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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]

The electricity sector plays a key role in achieving zero emissions targets. The required transition will lead to substantial changes in the supply, demand, and distribution of electricity, as well as in stakeholder roles. Future market designs may change substantially to accommodate these changes, address challenges, and take advantage of new opportunities. This paper reviews the characteristics of future carbon-neutral electricity systems and electricity market design options. To provide a guiding framework for the literature review, we transfer the complexity of electricity systems into a three-layer structure: Firstly, we analyze papers that rely on techno-economic modeling of the physical electricity system. As a case study, we analyze various studies focusing on a decarbonized European electricity system in 2050. Secondly, we review papers that investigate the economic behavior and effects of self-interest-seeking stakeholders such as producers, network operators, and consumers. Finally, we review papers focusing on policy and market design questions that guide policymakers in achieving a target physical asset combination while considering the behavior of stakeholders. We highlight common trends and disagreements in the literature, review the main drivers of future markets, and finally provide a mapping between those drivers, challenges, and opportunities. The review concludes that the most promising next step toward a fully comprehensive assessment approach is to combine existing approaches across topical and disciplinary boundaries. [ABSTRACT FROM AUTHOR]

The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [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]

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

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 electric distribution sector is facing significant challenges in integrating distributed energy resources (DERs), with most assessment methodologies being primarily tested on standard feeders. However, given the nature of electric distribution systems, each feeder is unique and constantly changing, emphasizing the need for customized and updated feeder models for local quantitative studies. These studies encompass network operation, expansion planning, and DER integration. In response to this context, this paper proposes a methodology for extracting and processing information from public data to model up-to-date electric feeders. As these feeder models represent real operating circuits, they can be utilized by utilities, investors, and consumers. The proposed approach involves processing data from the Brazilian utilities' geographic database (BDGD, in Portuguese) to develop structured simulations of distribution feeders using OpenDSS software. The BDGD standards are established by the Brazilian National Electric Energy Agency (ANEEL). In order to validate the methodology, two case studies are presented evaluating the impacts of distributed generation on real feeders from the Brazilian utilities: Minas Gerais Electric Power Company (CEMIG) and Bahia State Electricity Company (Neoenergia COELBA). [ABSTRACT FROM AUTHOR]

This paper proposes an impedance-based approach for locating short-circuit faults in active distribution networks (DNs). This topic is a crucial task for operators, especially in grids with inverter-based distributed generators (IBDGs). Various methods have been proposed in this research area, including traveling waves, impedance-based methods, and artificial intelligence (AI) techniques. Among them, the impedance-based scheme offers a straightforward and efficient feature suitable for integration with AI-based techniques. This paper introduces an enhanced fault localization method based on impedance estimation, consisting of two main components: (i) fault distance determination and (ii) faulty section identification. This method accounts for the modeling of inverter-based resources under both symmetrical and asymmetrical faults, incorporating the impact and behavior of such sources. Unlike conventional impedance-based methods, our approach does not require network information such as structure, lines, load data, or voltage and current measurements along the feeder at multiple points. It can serve as a feature in AIbased techniques, significantly enhancing accuracy and reducing the complexity of such techniques. To validate the efficacy of the proposed approach, we conducted a series of time-domain case studies and provided mathematical proofs. The results demonstrate the effectiveness of our scheme in accurately locating faults with varying resistances at different positions in the presence of IBDGs. [ABSTRACT FROM AUTHOR]

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network. [ABSTRACT FROM AUTHOR]

To calculate the losses of distribution feeders, this paper uses an iterative method that is limited to restricted measurements. The approach presented in this paper uses bill data in addition to output information from a very small number of real-time measurements located on the secondary side of distribution transformers. This method attempts to estimate the load of distribution transformers injected into LV feeders. Energy losses for LV feeders are evaluated by first estimating the power and periodic energy injected to each of the LV feeders and then subtracting the total consumption bills from these estimated values. By using this method, the amount of energy loss is estimated. In this article, a new method called iterative power factor adjustment method is considered as a potential method for estimating losses. The power factor can be increased by repeatedly using evolutionary algorithms and including capacitors in the system. In order to reduce system losses and increase network effectiveness. In this paper, a new method for examining and evaluating Non-Technical Losses (NTL) is proposed. This method considers load estimation and limited measurement to place high priority feeders. [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]

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]

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]

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]

Ultrasonic atomization technology has recently been widely used in industry due to its higher atomization efficiency. This paper proposes a method of electrostatic-assisted ultrasonic atomization spraying and designs a particular ultrasonic power supply that can directly introduce high-voltage static electricity onto the surface of the ultrasonic atomization so that the liquid can contact the charge and enhance the charging effect. This paper conducts numerical simulation analysis on the atomization process and statistically analyzes the numerical simulation atomization particle size through image processing. In addition, this paper designs experiments from two aspects of atomization particle size and film-forming effect. The results show that the experimental results are consistent with the numerical simulation results, verifying the accuracy of the numerical simulation results. It shows that in sub-droplet formation, introducing static electricity will increase the number of capillary waves, promote two mechanisms of the capillary pinch-off mechanism and the Rayleigh-plain instability, reduce atomization particle size, and increase atomization efficiency. Compared with the traditional airflow beam method, the film-forming surface quality of the electric field beam method is higher, and the surface roughness is lower. It solves the problem that airflow affects film-forming quality in traditional ultrasonic spraying with the airflow beam method. It provides a theoretical basis and scientific guidance for applying electrostatic-assisted ultrasonic atomization technology in the atomization and film preparation industry. [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 COVID-19 pandemic has caused fluctuations in electricity demand, altering people's lifestyles and electricity usage patterns, thereby affecting the accuracy of demand predictions. However, existing studies on electricity forecasting have not adequately considered the incorporation of COVID-19-related features and the analysis of electricity usage characteristics across different regions of the UK. Therefore, this paper, based on data of the UK's national electricity demand, conducts an analysis around the scenario of a large-scale health emergency in society. We explore the changing patterns and regional characteristics of electricity consumption during the COVID-19 pandemic, comparing the forecast results before and during the pandemic to illustrate its impact on the UK's electricity demand. By introducing COVID-19-related features into the models, we compare the forecast results before and after their inclusion. The results indicate that the COVID-19 pandemic has had a certain impact on the electricity prediction in the UK, leading to a 22.8% decrease in prediction accuracy. However, the models' correlation improved with the inclusion of COVID-19-related features, resulting in a 13.2% enhancement in prediction accuracy compared to the previous models. Additionally, the study summarizes other factors influencing electricity demand, such as power imports/exports and clean energy usage, as considerations for electricity distribution planning. This contributes to improving the accuracy of predicting the UK's electricity demand during COVID-19 pandemic, enabling the government to adjust power dispatching plans reasonably based on relevant factors, achieving rational distribution and efficient scheduling of power resources. [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]

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

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The electrical power distribution parts include the distribution transformer responsible for reducing high voltage (HV) 11 Kv to low voltage (LV) 416/240 volts, and the power distribution lines (PDLs), which are the source of electrical power supply for residential neighborhoods. Any malfunction of these parts, result in a power outage. Currently, any faults are reported, detected, and alert in a primitive way, and this mechanism certainly takes a lot of time and effort. In this paper, an automatic approach for detection, report and alert has been suggested. This approach process is carried out by continuously checking the status of the three output feeders (power distribution lines), which is the low voltage side of the distribution transformer, when a malfunction occurs in any one or more of these feeders, it will generate an alert signal in the form of a text message using short message service (SMS). The maintenance center is notified by receiving this SMS via SIM900 modem through the global system for mobile (GSM) communications network. The SMS text message includes feeder number, fault type and location address (street number and distribution transformer number). In this design, time and effort are shortened and the maintenance center enables fully automatic monitoring of electric power distribution parts within the sector of responsibility. [ABSTRACT FROM AUTHOR]

Renewable energy sources play a crucial role in the provision of power supply in active distribution networks. In networks that rely on renewable energy sources, it may become imperative to curtail the power generated by such units due to a variety of factors. The curtailment of renewable energy generation can be attributed to various factors such as overgeneration, voltage limitations at the network bus, and power limitations at the network line. As per the specified parameters, it is possible for the power generation of renewable units to exceed the load consumption at certain intervals throughout the operational duration. Hence, it is essential to devise a solution that mitigates the frequency and duration of power generation curtailments of renewable units. It is feasible to redistribute overgeneration of renewable energy sources from periods of excess to periods of insufficient capacity to meet demand. This study demonstrates that the provision of power to loads from the sub-distribution substation experiences a reduction in power supply and cost. It is feasible to transmit surplus power from a single bus to multiple other buses. The term used to refer to the ability to transfer power across different locations and time periods is known as spatiotemporal flexibility. The presence of data centers in distribution networks enables a reduction in costs and the frequency of power interruptions associated with renewable energy sources, through the utilization of spatiotemporal flexibility. The objective of this paper is to present a methodology for achieving temporal and spatial flexibility in data centers, with the aim of minimizing costs and curtailing the output generation of renewable energy units. Undoubtedly, this approach can also be executed by incorporating mobile batteries; however, it is noteworthy that mobile batteries entail additional costs on the network. Consequently, the implementation of the spatiotemporal flexibility approach in data centers has been adopted as a substitute for the utilization of mobile batteries. This approach, also referred to as virtual battery, effectively eliminates the costs associated with the use of mobile batteries. This study examines overgeneration of renewable units, bus voltage limitations, and line power restrictions on the standard IEEE 33-bus network. Three scenarios are considered, each involving changes in data center power in response to varying load profiles. The implementation of equitable power distribution across data centers, both in terms of temporal and spatial dimensions, as well as the adoption of flexible spatiotemporal flexibility scenario for data centers, have been observed. The findings of this study indicate that the approach employed in this paper leads to a decrease in costs and the output power of renewable units in the examined scenarios. The implementation of spatiotemporal flexibility mode has resulted in a reduction of power supply expenses ranging from 12 to 37 percent when compared to the basic case. Ultimately, the sensitivity analysis pertaining to the placement of data centers and the associated uncertainty index is presented. • Implementing a model of spatiotemporal flexibility within data centers to function as a virtual storage device. • The curtailment of power generation in renewable energy units has been executed under three different distinct circumstances. • The robust optimization method has been employed to demonstrate the uncertainties inherent in the operation problem. [ABSTRACT FROM AUTHOR]

In recent years, new smart technologies like electric vehicles and active demand management have been introduced to reduce carbon emissions in electricity distribution networks, resulting in altered electricity consumption patterns. However, the impact of the technologies on electricity consumption remains uncertain due to a lack of rigorous evaluation methods and planning techniques accounting for these changes. Addressing these gaps, this paper contributes three key elements to information systems literature. First, this paper presents a policy to plan multi-period electricity distribution networks that are able to take into account changing electricity consumption patterns. This paper uses a policy to determine the impact of new technologies on distribution network investments. Second, this paper determines the effect of network topologies on distribution network investments with altered consumption patterns. Third, this paper develops and solves a novel optimization problem that formalizes the task of long-term distribution network planning under variable consumption patterns. This work holds implications for distribution network management, policy, and research. It offers recommendations for integrating our policies into practical procedures, emphasizing the need for more rigorous planning incentives for policymakers. Furthermore, the large effect of altered consumption patterns shows that actions are needed to design policies that work towards lowering load coincidence. For researchers, the presented problem and solution methods are generalizable and can help researchers in other domains (e.g. parcel delivery, water distribution) to solve comparable planning problems. [ABSTRACT FROM AUTHOR]

• Enhancing the reliability of the electricity distribution system (EDS) in normal conditions by allocating the remote control switches (RCSs) using an MILP formulation. • Improving the resiliency of the EDS. Specifically, aiming to minimize the customers' interruption time and cost by using both the fragility and bathtub curves. • Improving the reliability and resiliency of the EDS by incorporating RCSs and distributed generations (DGs). • Conducting a sensitivity analysis of various factors, such as the customers' load, the investment cost, the RCS operations, and the failure rate. In today's world, power outages significantly impact people's lives. Improving the reliability and resiliency of the electricity distribution system (EDS) can significantly reduce the negative impact of power outages. In this regard, remote control switches (RCSs) are efficient devices that play a crucial role in enhancing the reliability and resiliency of EDSs. However, the challenge lies in strategically placing these switches to minimize power outages. This paper proposes a mixed-integer linear programming (MILP) formulation for the optimal allocation of RCSs. It considers load uncertainties and aims to enhance reliability under typical circumstances while reinforcing resiliency during extreme conditions. The formulation also considers two specific resilience criteria: the duration of interruptions experienced by customers and the total interruption cost incurred by all affected customers in the aftermath of disasters. A system is considered more resilient if individual customers experience fewer power interruptions during disasters. The formulation is rigorously assessed through its implementation on bus four of the Roy Billinton test system (RBTS4), and the outputs demonstrate that the proposed method improves both reliability and resiliency indices. Finally, this paper concludes with technical and economic comparative analyses that examine the effect of changing various parameters. [ABSTRACT FROM AUTHOR]

Against the background of 'Emission Peak, Carbon Neutrality', there is a significant integration of new energy into the power grid. The characteristics of 'double high' and 'double peak' in power systems become more pronounced, posing a substantial challenge to the stability and energy coordination of the power grid. To address this, we propose a flexible interconnection scheme based on an AC-DC bidirectional converter. It can solve the problem of high-load caused by new energy generation. Firstly, high-load and adjacent substations are flexibly interconnected through the DC bus on the low-voltage side, enabling load management within a small range. Secondly, AC-DC power transfers the excess new energy generation to other substations. It can promote the absorption of new energy. This method can improve the utilization efficiency of distribution transformers, realize capacity sharing between different transformers, and improve the reliability of power systems in low-voltage distribution networks. Finally, based on a DC interconnection project in Shandong Province (China), the effectiveness of the proposed flexible interconnection scheme is verified. The new energy consumption increases from 5% to 24.4%, and the load rate of high-load transforms drops below 70%. [ABSTRACT FROM AUTHOR]

The intermittency of electricity supply from conventional sources, increase in fuel prices and constant emission of greenhouse gases by non-renewable energy sources are major challenges faced by energy users. Energy from reneweable sources have advantages over the traditional (non-renewable) sources of energy. This paper presents the sizing and analysis of a standalone photovoltaic (PV) system for a 3-bedroom residence situated at Obollo-Nsukka (6.876°N, 7.403°E, 389 m) in Nigeria. The energy requirements of such a residence are 8.14 kWh/day and analysis have shown that the cost of constructing the PV system is 2,838,040 Nigeria naira (NGN). The cost of maintaining such a system within a lifetime of 20 years is between 159,328 NGN/year to 1,895,918 NGN/year. Comparing the levelized cost of energy (LCOE) of enugu electricity distribution company (EEDC) which is 66.5 NGN/kWh to the LCOE of the standalone PV system which is between 102,124 NGN/kWh to 419 NGN/kWh it was found out that the cost of electricity from the PV system is more than that of the conventional grid. The PV system provides feasible solution to the intermittency issues of the conventional grid in Nigeria. Hence, this technology only technically viable for residential electrification purposes in Nigeria. [ABSTRACT FROM AUTHOR]

This paper examines the technical efficiency of electricity supply across 11 electricity distribution companies using the Data Envelopment Analysis (DEA). The analysis was performed with a recent and extended data from 2015 to 2022. The output indicator for calculating electricity supply efficiency is electricity supply proxy by energy received by each electricity distribution company. The input indicators are network losses (proxy by transmission losses) and aggregate technical commercial and collection losses (ATC&C). The results show that all electricity distribution utilities are technically inefficient in electricity supply to a varying degree. Four electricity distribution companies performed above 45 percent level of technical efficiency, while two operate at less than 80 percent. Also, the efficiency performance of the 11 distribution companies worsened since the beginning of the COVID-19 pandemic era. Thus, privatization has not eradicated technical inefficiencies in the electricity supply. The inefficiencies in the electricity distribution sub-sector are partly due to technical constraints from network losses. [ABSTRACT FROM AUTHOR]

[Display omitted] • A physics-informed fast voltage control method of distribution network is proposed. • It develops a novel deep reinforcement learning algorithm with graph knowledge. • The proposed algorithm uses multiple agents with continuous action space. With the recent rapid uptake of photovoltaic (PV) resources, the overvoltage condition during low load periods or intermittent renewable generation is one of the most critical challenges for electricity distribution networks. Traditional model-based local or centralized control methods that rely on proper system parameters are difficult to mitigate rapid changes in power systems. Utilizing model-free/data-driven multiagent deep reinforcement learning (MADRL) has been recognized as an effective solution to active voltage control. However, existing MADRL-based control approaches trained solely on data are agnostic to the underlying real-world physics principles. Therefore, this paper aims to incorporate physical knowledge of regional distribution networks into MADRL's decision-making. The main contributions of this paper are summarized as follows. First, a novel physics-informed MADRL -based distributed voltage control method is proposed, which is still under a centralized training and distributed execution framework and only requires local measurements. Second, graph neural networks are employed to help MADRL agents learn graph knowledge (node features and topological information). Further, transformer is introduced to extract discriminative representations and ensure agents' cooperative control. Third, we adopt a physics-guided architecture of neural networks in the actor network to stabilize the training process and improve sample efficiency. Finally, simulation results based upon modified IEEE 33-bus and 141-bus networks validate the proposed method's effectiveness, robustness and computation efficiency. [ABSTRACT FROM AUTHOR]

• Incorporates market-based flexibility procurement to distribution system planning. • Hybrid approach that combines binary particle swarm and linear programming. • Case study for real 500 bus MV grid in Spain. • Procurement of flexibility services reduces network reinforcement investment by 14% • Sensitivity to calculate cost and availability thresholds that make flexibility competitive. Flexibility services enable distribution system operators to actively manage the grid for accommodating demand and generation growth while potentially reducing or delaying investments in grid reinforcements. This paper proposes a novel hybrid particle swarm optimization and linear programming methodology that analyzes explicit flexibility procurement as an alternative to conventional network reinforcements in electricity distribution network planning. The distribution system planning problem is decomposed into a master problem and an inner problem. Binary particle swarm optimization (BPSO) is used to determine the optimal investment decisions, binary variables, from a set of candidate grid reinforcements in the master problem. At the inner linear programming optimization problem, a market-based procurement of flexibility services is performed. The inner optimization problem obtains the total cost of flexibility and the volume of flexibility at each network bus required to defer or avoid part of the grid reinforcements. A real 500-bus medium voltage network is used to validate the proposed methodology. Results illustrate cost-effective network plans that combine flexibility procurement with network reinforcements. A sensitivity to the cost and availability of flexibility services is also conducted to calculate the thresholds where flexibility becomes an efficient alternative to reinforcing the network. [ABSTRACT FROM AUTHOR]

• An integrated nodal price is proposed for cost recovery of distribution networks. • The integrated nodal price enables to promote the consumption of renewable energy. • The proposed integrated nodal price can be decomposed into three price components. • An extended distribution locational marginal price of electricity is proposed. Traditional prices at the distribution level cannot efficiently guide the comprehensive cost recovery. To this end, this paper proposes a novel integrated nodal price by solving a comprehensive investment problem. The proposed integrated nodal price can be inherently decomposed into three price components: i) extended distribution locational marginal price of electricity, ii) distribution locational price of carbon, and iii) distribution locational price of investment. Specifically, the extended distribution locational marginal price enables to recover the power generation cost and on–off cost of generation units via a convexified mixed-integer optimal power flow model; the distribution locational price of carbon enables to recover the carbon emission cost through a carbon footprint tracing method; and the distribution locational price of investment enables to recover the investment cost of distribution network via a reverse power flow tracking method. These three price components work in a complementary manner and are coupled with each other through the shared optimal power flow results. Numerical results demonstrate that the proposed extended distribution locational marginal price can reduce the production cost by 10.41% in contrast to the traditional distribution locational marginal price. [ABSTRACT FROM AUTHOR]

This article addresses the pressing issue of reducing heat energy and conserving energy resources by exploring new energy sources, specifically wind and photovoltaic electricity generation in distribution systems. The study highlights the expanding use of electric devices in apartment houses, particularly the adoption of DC smart homes. A key highlight is the development of a smart grid optimization method to tackle power fluctuations, a critical challenge in integrating renewable energy sources and loads into the power system. The paper proposes controllable distributed loads, like batteries and heating pumps, to balance energy production and demand, forming an efficient network with a solar collector and photovoltaic generator. A breakthrough is identifying the best performance for a group of DC smart houses in the network, paving the way for potential future advancements that benefit consumers. The concept of a smart network with controllable loads in smart homes is thoroughly investigated, offering valuable insights into its potential benefits. Simulation results validate the system’s effectiveness, providing high-quality electricity, cost reduction, and improved network stability while mitigating greenhouse gas effects, contributing to sustainability efforts. The study’s findings are reinforced by simulations using MATLAB, further validating the novel approach’s practical implications for real-world application. [ABSTRACT FROM AUTHOR]

• An unprecedented very large-scale T&D dataset covering Texas is made publicly available. • The sheer size of this dataset is intended to provide a challenge for the scientific community. • This lays the foundation for demonstrating the coordination of T&D. • It can help foster the improvement of algorithms and tools for years to come. Distributed energy resources, such as rooftop solar, have rapidly expanded in recent years, given declining costs and the desire to reduce carbon emissions. With more energy resources located in the lower-voltage distribution system, it is increasingly helpful to utilize combined transmission and distribution (T&D) system models to analyze interactions between these normally-distinct subsystems. This paper proposes a methodology for creating very large-scale, highly detailed, combined T&D systems that are synthetic—that is, free from non-public data—yet still realistic. The methodology creates very large-scale combined T&D systems by merging the most up-to-date techniques for creating synthetic distribution feeder networks with the latest methods for building synthetic, meshed bulk-power transmission networks. This methodology is demonstrated on a T&D system geolocated in Texas, and benchmarked with co-simulation results. Validation demonstrates that the resulting syn -texas-TDgrid synthetic test system realistically represents characteristics found in actual networks, addressing the lack of available T&D test systems. With over 15,000 feeders and 46 million electrical nodes, this T&D dataset has applications for research in optimal power flow algorithms, voltage control, reconfiguration, and T&D coordination schemes under high adoption of distributed energy resources. [ABSTRACT FROM AUTHOR]

This paper proposes a set of digitalisation indicators focused on measuring the different digital capabilities and infrastructure of electricity distribution systems, as opposed to previous indicators which have mainly focused on performance and quality of service aspects. The indicators are classified according to the pillars of digitalisation: sensor and actuator, connectivity, data processing, and digital culture. They are use-case-agnostic and do not require a huge amount of information. In addition to this, three possible new applications of these indicators for distribution system operators and regulatory authorities are identified and discussed. The extensive use of these indicators in Europe could allow the development of fruitful collaborations between distribution system operators, allow the identification of cause–effect relations between grid performance and digital infrastructure, and improve the replicability of innovative smart grid solutions. However, this will only be possible if regulators promote the adoption of the proposed indicators and the dissemination of their results. • Smart grid indicators are required by European Directive 2019/944. • Indicators on sensors/actuators, connectivity, data processing, and digital culture. • Indicators to provide an overview of the distribution grid. • Indicators to study relation between performance and digital infrastructure. • Indicators to generate information for the replicability of solutions. [ABSTRACT FROM AUTHOR]