Electrical Distribution Networks Research Articles

Optimal integration of photovoltaic sources and capacitor banks considering irradiance, temperature, and load changes in electric distribution system

This paper introduces the Efficient Metaheuristic BitTorrent (EM-BT) algorithm, aimed at optimizing the placement and sizing of photovoltaic renewable energy sources (PVRES) and capacitor banks (CBs) in electric distribution networks. The main goal is to minimize energy losses and enhance voltage stability over 24 h, taking into account varying load profiles, solar irradiance, and temperature effects. The algorithm is rigorously tested on standard distribution networks, including the IEEE 33, IEEE 69, and ZB-ALG-Hassi Sida 157-bus systems. The results reveal that EM-BT outperforms established methods like Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), demonstrating its effectiveness in reducing energy losses and maintaining stable voltage profiles. By effectively combining PVRES and CBs, this research highlights a robust approach to enhancing both technical performance and operational reliability in distribution systems. Additionally, the consideration of temperature effects on PVRES efficiency adds depth to the study, making it a valuable contribution to the field of power system optimization.

Optimization of the Fuse Protection System of The BT Electrical Distribution Network in Likasi in Order to Reduce Electric Arcs And Expenses

Optimization of the Fuse Protection System of The BT Electrical Distribution Network in Likasi in Order to Reduce Electric Arcs And Expenses

High‐Impedance Fault Detection in Electric Power Distribution Network Using Local Pattern Transformation Methods and Bidirectional Long Short–Term Memory

High‐Impedance Fault Detection in Electric Power Distribution Network Using Local Pattern Transformation Methods and Bidirectional Long Short–Term Memory

Real time fault level monitoring to increase electricity distribution network utilisation

Real time fault level monitoring to increase electricity distribution network utilisation

Effects of negative spot market prices on electricity distribution network loading, experiences from the Finnish distribution system

Effects of negative spot market prices on electricity distribution network loading, experiences from the Finnish distribution system

The effect of the economic parameters of the electricity distribution system on CO2 emissions caused by the activities of the electricity distribution network

Iran ranks among the 20 most polluted countries globally, with a significant portion of carbon dioxide emissions stemming from fossil fuel-burning power plants. While these emissions are often associated with electricity generation, this study focuses on an overlooked source: the electricity distribution system within cities. Operational teams responsible for outage elimination, maintenance, and asset development—such as transformers and meters—contribute to emissions by relying on vehicles that navigate congested urban traffic. This research examines economic factors including development coefficients, failure rates, geographic conditions, inflation, and maintenance costs to optimise carbon dioxide emissions in three key sectors: outage elimination, maintenance, and development. Using sensitivity analysis of these parameters from 2016 to 2020, we quantified and proposed reductions in CO2 emissions across these activities. Additionally, a comparison of failure and maintenance rates identifies the processes with lower emissions. The findings provide a framework for optimising urban electricity distribution systems, offering insights into mitigating pollution at the distribution level and improving environmental sustainability in megacities.

Active power loss reduction for optimal photovoltaic distributed generators allocation in hybrid medium-voltage AC-DC electrical distribution system

Due to improvements in PV technology, electrical distribution networks are increasingly integrating PV-based distributed generation (PVDG) systems to reduce reliance on a single power source and the greenhouse gas emissions produced by conventional power plants. However, reverse power flow created by high power output from intermittent PVDG can increase system power losses and distort the voltage profile. Proper positioning and size are required to inject a PVDG during peak hours. The principal contribution of this paper is the application of a new efficient and reliable algorithm known as the equilibrium optimizer algorithm (EOA) to solve the optimal allocation of multiple PVDG units in hybrid medium-voltage AC-DC distribution networks. Comparing the EOA technique to the light spectrum optimizer (LSO), the energy valley optimizer (EVO), and the Archimedes optimization algorithm (AOA), the convergence characteristics and results show that the EOA approach was the quickest and best strategy to obtain the best solutions. The results from the simulation demonstrate that higher performance in terms of reducing power loss and improving voltage profile can be obtained by achieving the ideal size and positioning of the PVDG sources via an optimum energy management strategy.

The relevance of the issue of increasing the energy efficiency of the electricity conversion process in the current conditions of operation of the electricity distribution networks of Ukraine

The work sets out the basic conditions for the functioning of Ukrainian electric networks, including: the need to comply with the requirements of European integration; functioning in conditions of a deficit in generating capacity and market relations; changing the nature of electricity consumption; wear and tear of electric network equipment. The tasks that are associated with the conditions for functioning and require solutions for the reliable functioning of electric networks and further activities of Ukraine as a member of the European Union are indicated. The main tasks set out in the work include: improving the reliability of electricity supply; the need to increase the level of informatization, automation and energy efficiency. It is noted that on the way to solving the set tasks, the implementation of work on the reconstruction of electric networks is carried out, the basis for financing which is investment programs. The issue of improving the energy efficiency of the process of transforming electric energy under the current conditions of the functioning of the Ukrainian electric networks is revealed and analyzed. Based on the results of the analysis, it is established that the issue of improving the energy efficiency of the process of transforming electric energy is relevant at the time of reconstructing electric networks. It is indicated that the analysis of the energy efficiency of transforming electric energy can be carried out on the basis of a technical and economic model of the costs of transforming electric energy and the parameters that affect the size of the costs. It is noted that in the case of presenting the parameters included in the technical and economic models of costs for the transformation of electrical energy in the form of separate components, a competing effect is observed between the components. It is noted that the advisability of the formulation of general calculation methods for calculating the economic component during the review of the nominal power of transformers.

Optimization of distribution network mode by voltage under modern conditions

The article examines a fragment of a real distribution electrical network with a voltage level of 35–110 kV, consisting of a closed-loop 110 kV network with 35 kV branches. Real technical parameters of transformers installed at load nodes and the characteristics of power line conductors were used. The analysis took into account the actual load values, considering the operation of alternative energy sources connected to each node. Under the influence of these factors, some load nodes transform into generator nodes. A calculated equivalent circuit of this network was obtained, considering both longitudinal and transverse parameters. The calculation algorithm used for analyzing the operating modes of the electrical network is described. Numerical calculations of the normal operating modes of the network were performed using a simulation program, enabling the study of the impact of voltage regulation on electrical energy losses. Specifically, the study examined how changes in the voltage level at the base and balancing nodes of the 110 kV network affect the steady-state normal operation of the entire network. The research includes a comparative analysis of energy losses for different components: active and reactive power. This approach allows conclusions to be drawn regarding the influence of voltage regulation measures on energy losses. Based on the performed calculations, the quantitative and qualitative impact of increasing voltage levels at the base node on reducing energy losses across the entire network was determined. Thus, a conclusion was made about the potential for optimizing the operating mode of the electrical network through voltage regulation.

Evaluating the Total Active Power Loss under Different Placement of Photovoltaic Power Plants Using an Effective Northern Goshawk Optimization

This research presents a detailed evaluation of the total active power loss (TAPL) under different placements of photovoltaic power plants (PVPs) in the electric distribution network (EDN) IEEE 33-node. Three study cases have been conducted to serve the initial intention, including 1) optimizing both rated power and position of a PVP on selected EDN; 2) optimizing the positions of different quantities of PVPs independently to the grid with the same rated power, and 3) optimizing a sole PVP with a wide range of rated power. In all three study cases, northern goshawk optimization (NGO) is the primary search method for determining the essential results and data, especially in the last two cases, after proving its competitive performance in the first case compared to other methods. The results in study cases 2 and 3 indicated that for reaching the minimum value of TAPL, placing many PVPs independently on the grid simultaneously is the best implementation. Notably, the placement 7 PVPs with a total rated power of 2800kW has resulted in a significantly better TAPL than all the results in study case 3. However, for the situation where EDN can only adopt a sole PVP, all the data and results presented in study case 3 are also good academic material.

Revenue loss reduction in the electrical distribution networks using distributed generators: A case of Tanzania electrical distribution network

Most power losses in power systems occur within distribution networks, resulting in poor service quality, higher electricity costs for consumers, and revenue loss for utility companies. This study suggests strategies for minimizing these revenue losses by integrating distributed generators (DGs) into the distribution networks. The process involves identifying optimal locations and determining the best power output for each DG based on fluctuating power system variables, making it a complex NP-hard optimization problem. Therefore, this study proposes the use of metaheuristic algorithms to solve the DG power dispatch problem and reduce revenue losses in the electrical distribution networks. The proposed technique has been tested in the Tanzanian electrical distribution network using an hourly load profile and tariff D1, T1 and T2 users. Four metaheuristic algorithms were used, namely Golden jackal optimization (GJO), Grey wolf optimizer (GWO), Walrus optimizer (WO) and Marine Predators Algorithm (MPA). The algorithms were evaluated based on convergence profiles and computational times, with GWO performing the best. The study analyzed the impact of the number of DGs on revenue loss reduction, finding that the revenue losses decrease with the increase in the number of DGs. The analysis of the impact of the tariffs category on revenue loss reduction shows that integrating DGs reduced revenue loss by over 56% in all cases. These results demonstrate that DG integration can effectively decrease revenue losses in distribution networks.

Applied equilibrium optimizer algorithm for optimal planning multiple photovoltaic distributed generators in hybrid AC-DC distribution networks

Due to improvements in PV technology, electrical distribution networks are increasingly integrating PV-based distributed generation (PVDG) systems to reduce reliance on a single power source and the greenhouse gas emissions produced by conventional power plants. However, reverse power flow created by high power output from intermittent PVDG can increase system power losses and distort the voltage profile. Proper positioning and size are required to inject a PVDG during peak hours. This paper's main contribution is the application of a brand-new, dependable, and efficient algorithm called the equilibrium optimizer algorithm (EOA) to solve the problem of linking different numbers of PVDG units optimally in order to solve the optimal allocation of multiple PVDG units in hybrid medium-voltage AC-DC distribution networks. Comparing the EOA technique to the light spectrum optimizer (LSO), the energy valley optimizer (EVO), and the Archimedes optimization algorithm (AOA), the convergence characteristics and results show that the EOA approach was the quickest and best strategy to obtain the best solutions. The results from the simulation demonstrate that higher performance in terms of reducing power loss and improving various technical parameters can be obtained by achieving the ideal size and positioning of the PVDG sources via an optimum energy management strategy.

INFLUENCE OF ANOMALOUS VALUES ON THE ACCURACY OF FORECASTING LOSSES IN DISTRIBUTION NETWORKS

The relevance of the study is determined by modern trends in managing the operation modes of distribution electrical networks using Smart Grid technologies, as well as the need to reduce the costs incurred by distribution system operators for purchasing electricity. Accurate load forecasting results at network nodes for different forecasting horizons are crucial for this purpose. Sudden changes in network topology can increase the errors in loss forecasts as a single time series, negatively impacting network management efficiency and increasing the costs of electricity procurement to cover losses. The study proposes using forecasting methods based on artificial neural networks for the calculation and prediction of electricity losses, along with a comparison of these methods. The calculations were performed using data from one of Ukraine's distribution system operators, and the test electrical network was adapted based on the CIGRE scheme for modeling electricity losses. Since the nodal load data contained gaps and anomalies, a two-step data analysis algorithm was employed using the DBSCAN clustering method for detection and correction. As a result of loss calculations based on cleaned data, the error was reduced threefold compared to calculations based on load factors. Applying data analysis methods and forecasting methods based on artificial neural networks significantly improves the accuracy of loss calculations and minimizes errors.

Short-term prediction of power outages in electrical distribution networks

Predictive maintenance and reliability engineering are critical in industrial settings to enhance equipment performance and minimize unplanned downtime. This research, conducted within the machine learning framework, presents innovative solutions to the challenging problem of equipment failure prediction. The study creatively utilizes extensive datasets, including equipment records, weather conditions, and maintenance logs, to develop robust predictive models. Two distinct machine learning models are established for equipment and cables/lines, addressing the intricacies of class imbalances and missing data attributes. Model refinement, feature engineering, and interdisciplinary collaboration enhance predictive accuracy, precision, and recall. Notably, this research highlights the creative application of engineering knowledge and data science techniques, reasoning about complex equipment systems, and the importance of problem decomposition. The outcomes underscore the potential for real-time predictive maintenance in industrial contexts, offering substantial cost savings and improved equipment reliability. This research contributes to the evolving field of predictive maintenance and paves the way for future innovations in reliability engineering.

Fairness considered aggregation mechanism for consumers and prosumers in electricity distribution networks

Efficiently realizing a decarbonized energy system relies on effectively aggregating and equitably allocating cost to consumers and prosumers in distribution networks. This is crucial for fostering flexibility in transitioning to a sustainable, low-carbon energy system with increased renewable integration. This paper proposed an innovative aggregation mechanism for prosumers and consumers, emphasizing fairness and equality in their aggregation via proper pricing and efficient dispatching. The approach aims to enhance participant retention and attract investments by analysing characteristics, operation models, and operational impact on end users. The proposed two-level model underscores fairness and equity on different scopes. The upper-level model incorporates distributed nodal pricing (DNP) to facilitate fair dispatch among nodes, accounting for transmission congestion impacts. Simultaneously, the lower-level model addresses the efficiency-fairness trade-off, emphasizing equity in the energy sector and utilizing user satisfaction levels as the reference index. To validate the proposed aggregation mechanism, an example system is employed, with numerical studies illustrating its potential to enhance efficiency and fairness in the aggregation. A substantial improvement in the fairness index is observed when the efficiency-vs-fairness ratio changes from 1 to 0.9, indicating that even a modest emphasis on equity indicators can significantly enhance energy fairness.

An Algorithm for Locating the Coordinates of Unauthorized Consumers in the Distribution Electricity Network

This paper explores operational detection and control of unauthorized electricity consumption (UEC) in distribution electricity networks (DENs) with the Automated System for Commercial Metering of Electricity (ASCME) in operation. This problem pertains to the class of problems characterized by significant uncertainty about the current state of the object under study and the parameters of external disturbances, which are represented by unauthorized electricity consumers. In this regard, in order to reduce the level of uncertainty and obtain additional necessary information about the state of the object, the concept of a virtual model of the DEN is introduced. This concept is intended to describe the desired state of DEN, which is characterized by the absence of unauthorized consumers in the network. A new method and an algorithm for solving the formulated problem based on the concept of a virtual model of the DEN are proposed. Conditions for identifying the current state of the DEN are obtained. Vectors of effective values of currents and voltages of the virtual network are determined. A criterion for locating the UEC is formulated and a computational algorithm for the automated control method is developed. The findings allow enhancing the DEN reliability and can be used to create special software for the subsystem for locating the UEC coordinates in the distribution network as part of the ASCME.

GENETIC ALGORITHM-BASED OPTIMIZATION OF DISTRIBUTED GENERATOR PLACEMENT IN DISTRIBUTION NETWORKS TO MINIMIZE POWER LOSSES

An increase in electrical energy can lead to an increase in power losses and a decrease in voltage in the system. One of the efforts made to reduce power losses that occur in the distribution network is by placing the optimal Distributed Generation (DG) in the right location. Installation of DGs with suboptimal capacity and placement location can result in greater active power losses and further reduce voltage stability. This study discusses the optimization of DG placement in the electric power distribution network using genetic algorithm methods that are known to be effective in solving complex optimization problems. The IEEE 33 bus distribution system, which is a standard system frequently employed in power flow research, was the subject of the case study. The Newton-Raphson method is a commonly used iterative method for power flow analysis due to its accuracy in calculating power flow in electrical networks. The research findings indicate that the most suitable location for two DG units with an injection power of 6,626 MW is on buses 3 and 4. The placement of this DG has the potential to substantially mitigate power losses within the distribution network. The placement of two DG units on buses 3 and 4 results in a more significant improvement in system power losses than the placement of DG units on other buses. Power losses of buses 3 and 4 experienced power losses of 67724.69 MW, while system losses were reduced by 1137621.21 MW, or 5.61 percent.

Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach

Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. However, the current literature yields a lack of methods for efficient fault prediction and fast reconfiguration. To achieve this goal, this paper builds on DEN2DE, an adaptable routing and reconfiguration solution potentially applicable to SGs, and investigates its potential extension with AI-based fault prediction using real-world datasets and randomly generated topologies based on the IEEE 123 Node Test Feeder. The study applies models based on Machine Learning (ML) and Deep Learning (DL) techniques, specifically evaluating Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and Artificial Neural Network (ANN) as a DL method, evaluating each for accuracy, precision, and recall. Results indicate that the RF model with Recursive Feature Elimination (RFECV) achieves 94.28% precision and 81.05% recall, surpassing SVM (precision 89.32%, recall 6.95%) and ANN (precision 72.17%, recall 13.49%) in fault detection accuracy and reliability.

Unit commitment in solar‐based integrated energy distribution systems with electrical, thermal and natural gas flexibilities: Application of information gap decision theory

AbstractThe depleting oil reserves, air pollution and increasing energy demand, have overturned the focus of the scientific community to renewable energy sources. Among which the photovoltaic (PV) systems occupy more than half of the market share and are generally installed at the distribution level. The volatile and uncertain nature of these PV productions necessitates flexible resources in energy systems. To this end, the district heating systems have an outstanding flexibility on account of their high thermal inertia. This study investigates the optimal unit commitment scheduling for gas‐fired and non‐gas‐fired distributed generation units (NGU) in an integrated energy distribution system (IEDS) within the physical constraints of the electrical, natural gas and thermal energy distribution networks. Moreover, a planning‐based optimization framework is proposed to investigate the investment of battery storage systems in the electric distribution network under the high penetration of PV systems with the aim of enhancing flexibility and reducing the operating costs of the IEDS. In this framework, the information gap decision theory is deployed under risk‐averse and risk‐seeker strategies to deal with uncertain PV energy production. Additionally, the environmental emissions are considered in a multi‐objective approach. The IEDS is embodied through IEEE 33‐bus EDS, 20‐node natural gas network and an 8‐node district heating systems. Eventually, The proposed approach makes a noteworthy contribution to the advancement of solar energy systems in IEDS.

Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania

This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated to attribute outage events with weather conditions. A Bayesian change point analysis identified thresholds for these meteorological factors, indicating points at which the probability of outages increases sharply. The analysis reveals that wind gust speeds, particularly those exceeding 21 m/s, are significant predictors of increased outage rates. Precipitation also plays a critical role, with a 15-fold increase in the relative number of outages observed when 3 h accumulated rainfall exceeds 32 mm, and a more than 50-fold increase for 12 h snowfall exceeding 22 mm. This study underscores the substantial contribution of lightning discharges to the number of outages. In forested areas, the influence of meteorological conditions is more significant. Furthermore, the research emphasizes that combined meteorological factors, such as strong winds accompanied by rain or snow, significantly increase the risk of outages, particularly in these forested regions. These findings emphasize the need for enhanced infrastructure resilience and targeted preventive measures to mitigate the impact of extreme weather events on Lithuania’s power grid.