for an electrical distribution company. A combination solution utilizing solar panels, battery storage systems, electric vehicle charging infrastructure, and a natural gas turbine within the gearbox company's network is the subject of a case study that is presented. The concept of local flexibility and its mutually beneficial relationship with the national auxiliary service markets are also clarified in the study. The findings demonstrate the potential profitability and advantages of battery storage for the national energy grid. A substantial increase in prices for Frequency Restoration Reserve is imperative to render the natural gas turbine economically sustainable. [ABSTRACT FROM AUTHOR]

This paper investigates outage management in electricity distribution networks through the application of artificial intelligence techniques. The core of the system utilizes a diverse dataset compiled from outage management system records, weather forecasts, and geographical data to predict potential electricity outages. The data is rigorously analyzed to determine correlations between various weather conditions and outage occurrences, with particular emphasis on the impact of wind speed and storm conditions. The predictive model, a cornerstone of this research, employs a hybrid artificial intelligence algorithm that integrates outputs from convolutional neural networks, recursive neural networks, and extreme gradient boosting. The predictions are further refined using a feedforward neural network and distributed to specific districts based on historical data trends. Comparative analysis against a naive model based on historical averages highlights the superior performance of the hybrid model, showcasing its reduced error rates and enhanced predictive accuracy. This decision support system not only provides reliable outage predictions but also facilitates more effective management strategies, thus improving operational efficiencies and customer service in electricity distribution. The findings underscore the potential of advanced analytics in transforming utility management and pave the way for further innovations in smart grid technology and outage prevention strategies. [ABSTRACT FROM AUTHOR]

The significant growth in demand for electricity has led to increasing congestion on distribution networks. The challenge is twofold: it is needed to expand and modernize our grid to meet this increased demand but also to implement smart grid technologies to improve the efficiency and reliability of electricity distribution. In order to mitigate these congestions, novel approaches by using flexibility sources such as battery energy storage can be used. This involves the use of battery storage systems to absorb excess energy at times of low demand and release it at peak times, effectively balancing the load and reducing the stress on the grid. In this paper, two optimal power flow formulations are discussed: the branch flow model (non-convex) and the relaxed bus injection model (convex). These formulations determine the optimal operation of the flexibility sources, i.e., battery energy storage, with the objective of minimizing power losses while avoiding congestions. Furthermore, a comparison of the performance of these two formulations is performed, analyzing the objective function results and the flexibility operation. For this purpose, a real Spanish distribution network with its corresponding load data for seven days has been used. [ABSTRACT FROM AUTHOR]

The expansion of distributed renewable resources, together with increased demand from the electrification of transport and heating sectors, impacts distribution networks significantly. Additionally, the emergence of non-programmable and intermittent generators is set to diminish the dominance of traditional rotating and programmable generation, thereby affecting the overall stability of the system. Nevertheless, the flexibility offered by distributed resources has the potential to alleviate the necessity for network reinforcement and contribute to system stability at competitive costs. Local flexibility procurement should be rooted in local markets, serving as mechanisms to address distribution congestion and coordinate the provision of flexibility for transmission network services. The multitude of existing systems and the interdependence of flexibility services have given rise to diverse solutions, still undergoing experimentation in various countries. This paper aims to scrutinize key projects that have established local flexibility markets, delineating their fundamental characteristics, the most common solutions, identifying prevalent barriers and suggesting potential future improvements. The investigation focuses on the most uncertain aspects of local markets: possible TSO-DSO coordination schemes, the time horizon for the acquisition of services and the baseline definition methodologies. [ABSTRACT FROM AUTHOR]

It is widely recognised that improving the visibility and controllability of distributed energy resources (DERs) within electricity distribution networks will have significant benefits, particularly for the management of low-voltage (LV) and medium-voltage (MV) networks. Much work within the electricity distribution industry is currently focused on improving the visibility of DERs on LV networks. From a control-theoretic perspective, this enables closing the loop between the DER and the control room and enables a shift towards utilising data-driven model-based control strategies for DERs. The result is a system-wide performance that is closer to the theoretical optimal. In the Australian context, several jurisdictions are trialling techniques such as dynamic operating envelopes to enhance DER hosting capacity, using IEEE 2030.5-based architectures, with the implementation of distributed energy resource management (DERMS) systems at the enterprise level still quite limited. While there is significant activity focused on DER behaviour and control techniques by way of inverter grid codes and standards, the core issue of interoperability with distribution management systems (DMSs), market operators or participants, electric vehicles (EVs) or other DERs is still a work in progress. Importantly, this is also an impediment to realising distributed architectures for DER control in the grid. The unique characteristics of Australian distribution networks highlights several challenging problems for DER control and management. The objective of this paper is to provide a broad overview of DER control and management strategies in the Australian context, with an application focus on DER control in distribution network management. [ABSTRACT FROM AUTHOR]

A terminal for remote control of charging stations for electric vehicles (EV) powered by renewable energy has been presented in this paper. This terminal enables remote control of EV chargers, smart batteries, smart electricity meters, fiscal cash registers (FCR), as well as remote control of renewable energy sources and other devices within the station. This terminal also makes charging stations more accessible to electric vehicles users, to electricity distribution companies, to electricity suppliers, to tax administrations, and finally to users and owners of charging stations. Therefore, communication and control with all these devices and systems is integrated in one device. Realization of hardware and software of such a terminal has also been described in this paper. The net result of development and commercialization of terminals would encourage an increase in the use of electric vehicles powered by energy from renewable sources, which would cause a decrease in the level of air pollution and all negative effects it causes in the future. Different categories of this device are considered. Moreover, although it is a device with embedded software, a very advanced method was used, that is, a model-driven development method, which enables fast and more efficient development and maintenance of the device. The results of the application of this method to the terminal for remote control of fiscal cash registers are provided. They were compared with the results of the development of the terminal for remote control of smart meters without applying this method. A simulation of the development of the terminal for remote control of the station is also provided. The presented method can be used in the future for faster and better-quality development of embedded software. [ABSTRACT FROM AUTHOR]

Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP. [ABSTRACT FROM AUTHOR]

With the development of EVs (Electric Vehicles) and the rapidly developing policies on low carbon and environmental protection, electric power systems and natural gas systems become increasingly larger. Under these circumstances, the V2G (Vehicle-to-grid) and the coordinated operation of an integrated electricity–gas distribution system (IEGDS), considering CO2 emissions, can play a part together in the process of energy conservation. Firstly, the V2G model is discussed; this paper presents the cost differences between out-of-order and order for the car. Secondly, the IEGDS model presents coupling constraints of gas turbines and power-to-gas. Lastly, carbon emission is considered in this paper; a carbon capture plant (CCP) captures the CO2 burning by fossil fuel in the power generation process and stores it in a carbon storage tank. This paper also considers trading with the carbon market via a carbon storage warehouse. With the cooperation of various components, a comprehensive model considers the use of V2G to store power in the IEGDS system, with consideration of the carbon trade. Numerical experiments validate the effectiveness of the combination between V2G and IEGDS, considering carbon emissions and carbon trading. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system's modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery's efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation. [ABSTRACT FROM AUTHOR]

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization. [ABSTRACT FROM AUTHOR]

With the continuous development of renewable energy technologies, both domestically and internationally, the focus of energy research has gradually shifted towards renewable energy directions such as distributed photovoltaics and wind power. The penetration rate of renewable energy generation is constantly increasing, at the same time, the elements in the grid are becoming increasingly complex, and large-scale energy storage, as well as a variety of electricity loads such as electric vehicle charging piles and data centers are gradually appearing. Therefore, traditional distribution methods of the power grid are difficult to ensure the stable operation of the power system and cannot achieve efficient integration of renewable energy. Consequently, some scholars have proposed the concept of an energy internet. Compared to traditional power grids, the energy internet employs more comprehensive power electronics and communication technologies, enabling the interconnection of various new and traditional energy sources, and effectively integrating renewable energy. As the core device in the energy internet, the energy router plays a role in energy transformation and distribution, facilitating multi-information interconnection and multi-energy exchange within the energy internet. At the level of distribution network, the energy router can realize the efficient access of various forms of energy and the flexible control and management, which is of great significance for the optimal operation of distribution network. Against this backdrop, this paper reviews the development and current research status of energy routers, systematically analyzes the typical topologies and related control technologies of multi-port energy routers and summarizes and forecasts key issues and future development trends, aiming to provide thoughts and reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Introduction: In order to promote the consumption of renewable energy, reduce carbon emissions, and take into account the uncertainty of renewable energy output and load fluctuations in the new power system that can affect the normal operation of market mechanism, a two-stage low-carbon optimization scheduling method for power system that considers demand response under multiple uncertainties is proposed in this paper. Methods: Uncertain scene sets are generated through Latin hypercube sampling and heuristic synchronous backpropagation method is used to reduce scenes to obtain typical scenes and their probabilities. Then, a one-stage optimization model is established with the goal of maximizing energy efficiency and corresponding demand response strategies are obtained. Green certificate carbon trading joint mechanism model consisting of tiered green certificate trading and time-sharing tiered carbon trading are established, and the output of two-stage units are optimized with the goal of minimizing comprehensive operating costs. Result: The simulation results show that the carbon emissions are decreased by 251.57 tons, the consumption rate of renewable energy is increased by 8.64%, and the total costs are decreased by 124.0612 million yuan. Discussion: From this, it can be seen that the dual layer low-carbon optimization scheduling strategy for power system considering demand response under multiple uncertainties can effectively reduce the operating costs and carbon emissions of the system, while balancing the economic and environmental aspects of power system operation. [ABSTRACT FROM AUTHOR]

In this paper, the problem of sizing and placement of constant and switching capacitors in electrical distribution systems is modelled considering the load uncertainty. This model is formulated as a multicriteria mathematical problem. The risk of voltage violation is calculated, and the stability index is modelled using fuzzy logic and fuzzy equations. The instability risk is introduced as the deviation of our fuzzy-based stability index with respect to the stability margin. The capacitor placement objectives in our paper include: (i) minimizing investment and installation costs as well as loss cost; (ii) reducing the risk of voltage violation; and (iii) reducing the instability risk. The proposed mathematical model is solved using a multi-objective version of a genetic algorithm. The model is implemented on a distribution network, and the results of the experiment are discussed. The impacts of constant and switching capacitors are assessed separately and concurrently. Moreover, the impact of uncertainty on the multi-objectives is determined based on a sensitivity analysis. It is demonstrated that the more the uncertainty is, the higher the system cost, the voltage risk and the instability risk are. [ABSTRACT FROM AUTHOR]

A Reward and Penalty Scheme (RPS) is generally used for setting up service quality and it will bring about financial benefits for distribution companies due to customer reliability and the resulting demand. In this paper, an algorithm was presented for the optimal switch number and placement in distribution networks in the presence of RPS. The main objective of this paper is to improve the reliability and minimize the costs of Sectionalizing Switches (SS) and Tie Switches (TS) for a given regulatory period considering the acceptable financial risk. In this algorithm, uncertainty in the reliability appears as a financial risk. A genetic algorithm was adopted to solve the optimization problem. The numbers and locations of SS and TS were determined by considering the financial incentives of RPS, capital investment and annual operation, and maintenance costs. The performance of the proposed approach was assessed and illustrated within a real distribution network. The results showed the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

China's carbon peak strategy constrains industry's carbon footprint, limiting the total amount of electricity production from coal burning, rendering electricity a relatively scarce resource and affecting manufacturing enterprises' production and operation activities. To address these circumstances, this paper applies a non-cooperative game model to describe the electricity resource distribution and product production process under enterprises' competitive state, proposing an optimal electricity distribution analysis framework for manufacturing enterprises. The results of optimal electricity distribution for six subindustries in the 'three innovations' manufacturing industries (referring to a collection of economic activities to establish new industries, new formats and new business models) are analysed to examine the difference in electricity consumption efficiency among industries. The electricity quota of each industrial chain significantly differs before and after redistribution;therefore, the industrial differences of optimal electricity distribution are further explored from the perspective of industrial chains, revealing the supply–demand relationship of upstream, middle and downstream enterprises. Finally, corresponding policy suggestions and directions for further research are presented. [ABSTRACT FROM AUTHOR]

In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability of distribution systems with installed DG sources. This paper deals with a reliability analysis for both present and future medium voltage (MV) electric distribution system structures. The impact of DG technology used and energy source location on the power supply reliability has been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources as well as information and communications technology (ICT) components have been proposed. Main contribution of this paper are the results of performed calculations, which have been analyzed for specific system structures (two typical present network structures and two future network structures), using detailed information on DG types, their locations and power capacities, as well as distribution system automation applied (automatic stand-by switching on—ASS and automatic power restoration—APR). The reliability of the smart grid consisting of the distribution network and the coupled communications network was simulated and assessed. The observations and conclusions based on calculation results have been made. More detailed modeling and consideration of system automation of distribution grids with DG units coupled with the communication systems allows the design and application of more reliable MV network structures. [ABSTRACT FROM AUTHOR]

The DC/DC converters are commonly used in DC microgrids to achieve the desired performance of a system under different operating conditions. The dynamics of an isolated DC microgrid with solar photovoltaic panels, battery, and constant power load is a fractional nonlinear model; thus, the complex nonlinear method should be applied for controller design and stabilizing the system. In multi-input multi-output systems with non-linear elements, non-linear control methods should be used, but this paper presents a straightforward technique for designing decentralized controllers for the buck, boost, and buck-boost converters in an isolated DC microgrid to stabilize the system and implement Maximum Power Point Tracking (MPPT) control of the solar photovoltaic panels. The presented methods consider the nonlinear decentralized feedback linearizing control for the nonlinear system. Depending on the performance of the converters in the system, proper dynamic models are introduced for the converters, and decentralized controllers are determined. Various scenarios are simulated and compared to conventional methods. The results indicate that the settling time of the output variables and the overvoltage of the DC-link voltage are reduced by less than 30% compared to other techniques. [ABSTRACT FROM AUTHOR]

The aim of this paper is the optimal scheduling of an active distribution network at the feeder scale to achieve minimum operating costs. To achieve this goal, a two-stage stochastic programming approach is presented that consists of here-and-now and wait-and-see decisions. In order to handle the uncertain parameters in the model, with the Monte Carlo simulation approach, different scenarios were produced, and then the scenarios were reduced. As a result, the mathematical problem is formulated as a linear model programmed in GAMS and then resolved with CPLEX solver. The effectiveness of the proposed model is assured through simulation studies on the standard IEEE 33-bus test system. The operating costs were calculated under different network conditions and were compared among themselves and according to the availability of storage. Obtained operating cost results have indicated that there is 6.4% cost reduction in the case where storage is used under the same conditions, compared to the case where no storage is used. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DERs), such as photovoltaic (PV) sources, together with storage systems, such as battery energy storage systems (BESS), are increasingly present and necessary in our electricity distribution networks. Furthermore, the need for efficient use of energy from DERs, especially in developing countries and remote communities, must be addressed with the development of nanogrids (NGs), particularly DC NGs, and standalone PV systems with adequate control strategies. This paper investigates the control and dynamic operation of a standalone PV system. It consists mainly of three DC–DC power converters for the PV source interface, battery management, and load voltage control. A two-level modulation scheme is applied to each of these converters to switch them ON and OFF. A maximum power point tracking (MPPT) closed-loop voltage control system is implemented to make sure that the PV operates at optimum power regardless of the irradiance level or temperature, while battery voltage and load-side voltage control are also implemented to indirectly provide the required load power. The control of each of the converters is achieved by deriving their small-signal models using a state-space approach from which various control objectives are implemented. The DC-link is clamped by a BESS which acts as a backup source to provide power to the DC load in the absence of sufficient power from the PV panel. The dynamic operation of the whole system is enhanced by proposing a robust feedforward scheme that improves the response of the system in the presence of disturbances. The models are analyzed and implemented using PLECS, and numerical simulations are performed to validate the developed models and control schemes. [ABSTRACT FROM AUTHOR]

With the deregulation of electricity market in distribution systems, renewable distributed generations (RDG) are being invested in by third-party social capital, such as distributed generations operators (DGOs) and load aggregators (LAs). However, their arbitrary RDG investment and electricity trading behavior can bring great challenges to distribution system planning. In this paper, to reduce distribution system investment, a distribution system expansion planning model based on a bi-level Stackelberg game is proposed for the distribution system operator (DSO) to guide this social capital to make suitable RDG investment. In the proposed model, DSO is the leader, while DGOs and LAs are the followers. In the upper level, the DSO determines the expansion planning scheme including investments in substations and lines, and optimizes the variables provided for followers, such as RDG locations and contract prices. In the lower level, DGOs determine the RDG capacity and electricity trading strategy based on the RDG locations and contract prices, while LAs determine the RDG capacity, demand response and electricity trading strategy based on contract prices. The capacity information of the DRG is sent to the DSO for decision-making on expansion planning. To reduce the cost and risk of multiple agents, two long-term renewable energy contracts are introduced for the electricity trading. Conditional value-at-risk method is used to quantify the RDG investment risk of DGOs and LAs with different risk preferences. The effectiveness of the proposed model and method is verified by studies using the Portugal 54-bus system. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unprecedented impact on various aspects of our lives, including education, work dynamics, and social interactions. Dealing with the provision of building utilities in such circumstances has become a formidable challenge. During lockdowns, it becomes crucial to allocate resources strategically, giving priority to residential areas over commercial and financial districts based on population density. Identifying residential areas is of utmost importance not only for effective emergency response during natural disasters but also for ensuring fair distribution of electricity and gas when resources are scarce. However, accurately delineating residential zones is challenging due to the intricate nature of urban landscapes. This paper aims to discuss a comprehensive indicator that utilizes open-source intelligence and incorporates a multi-criteria decision-making framework to assess the likelihood of an area being residential. This indicator will greatly assist in optimizing resource allocation for power, gas, and water distribution. To demonstrate the effectiveness of the proposed approach, a case study conducted in Nicosia, Cyprus, is presented. [ABSTRACT FROM AUTHOR]

As the most promising alternative to internal combustion engines (ICEs), electric vehicles (EVs) have an excellent development outlook. The charging route scheduling of EVs can simultaneously affect traffic congestion in the transportation network (TN) and power flow distribution in the power distribution network (PDN). The research on TN and PDN coupling networks based on the static traffic flow model is relatively mature; however, it ignores that the traffic flow will spread across periods in a short scheduling period. In this paper, a semi‐dynamic traffic flow model is proposed to represent the dynamic propagation characteristics of EVs and ICEs flow. Furthermore, the cost of carbon emission and system operation are combined as the overall goal of system optimisation. Since the model has become a more complex non‐linear model, this paper proposes to combine the heuristic sequential boundary tightening and binary expansion method to linearise the model. The study compared four cases and found that a 20% penetration rate of EVs can reduce carbon emissions by 4.2% while reducing the system's total cost by 10%. Moreover, the impact of network congestion on the spatiotemporal distribution of traffic flow and power flow in the coupled network is alleviated. [ABSTRACT FROM AUTHOR]

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a non‐intrusive method for identifying the load state of a distribution network. The method focuses on continuously varying loads. By considering the load on‐off state switching points and the continuous features at on state, a deep convolutional method considering non‐local spatiotemporal features is proposed. The addition of an attention component to the convolutional network enhances the non‐local feature extraction capability of the convolutional network. Ultimately, the effectiveness of the method is demonstrated in an experimental setting. In addition, this paper demonstrates that the proposed method can effectively integrate switching point features as well as persistent features through neural network visualization techniques. [ABSTRACT FROM AUTHOR]

The quality of electricity is one of the most discussed issues in the regulatory context, structured by guidelines that allow the assessment of efficiency in electricity supply. Regarding electricity distribution systems, it is essential to highlight the aspects associated with the definition of continuity indicators to measure the service provided by the utilities. The parameters used to calculate the indicators are set by specific agencies in each country based on studies and their characteristics. With the growth in demand for electricity, debates associated with reducing greenhouse gas emissions in the atmosphere, and the increase in Distributed Energy Resources (DERs) connected to the grid, a new trend related to the energy supply is verified. Given these new scenarios, this paper presents a literature review in the regulatory field of existing distribution systems in South American countries. The research proposes to analyze, compare, and expose the current models to evaluate their methods and indicators, including a complete case study in Brazil. This paper seeks to illustrate and point out perspectives, challenges, and future perspectives through the collected and analyzed data. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become one of the most significant issues in nonlinear optimization research. OPF generally improves the performance of electric power distribution, transmission, and production within the constraints of the control system. It is the purpose of an OPF to determine the most optimal way to run a power system. For the power system, OPFs can be created with a variety of financial and technical objectives. Based on these findings, this paper proposes the peafowl optimization algorithm (POA). A powerful meta-heuristic optimization algorithm inspired by collective foraging activities among peafowl swarms. By balancing local exploitation with worldwide exploration, the OPF is able to strike a balance between exploration and exploitation. In order to solve optimization problems involving OPF, using the standard IEEE 14-bus and 57-bus electrical network, a POA has been employed to find the optimal values of the control variables. Further, there are five study cases, namely, reducing fuel costs, real energy losses, voltage skew, fuel cost as well as reducing energy loss and voltage skew, and reducing fuel costs as well as reducing energy loss and voltage deviation, as well as reducing emissions costs. The use of these cases facilitates a fair and comprehensive evaluation of the superiority and effectiveness of POA in comparison with the coot optimization algorithm (COOT), golden jackal optimization algorithm (GJO), heap-based optimizer (HPO), leader slime mold algorithm (LSMA), reptile search algorithm (RSA), sand cat optimization algorithm (SCSO), and the skills optimization algorithm (SOA). Based on simulations, POA has been demonstrated to outperform its rivals, including COOT, GJO, HPO, LSMA, RSA, SCSO, and SOA. In addition, the results indicate that POA is capable of identifying the most appropriate worldwide solutions. It is also successfully investigating preferred search locations, ensuring a fast convergence speed and enhancing the search engine's capabilities. [ABSTRACT FROM AUTHOR]

Electrical power distribution is the most important division in the power supply chain. However, its sustainability in terms of efficiency is very important for the growth of every country. This main objective of the paper is to assess the productivity dynamics of this process using the data envelopment analysis (DEA) methodology to analyse the effectiveness of the electricity distribution regions (EDRs) over a period of 7 years. The paper adapts the biennial Malmquist productivity index by infusing it with the slacks-based measure (SBM) to assess the productivity dynamics of EDRs in Ghana. Productivity dynamics were assessed by decomposing the SBM-BMPI productivity scores into the efficiency, technology, and scale change. It was discovered that the productivity of EDRs in Ghana progressed by 16.23% per annum over the sample period. Productivity was driven mainly by technological change and not the efficiency changes and scale changes. [ABSTRACT FROM AUTHOR]

Virtual synchronous generator technology can effectively improve the anti‐interference characteristics of the system frequency and bus voltage in the microgrid, and solve the problems of insufficient damping and low inertia of the system. However, in an islanded microgrid with multiple distributed generation, the difference in line impedance will cause local voltage deviations, which in turn leads to a series of problems, such as reducing power distribution accuracy and increasing bus voltage drop. Therefore, for the island‐type microgrid multi‐inverter distributed power generation parallel system, in order to solve the problem of low power distribution accuracy and large frequency oscillation caused by system parameters in virtual synchronous generator control, an improved virtual synchronous generator control algorithm based on adaptive droop coefficient is proposed in this paper, which not only eliminates the resistance component of the line impedance, makes the system impedance characteristic present a purely inductive nature, but also realises real‐time adjustment of active and reactive power. While maintaining the stability of the bus voltage and system frequency, it maintains high power distribution accuracy and improves the dynamic performance and operational stability of the power grid system. [ABSTRACT FROM AUTHOR]

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively. [ABSTRACT FROM AUTHOR]

The physics of an electrical grid requires that the supply injected into the grid is always in balance with the quantity consumed. If that balance is not maintained, cascading outages are likely to disrupt supply to all consumers on the grid. In the past, vertically integrated monopoly utilities have ensured that supply is adequate to meet demand and maintain grid stability, but with deregulation of generation, assuring adequate supply has become much more complex. The unique characteristics of electricity distribution means that there are immense potential externalities among market participants from supply shortfalls. In this paper, we discuss the institutions that US electricity markets have developed to avoid such destabilizing supply shortfalls when there are multiple generators and retailers in the market. Though many of the markets rely on standardized requirements for supplier reserves, we conclude that recent technological progress may steer future evolution towards a system that relies to a greater extent on economic incentives. [ABSTRACT FROM AUTHOR]

Power utilities worldwide are facing enormous challenges when it comes to the distribution of electricity. With these challenges, electricity theft is regarded as the most common challenge in the electrical distribution system. Electricity theft can be meter tampering done in consumer houses and illegal connections done using hook-ups from the distribution pole grids. These electricity theft challenges have caused power utilities to reconsider customer engagements focusing on feedback, putting loss detection systems in their distribution system networks, using artificial intelligence to schedule maintenance and other asset management activities, etc. The main focus of this paper is to design a real-time power theft monitoring and detection system that is able to detect power theft in distribution systems. This proposed system utilizes smart meters consisting of an Arduino ATMega328P microcontrollers with GSM modules (Global System for Mobile Communication) used for system communication. Cloud storage is created to store the smart meter data. Simulations of the proposed system were done using Proteus Design Suite v.8.10 SP3 software. The proposed system is practically constructed for prototype measurement results. Should power imbalances be measured by the system, the authority office will receive an SMS notification as an alert for power theft detected by a specific smart metering system. The authority office will analyse the power measurements sent to the cloud storage (MATLAB Online, ThingSpeak IoT channels display), and further action will be taken. [ABSTRACT FROM AUTHOR]

Due to the strong dependence between electricity generation and heating supply and the need for optimal use of renewable energies to supply power and heat loads, it is essential to provide an integrated energy management structure for electricity and heating systems. This paper studies the techno‐economic and environmental aspects of a combined electricity distribution and district heating system by considering multi‐type flexible energy resources and the environmental and practical constraints of both networks. The integrated energy system (IES) is equipped with wind power plants, combined heat and power plants, gas boilers, heat pumps, multi‐energy storage systems, electric vehicle parking lots, and multi‐energy demand response, as well as the IES operator can take part in the energy market to supply thermal and electrical demands. Besides, a robust strategy is adopted to control the power price uncertainty in the day‐ahead scheduling of IES, where a robustness parameter is considered to adjust the risk level of scheduling. The numerical results show that technical and environmental constraints can significantly increase the operation cost of IES by 11.8% and 3.6%, respectively. In addition, multi‐type flexible resources can improve the operational flexibility of IES by 16.1%. [ABSTRACT FROM AUTHOR]

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models. [ABSTRACT FROM AUTHOR]

Electricity distribution companies are in-charge-of supplying stable electric energy to urban customers over a wide geographical area. In these distribution networks, large number of Distribution-Transformers (DTs) are installed on towers or in electric-rooms with the ratings below 1000 kVA, which are the most important assets of distribution companies. Periodic maintenance and test services are not economically feasible for all in-operation transformers. Hence, estimating the remaining useful life and scheduling the maintenance times of DTs are of great technical and economic importance. An analytical approach is presented in this paper for predicting lifetime of DTs based on degree of polymerization. An appropriate Machine-Learning (ML) algorithm trained by measured data is selected for forecasting driven load according to the ambient temperature and humidity and day-hour schedules. The relation of Hot-Spot-Temperature in IEC60076∼7 is modified based on the measured data for accounting unbalanced-loading. Neutral-current is predicted using a trained ML-model. Another approach is proposed for determining times of oil filtering services aiming to minimize the maintenance cost and maximizing the remaining life. The proposed lifetime estimation method is validated by comparing its results with the damaged DT data of a sample network chosen as the case studies for this research, i.e., Bushehr province, Iran. [ABSTRACT FROM AUTHOR]

Transformer failures have a significant cost impact on the operation of an electrical network. In many utilities, transformers have been operating for many years past their expected usable life. As power demand has surged, transformers in some areas are being loaded beyond their rated capacity to meet the demand. One of the vital components in a transformer is the on-load tap changer (OLTC), which regulates the voltage in the distribution network. This study aims to review several condition-monitoring techniques (online and offline) that can monitor the health of the OLTC and assure the safety of the transformer's OLTC from irreparable damage by detecting the defect at an earlier stage, which is preceded by the specification of typical faults. This paper also discussed the common faults of the OLTC and the root causes of these faults. The OLTC is prone to mechanical faults due to its frequently changing mechanism in the tap operation. The OLTC are also prone to oil as well as thermal faults. As a result, it is critical to monitor OLTC conditions while they are in use. Proper management of condition monitoring (CM) for the OLTC is useful and necessary to increase availability and achieve optimised operating. Condition monitoring (CM) and diagnostics methods (DM) have been developing since the 1950s. CM and DM have been implemented to diagnose and detect an incipient fault, especially for the OLTC. Many techniques, online and offline, are being used to monitor the condition of the OLTC to prevent failure and minimize outages. These DM and CM will prolong the operational cycle and avoid a major disaster for the OLTC, which is an unfavorable scenario. [ABSTRACT FROM AUTHOR]

Electricity consumption pattern recognition is the foundation of intelligent electricity distribution data analysis. However, as the scale of electricity consumption data increases, traditional clustering analysis methods encounter bottlenecks such as low computation speed and processing efficiency. To meet the efficient mining needs of massive electricity consumption data, in this paper a parallel processing method of the density-based k-means clustering is presented. First, an initial cluster center selection method based on data sample density is proposed to avoid inaccurate initial cluster center point selection, leading to clustering falling into local optima. The dispersion degree of the data samples within the cluster is also used as an important reference for determining the number of clusters. Subsequently, parallelization of density calculation and clustering for data samples were achieved based on the MapReduce model. Through experiments conducted on Hadoop clusters, it has been shown that the proposed parallel processing method is efficient and feasible, and can provide favorable support for intelligent power allocation decisions. [ABSTRACT FROM AUTHOR]

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework's ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade. [ABSTRACT FROM AUTHOR]

Active electricity distribution systems (AEDS) play an important role on the social welfare that is highly relies on the reliability of these systems. In this regard, maintenance management tries to decrease component failures via repairing component that are more possible to be failed in operation time. In this manuscript, a stochastic MINLP maintenance scheduling model is proposed in which the reliability of AEDS's equipment is considered through employing a three-state Markov model. This model helps us to embed the uncertainty of AEDS's equipment service restoration time by considering online monitoring system for it and FLISR functions in AEDS. In this paper, minimizing the total cost of AEDS is determined as objective function considering total customer interruption cost, total cost of energy not supplied, total cost of prevented maintenance actions, total corrective maintenance actions cost implemented in the AEDS and total cost of implementing online monitoring for AEDS's component in maintenance scheduling. The proposed framework is applied in two different cases, Case I is maintenance scheduling without online monitoring system and Case II is maintenance scheduling with online monitoring system. The aforementioned cases are implemented in an IEEE Test System as well as a real scale distribution network. Moreover, several sensitivity analyses are implemented for indicating the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Purpose: The Balanced Scorecard (BSC) possesses an inherent duality, as it has been described as a carrier of institutions (i.e. the BSC is a "management ideology" or "mode of thinking") and a flexibly interpretive boundary object at the same time. This study examines how this inherent duality of the BSC may influence the unfolding rationales surrounding its implementation and use. Design/methodology/approach: Empirical support for this investigation is gathered from an in-depth field study. The focal firm is a Brazilian electricity distribution company that transitioned from state to private ownership under hyper-regulation, and whose holding company experienced strategic and structural changes. Findings: The study identified a misalignment between the characteristics of the firm (e.g. organizational logics) and the perceived BSC features. This misalignment initially produced tensions and institutional logics complexity for the organization forcing the BSC implementers to rationalize it to provide meaning regarding its implementation in the firm. The findings also show why and how the promoters of the BSC conducted its "strategy of translation" in order to disentangle and reassemble both the material and symbolic components of the BSC to facilitate its implementation and use. It was found that promoters of the BSC engaged in contextualization work, which featured two main actions: a combination of coupling and selective decoupling and a change of meaning. Originality/value: This paper advances current understanding of the process of the unfolding rationales surrounding management accounting innovations (e.g. the BSC). The study shows that the BSC unfolds in more complex, time-related and simultaneous ways than has previously been reported in the literature. Moreover, the paper contributes by explaining how the management's rationales, relating to their historical understanding, perception of legitimation needs and social skills, contributed to the continuous unfolding of the BSC. In addition, four potentially interesting areas for further research were identified. [ABSTRACT FROM AUTHOR]