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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 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]

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]

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 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]

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]

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]

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]

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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]

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]

[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]

• 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]

The studies on strategies for improving restoration times in electrical distribution systems are extensive. They have theoretically explored the application of mathematical models, the implementation of remotely controlled systems, and the use of digital simulators. This research aims to connect conceptual studies and the implementation of improvements and impact assessment in electrical distribution systems in developing countries, where distribution technologies vary widely, by employing a comprehensive methodology. The proposed research examines the restoration times for faults in substations within general distribution networks in the central–western region of Mexico. The study comprises these stages: (a) diagnosing the electrical supply, demand, and infrastructure; (b) analyzing the electrical restoration time and the restoration index of the substations; and (c) providing recommendations and implementing pilot tests for improvements in the identified critical substations. The results revealed 12 analysis zones, including 120 distribution substations, 150 power transformers, and 751 medium voltage circuits. Among the substations, 73% have ring connections, 15% have TAP connections, and 12% have radial connections. Additionally, 27% of the substations rely on only a single distribution line. The study identified areas with significant challenges in restoring electricity supply, particularly focusing on power transformers: 32 transformers with permanent power line failures requiring load transfer via medium voltage; 67 transformers requiring optimized restoration maneuvers due to specific characteristics; and 4 areas with opportunities to enhance the reliability of the power supply through remote-controlled link systems. The analysis resulted in the installation of 145 remote link systems, which improved restoration rates by over 40%. This approach is expected to be replicated throughout Mexico to identify improvements needed in the national distribution system. [ABSTRACT FROM AUTHOR]

The cost reflectivity of electricity distribution network tariffs has been debated in several countries, and various ways to enhance it have been investigated in recent years. However, the recent academic literature regarding the approach based on cost causation has a clear gap because no case studies show how distribution network tariffs can be determined in practice for large customer groups. This article offers a calculation methodology to determine distribution network tariffs based on cost causation along with a case study where unit prices are determined for the tariff structures still widely used today using the data for two separate network areas being operated by two Finnish distribution system operators (DSOs) in an unbundled electricity market environment. The results of the case study show that the total differences between the target and the realized turnovers in both investigated networks are smaller than 1%, which means almost a full cost recovery. In addition to traditional tariff structures, the proposed calculation methodology can also be modified to design and determine other pricing schemes. The need for systematic calculation processes is growing to improve the cost reflectivity of present tariffs and adapt to the needs of the evolving operating environment, novel tariff structures, and new emerging customer groups. [ABSTRACT FROM AUTHOR]

With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy. [ABSTRACT FROM AUTHOR]

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total. [ABSTRACT FROM AUTHOR]

The decarbonisation of electricity generation requires real-time monitoring and control of grid components to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge is to identify the source of illegal electricity consumption, classify it, and localise it. To address these difficulties, two distinct classification problems are addressed through the utilisation of machine learning methodologies. The initial classification task concerns the categorisation of loads that consume illegal electricity in agricultural irrigation. The subsequent classification problem pertains to the categorisation of feeder branches where such loads are activated. Therefore, a pilot distribution grid feeder has been simulated, and irrigation motors have been used as illegal loads which are activated at different points along the distribution feeder. The data collected from these simulations are used to create a data set where three-phase current data are collected from the transformer substation. The generated data set is employed to train machine learning models for the classification of illegal loads and feeder branches. The performance results of machine learning methods is obtained using the following metrics: accuracy, precision, recall, and F1-score. The results of the classification of loads stealing electricity in agricultural irrigation demonstrate that the bagged trees (BAT) algorithm achieves 99.64% in each criterion. In branch classification, the algorithm achieves the best results, with 97.64%, 97.40%, 96.22%, and 96.81%, respectively. Both classification performance results indicate that the proposed algorithm is effective for solving both classification problems. This research demonstrates the efficacy of ML-powered real-time monitoring and control in combating energy theft and promoting efficient resource utilisation within agricultural irrigation networks. It is a pioneering study in the field of determining and classifying illegal loads in agricultural irrigation. Further research will investigate the potential for expanding the system’s capabilities to include different load types and exploring alternative ML techniques for broader applicability within the context of low voltage distribution network monitoring. [ABSTRACT FROM AUTHOR]

A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts. [ABSTRACT FROM AUTHOR]

• A pricing method of Time-of-Use transmission price is proposed based on ATC curve. • A market allocation model of power generation is built based on Prospect Theory. • A bi-level model is solved by combining heuristic algorithms with Cplex tool. • The proposed transmission price promotes the utilization for certain project. The electricity delivered by inter-regional line projects is impacted by the operation of spot market, the previously approved fixed transmission pricing method based on planned electricity may not be fully applicable. To solve the above problem, the Time-of-Use transmission pricing method for inter-regional line with spot market at both ends is proposed in this paper. Firstly, it analyses the impact of transmission price on the electricity distribution of the power producers in the spot market at both ends, and the clearing price and total power generation cost in the spot market at the receiving-end. Then, the calculation method of daily available transmission capacity is introduced. Further, based on the incentive of transmission price, a Bi-Level optimization model is established by considering spot market clearing at both ends and the cost recovery of inter-regional line. On the transmission side, the optimal model of transmission price to maximize the benefits of the inter-regional line is built. On the spot market side, consider transmission prices, the power distribution model in the sending-end market and a unit commitment optimization model in the receiving-end market are established, and the relationship between transmission price and transmission electricity is found for the inter-regional line. The two models are interacted and restricted by each other, so a Bi-Level model of the cooperation between the inter-regional line and the spot market is formed. Finally, an example is simulated based on the Particle swarm optimization algorithm and Cplex optimization tool, and the effectiveness of the method is verified. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

This paper presents an approach to the transactive energy (TE) market that aims to improve the network efficiency and fairness of electricity market in distribution systems. The TE market is designed to allow subscribers to trade electricity in real-time based-on their energy consumption and generation forecast. The proposed framework utilizes distribution locational marginal pricing (DLMP), which accurately reflects the costs of electricity transmission and distribution, to fairly allocate these costs among subscribers. The Small Neighborhood Search Optimal Points (SNSOP) algorithm is then introduced to solve the challenge of finding optimal points for all participants in the TE market, as the maximum financial gain for one participant comes at the maximum financial loss of another participant. The results of the case study show that the proposed framework leads to a more efficient and reliable electricity network, by enabling decentralized control and reducing losses through optimal trades in the TE market. • A decentralized transactive energy framework is proposed for peer-to-peer electricity market. • Distribution locational marginal pricing values energy based-on network constraints. • A distributed algorithm coordinates participants while preserving privacy of sensitive data. • Case studies demonstrate network efficiency improvement by transactions. • Integrated techno-economic modeling ensures physical feasibility energy transfers. [ABSTRACT FROM AUTHOR]

New centralized automation functions in distribution system control centers are needed in order to ensure the control of both distribution network and connected DGs. Consequently, state estimators need to be developed for future distribution systems to assess the network's state in real time, i.e., minutes typical time frame, based on real, and virtual measurements. This paper proposes a new method based on the particle community optimization algorithm to estimate the state of electricity distribution networks. To increase the efficiency, accuracy, and speed of convergence rate to the answer and prevent the main particle assembly algorithm from fluctuating, the second particle assembly loop is provided in addition to the mutation algorithm and the isolation function. To consider the uncertainty of loads in distribution networks, modeled as active and reactive, virtual measurements with realistic error have been modeled. Results are presented and discussed to prove the efficiency of the proposed method, including sensitivity analysis for parameters used by the algorithm. The simulation results on two 6-bus and 34-bus radial distribution networks of the IEEE test show that the estimation of the distribution state based on the proposed DLM-PSO algorithm has a lower estimation error and standard deviation than the original HBMO, GA, WLS, and PSO algorithms. [ABSTRACT FROM AUTHOR]

The safety of the electrical energy supply is one of the most critical issues for companies engaged in production. The internal distribution network, which will provide the energy for the production elements, is essential. Power Transformers, which energize the loads fed from the electricity distribution network, are backed up in a way suitable for parallel operation for energy supply security. As a result of this situation, in case of a predictive failure in one of the transformers, energy supply safety is ensured since the other transformer is designed to carry the load alone. When one of these identical transformers is damaged, the relative short-circuit voltage of the new transformer to be integrated into the system may be different. The transformers with different short-circuit voltages are not shared their load equally. For this reason, the total amount of load that they can be loaded, the relative short-circuit voltage that will occur, and the load rates they are loaded change. In this study, a detailed analysis of two transformers with different relative short-circuit voltages and operating in parallel was made, both theoretically and as a result of the application, with the information obtained through the SCADA program. The theoretical values were calculated as a result of the analysis, the SCADA values were compared, and the margin of safety between them was established. It has been shown that the safety margin will protect against overload on a low short-circuit transformer. After the commissioning, the load rating needs to check and it can be regulated in line with the load rates that will occur after commissioning. [ABSTRACT FROM AUTHOR]

This article discusses the major challenges related to greenhouse gas (GHG) emissions in the electricity sector and their impact on global climate change. The electricity sector is responsible for about a quarter of total global GHG emissions. To address these challenges, Life Cycle Assessment (LCA) is used to measure the environmental impact of different energy sources and electricity generation and distribution processes. The circular economy is presented as a promising approach to reducing the carbon footprint of the electricity sector. By optimizing the use and value of materials throughout their life cycle, this approach contributes to waste minimization and resource efficiency. Morocco is committed to reducing its GHG emissions and has adopted policies and regulatory frameworks to combat climate change. This study aims to calculate the climate change impacts of electricity distribution phases by applying a life-cycle approach to the case of an electricity distribution company in Morocco. This assessment makes it possible to identify significant contributors from each area. In the context of this company, it is a question of demonstrating how the application of the principles of the circular economy concepts contributes to the reduction of greenhouse gas emissions, in particular, that of scope 3. This study may be useful for other similar companies. [ABSTRACT FROM AUTHOR]

The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge is devising incentive mechanisms that reward distribution utilities for their operational and investment choices, aiming to mitigate or decrease electricity non-payment rates and avoid tariff increases for regular customers. Despite adhering to the principles of incentive regulation, the Brazilian Electricity Regulatory Agency (ANEEL) uses a methodological approach to define regulatory targets for electricity defaults tied to econometric models developed to determine targets to combat electricity non-technical losses (NTLs). This methodology has been widely criticized by electricity distribution utilities and academics because it includes many ad hoc steps and fails to consider the components that capture the specificities and heterogeneity of distribution utilities. This study proposes a fuzzy inference-based model for defining regulatory default targets built independently of the current methodological approach adopted by ANEEL and aligned with the principles of incentive regulation. An empirical study focusing on the residential class of electricity consumption demonstrated that it is possible to adopt a specific methodology for determining regulatory default targets and that the fuzzy inference approach can meet the necessary premises to ensure that the principles of incentive regulation and the establishment of regulatory targets are consistent with the reality of each electricity distribution utility. [ABSTRACT FROM AUTHOR]

Technical losses in electrical grids are inherent inefficiencies induced by the transmission and distribution of electricity, resulting in energy losses that can reach up to 40% of the generated energy. These losses pose significant challenges to grid operators regarding energy sustainability, reliability, and economic viability. Distributed Energy Resources (DERs) offer promising solutions to lower technical losses by decentralizing energy generation and consumption, reducing the need for long-distance transmission and optimizing grid operation. Hence, estimating the impact of DERs on grid technical losses becomes paramount for grid operators and planners. In response, this article proposes the application of regression modeling and nonlinear curve fitting algorithms to provide a more nuanced understanding and better characterize the intricate interplay between DER deployment and technical losses. Through a comprehensive case study based on more than 1080 computer simulations, we demonstrate the effectiveness of our proposed dynamic polynomial varying coefficient regression model in estimating the impact of DERs on technical losses within electrical grids. The proposed model offers a simple and effective methodology that allows grid operators to gain insights into the nonlinear dynamics of DER integration and make quicker and more informed decisions regarding grid management strategies, infrastructure investments, and policy interventions. Also, this research contributes to advancing the field of grid optimization by offering a simple equation that enhances our ability and haste to assess and mitigate technical losses in the context of an evolving energy landscape characterized by increasing DER adoption. [ABSTRACT FROM AUTHOR]

In this study, a novel method called the Continuous–Discrete Student Psychology-Based Optimization (CD-SPBO) is introduced. The technique is designed to address the simultaneous allocation of renewable distributed generators (DGs) and distribution static compensators (D-STATCOMs). A novel multi-objective function is formed by combining indices concerning the active power loss, voltage profile, voltage stability and cost consisting of investment, penalty for violation of environmental limits, and energy loss, which are combined through analytic hierarchical process. The CD-SPBO algorithm is used for solving the multi-objective allocation problem for non-dispatchable solar PV-DGs, dispatchable biomass DGs, D-STATCOMs, and their combinations. The superiority of the CD-SPBO algorithm was both numerically and statistically established by comparing its results with other state of the art methods such as Gorilla Troop Optimizer, Artificial Humming Bird Optimizer, and Harris Hawk Optimizer for simultaneous allocation of one, two, and three pairs of DGs and D-STATCOMs on 33-bus and 118-bus test systems. Further, the simulation findings involving seven distinct planning scenarios for the allocation of DGs and D-STATCOMs supported the CD-SPBO algorithm's effective execution for simultaneous optimal allocation of dispatchable DGs, non-dispatchable DGs, and D-STATCOMS. The suitability of different planning scenarios for improving the overall performance of the distribution system are analyzed in detail. The research insights may prove beneficial for network planners in determining the best combination of devices to meet their needs. [ABSTRACT FROM AUTHOR]

This work presents a production management platform developed to minimize the costs of the capacity charge, part of the electricity bill associated with the cost of maintaining grid capacity during periods of high, fluctuating loads. After a summary of the regulatory solutions on the capacity market in Poland, a capacity charge management system is presented, specifically designed for production facilities within the Energy-Intensive Industry sector. The proposed platform combines hardware data collection, a simulation tool analyzing the electrical energy demand profile to predict the future impact on the capacity charge, and a cloud-based user interface providing real-time recommendations to the plant operators regarding the corrective actions needed to minimize the cost of operation. It was pilot tested in collaboration with a large production facility in Poland, for which the capacity charge was among the main components of the electricity distribution costs. Pilot tests were conducted in the period from January 2022 to September 2023. The tested platform allowed us to shorten the time span of elevated capacity charges from 33% in the year 2022 to only 7% in the year 2023. It also reduced the benchmark capacity charge indicator by more than 11%, from 4.02% to −7.56%, over the duration of the experiments. This improvement was achieved without major changes to the organization and planning of the work. [ABSTRACT FROM AUTHOR]

The lightning-induced voltage in 10-kV overhead distribution lines was calculated and analyzed using the Agrawal coupling model and second-order finite difference-time domain (FDTD) method. The influence of lightning position on lightning-induced voltage was analyzed using the concept of indirect lightning withstanding levels. To achieve the limiting effect on lightning-induced voltage in 10-kV lines, the installation density of surge arresters should not be less than 1 group per 200-300 m. [ABSTRACT FROM AUTHOR]

The integration of renewable energy systems into electricity grids is a solution for strengthening electricity distribution networks (SEDNs). Renewable energies such as solar photovoltaics are suitable for reinforcing a low-voltage line by offering an electrical energy storage system. However, the integration of photovoltaic systems can lead to problems of harmonic distortion due to the presence of direct current or non-linear feedback in networks from other sources. Therefore, connection standards exist to ensure the quality of the energy before injection at a point of common coupling (PCC). In this work, particle swarm optimization (PSO) is used to control a boost converter and to evaluate the power losses and the harmonic distortion rate. The test on the IEEE 14 bus standard makes it possible to determine the allocation or integration nodes for other sources such as biomass, wind or hydrogen generators, in order to limit the impact of harmonic disturbances (LIHs). The evaluation of the harmonic distortion rate, the power losses as well as the determination of the system size is done using an objective function defined based on the integration and optimization constraints of the system. The proposed model performs better since the grid current and voltage are stabilized in phase after the photovoltaic source is injected. [ABSTRACT FROM AUTHOR]

Here, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs. [ABSTRACT FROM AUTHOR]