Electric Distribution Research Articles

Thermoelectric generator efficiency: An experimental and computational approach to analysing thermoelectric generator performance

TEGs are devices that convert heat directly into electricity through the Seebeck effect, offering a promising solution for waste heat recovery in various industries. In this research, COMSOL Multiphysics 6.0 was used to conduct a comprehensive 3-dimensional computational study of TEGs. Integrating thermal and electrical models in COMSOL facilitates a detailed understanding of the thermoelectric phenomenon. Applying six distinct temperature gradients, temperature and electrical distribution, power output, and efficiency of the TEG was thoroughly analysed. Experimental validation confirms strong agreement between simulation and experimental data, emphasizing accuracy. The average efficiency for the TEG at 1 Ω load is 3.12 %, increasing to 3.62 % for a 2 Ω load. The relative error between the computational model and the experimental model was 5 % for open circuit, 12.56 % for closed circuit at 1 Ω, and 12.14 % for closed circuit at 2 Ω, affirming the accuracy of the computational approach. Therefore, the computational model is validated by experimental results.Moreover, the findings highlight the relationship between external load resistance and power output, revealing that the maximum output power was achieved when the external load resistance matched the internal load resistance at 2 Ω. This work also significantly contributes to advancing the computational modelling of TEGs, validated through rigorous experimental analysis.

A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network

AbstractThe adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1.

Magnetic Sensor Array for Electric Arc Reconstruction in Circuit Breakers.

Noninvasive imaging of circuit breakers under short-circuit testing is addressed by recording the magnetic field produced over an array of external sensors and by solving an inverse problem to identify the causing current distribution. The temporal and spatial resolution of the sensing chain are studied and implemented in a physical set-up. A wire model is adopted to describe electrical current distribution. Additionally, the simpler, more direct approach to evaluating the passage of electric current in front of sensors is proposed. The dynamics of suitable approximating models of the electric arc that forms across contacts is obtained and agrees with multi-physical simulations and with experimental time histories of current and voltage. The two methods are flexible and allow the analysis of different types of circuit breakers.

The Impact of Participation Ratio and Bidding Strategies on New Energy’s Involvement in Electricity Spot Market Trading under Marketization Trends—An Empirical Analysis Based on Henan Province, China

As new-energy electricity increasingly enters the post-subsidy era, traditional fixed feed-in tariffs and guaranteed purchase policies are not conducive to the optimal allocation of large-scale, high-proportion new-energy power due to the high pressure of subsidy funds and the fairness issues of power-generation grid connection. Encouraging new energy to participate in electricity market transactions is considered an effective solution. However, existing studies have presupposed the adverse effects of new energy in proposing market mechanism optimization designs for new-energy participation without quantitative results to support this, which is not conducive to a true assessment of the comprehensive impact of individual instances of new-energy participation in the market. To this end, this study, based on the actual experience and data cases of China’s electricity spot market pilot provinces, considers both unit commitment and economic dispatch in the electricity distribution process, and constructs a two-stage optimization model for electricity spot market clearing. According to the differences in grid connection time and the construction costs of new-energy power, differentiated proportions of new-energy participation in the market and bidding strategies are set. By analyzing the quantitative results of new energy participating in spot market transactions under multiple scenarios, using both typical daily data for normal loads and peak loads, the study provides theoretical support and a data basis for the optimized design of market mechanisms. The research results show that there is a non-linear relationship between the scale of new energy entering the market and its bidding strategies and market-clearing electricity prices. In the transition phase of the low-carbon transformation of the power sector, the impacts of thermal power technology with a certain generation capacity and changes in the relationship between power supply and demand on electricity prices are significant. From the perspective of the individual interests of new-energy providers, the analysis of their bidding strategies in the market is important.

Comparative experimental study of structural behavior and economic aspects of circular pre-stressed and H-shaped reinforced concrete electrical power distribution poles

Comparative experimental study of structural behavior and economic aspects of circular pre-stressed and H-shaped reinforced concrete electrical power distribution poles

Predictive Modeling and Forecasting of Solar Power Generation Using Machine Learning Techniques

The purpose of this research study is to investigate the predictive modelling and forecasting of solar power generation by utilising a variety of machine learning approaches. The paper addresses the significant obstacle that is presented by the variable production of solar energy, which is a barrier to the effective incorporation of solar power into the network of electrical power distribution systems. This research investigates the application of machine learning models such as Bayesian Ridge Regression, Gradient Boosting, and Linear Regression. The research makes use of historical meteorological data and solar power output to investigate the application of these models. The effectiveness of these models in estimating the amount of solar energy that will be produced under a variety of different weather situations is being investigated. Based on the findings, it is evident that the application of machine learning techniques has the potential to considerably improve the accuracy of solar power projections. This, in turn, will facilitate better grid integration and promote wider adoption of solar energy products. Through the demonstration of the potential of advanced machine learning approaches in enhancing the reliability and efficiency of solar power generation, this study makes a contribution to the expanding body of literature on the forecasting of renewable energy sources.

Time series processing-based malicious activity detection in SCADA systems

Many critical infrastructures, essential to modern life, such as oil and gas pipeline control and electricity distribution, are managed by SCADA systems. In the contemporary landscape, these systems are interconnected to the internet, rendering them vulnerable to numerous cyber-attacks. Consequently, ensuring SCADA security has become a crucial area of research. This paper focuses on detecting attacks that manipulate the timing of commands within the system, while maintaining their original order and content. To address this challenge, we propose several machine-learning-based methods. The first approach relies on Long-Short-Term Memory model, and the second utilizes Hierarchical Temporal Memory model, both renowned for their effectiveness in detecting patterns in time-series data. We rigorously evaluate our methods using a real-life SCADA system dataset and show that they outperform previous techniques designed to combat such attacks.

A New Application with Conditional Risk Matrix and Decision Rules: An Assessment of the Sustainability and Performance Electric Power Company

Abstract The 2030 Agenda for Sustainable Development of the United Nations General Assembly focuses on economic development, poverty cessation, environmental sustainability, and good governance at all levels, including peace and safety. For electric power distribution concessionary companies in Brazil, it is necessary to evaluate their sustainability and performance according to the parameters laid by the National Electric Power Agency (ANEEL). For that purpose, sixty-two indicators were assembled that were directly and indirectly associated with sustainability for 38 companies in the period–2013-2021. This study proposes a new application with a conditional risk matrix and decision rules aimed at the integrated assessment of sustainability and performance of electric power companies using the conditional probability of indicators in the ascent or descent of the companies’ classification. A set of “if..., then...” decision rules were inferred by the algorithm VC-DomLEM using concepts from the Rough Sets Theory and the Dominance principle, Dominance-based Rough Set Approach (DRSA).

Preface

Against the backdrop of the rapidly evolving global energy transition, power, grid and energy storage technology are receiving unprecedented attention as key forces in promoting cleaner, smarter and more efficient energy sources. The Proceedings of the 2024 2nd International Conference on Power, Grid and Energy Storage (PGES 2024) brings together the latest research results and cutting-edge explorations of experts and scholars in the field of power energy from all over the world, demonstrating the vigorous vitality and unlimited potential of this field. The PGES 2024 took place virtually in Kunming, China from June 21st to 23rd, 2024. The conference was organized in order to provide an international academic forum for promoting research and other academic activities as well as developing research cooperation in field of power, grid and energy. It is worth mentioning that the number of international participants in this conference hit a record high. Experts and scholars from Canada, Malaysia, Italy, New Zealand, China, and many other countries and regions gathered together to contribute to the development of power, grid and energy. They brought cutting-edge technologies and research results in their respective fields, injecting new vitality and momentum into the conference. The conference fully demonstrated the characteristics of globalized exchanges. Participants from different countries and regions promoted international academic exchanges and cooperation by sharing their research achievements and experiences. At the same time, the conference also invited a number of internationally renowned experts and scholars to give keynote speeches, bringing participants cutting-edge academic views and profound insights. The keynote speakers include Hossam A. Gabbar (full Professor of Ontario Tech University, Canada), Tao Lin (Professor of Wuhan University, China), M A Hannan (Professor of Sunway University, Malaysia), Pierluigi Siano (Full Professor of University of Salerno, Italy), Udaya Madawala (Professor of The University of Auckland, New Zealand), as well as Guobing Zhou (Professor of North China Electric Power University, China). In addition, the conference also organized oral and poster presentations, which provided participants with more opportunities for in-depth exchanges and discussions. These activities not only helped to enhance the friendship and cooperation among the attendees, but also laid a solid foundation for promoting international exchanges and cooperation in the field of information education and artificial intelligence. The topics of papers collected and undergone rigorous peer review in the Proceedings of PGES 2024 can be classified as follows: Power Systems and Automation, Energy Storage Equipment and Systems, Smart Grid Information Engineering, Electrical Power Generation Technology, Power Transmission and Distribution, Communication and Control in The Power System, etc. We would like to express our sincere thanks and gratitude to all the keynote speakers, authors, committee members and other colleagues for the success of PGES 2024, which has given rise to this present volume of selected papers. We would also like to acknowledge the staff of Journal of Physics: Conference Series for their effective work to make this Proceedings published. The Committee of PGES 2024 List of Committee Member is available in this Pdf.

Estimating primary substation boundaries and the value of mapping Great Britain's electrical network infrastructure

Localised data aggregation in many countries including Great Britain (GB) is typically performed at a geographical level with polygon boundaries that have a robust and trusted governance system in place. This means there is confidence in a process to create a set of polygons that have unique identifiers coupled to geographical areas, and the ability to have these updated through a defined code of practice. Examples found across many countries are in the delivery of post, such as post codes and zip codes, and of the definition of census areas and municipal boundaries. The confidence in these boundaries allows different data to be aggregated by third parties, which itself provides greater amounts of data over comparable geographical areas to enhance wider analysis and decision making. As helpful as these polygons are for certain types of analyses, they are not specifically defined for energy systems analysis. Here we combine publicly available datasets published from the six regional electricity Distribution Network Operators of GB to produce a new geospatial dataset with 4436 unique polygons defining the areas served by electrical primary substations. An example demonstrating the value of these polygons is given to link these polygons with postcode level open government datasets on domestic energy consumption (2015–2020) from the Department of Energy Security and Net Zero (DESNZ). The resulting new data reflects energy statistics aggregated to the geographical areas served by each primary electrical substation across Great Britain. The significant value of the generated data is demonstrated by the quantification of the domestic annual heating demand presently met by natural gas across different primary substation areas which varies from an average of 5100 kWh to 28,900 kWh per property. This highlights how the rate of electrical capacity expansion will have to differ by geography under an electrified heating scenario. Therefore, we believe there is a compelling argument for all countries to set up processes to create and update polygons that have a meaningful relationship to energy systems. This would allow more accurate energy systems analysis to be performed, ultimately leading to an accelerated or potentially lower cost transition to a net zero world.

Quantum Informative Analysis in Smart Power Distribution

Advancements in the Internet of Things (IoT) paradigm have greatly improved the quality of services in the electricity industry through the integration of smart energy distribution and dependable electric devices. Conspicuously, the current research introduces a method for managing electricity consumption in smart residences using IoT-Fog technology, focusing on efficient energy allocation and real-time energy needs. The study specifically examines the effectiveness of electricity grid sub-stations in distributing energy using fog computing technology. By utilizing a Quantum Computing-assisted approach, optimal energy distribution is achieved by calculating a novel Electricity Usage Measure (EUM) based on actual energy usage patterns of smart homes. Furthermore, the Quantumized Neural Network (QiM-NN) technique is developed to forecast the electricity distribution over grid substations. For performance assessment, 4-month data is collected using four smart houses. Comparative analysis with existing data assessment techniques illustrates the effectiveness in terms of Temporal Delay (6.33 ms), Optimization Performance (Specificity (93.00%), Sensitivity (90.86%), Precision (96.66%), Coverage (96.66 %)), Reliability (93.76%), and Stability (71%).

Study of the Management of the Electrical Energy Production and Distribution System Within the National School of Teachers of Mamou, Guinea

With the energy transition, marked essentially by the mass integration of energy production based on renewable resources, the missions and challenges of electrical energy distribution networks are evolving. This study is part of this dynamic, its objective is the study of the management of the production and distribution system of electrical energy within the National School of Teachers of Mamou. It emerges from this study that the supply of electrical energy to the National School of Teachers of Mamou is ensured by a hybrid system of three power sources: photovoltaic solar fields, Generator Group and Electricity of Guinea. The current electrical energy requirements of the Mamou NST are 40 kW. The total power of the installed photovoltaic solar fields is 70 kWp; the Generator used has a power of 10 kVA; the site’s Electricity of Guinea network is made up of transformers, cabin substations and protective equipment. The electricity distribution network is characterized by: Four (4) 250 A circuit breakers; a 32 A circuit breaker for the departure of lamps, sockets and fans; a 10 A circuit breaker for the lamps; a 10 A circuit breaker for the fans; a 16 A circuit breaker for the sockets and an 800 A mechanical inverter. The study shows that the power of photovoltaic solar fields is largely sufficient to cover the current electrical energy needs of the National School of Teachers of Mamou.

Identification of Power Quality Disturbances in Electrical Distribution System using Fast Fourier Transforms and Super Learner Ensembles

Currently, the use of non-linear loads and equipment, as well as renewable energy sources injected into the power system, tends to increase. As a result, the waveform of the electrical signal changes, and distortion occurs in the distribution system, which affects the quality and reliability of the electrical system. Importantly, sometimes this leads to malfunctions in protection equipment. This paper presents the algorithm for power quality disturbance (PQD) identification in electrical distribution systems, which involves three main steps: (1) Generating simulated waveforms using a signal processing approach; (2) extracting features using the Fast Fourier Transforms (FFT) technique; and (3) identifying the type of PQD using Super Learner Ensembles (SLE), which employs cross-validation to assess the performance of multiple machine learning models. Subsequently, the model’s efficiency is verified and tested using data from electronic energy meters installed in the distribution system of the Provincial Electricity Authority (PEA). The accuracy resulting from synthetic and experimental data sets is 99.90% and 99.69%, respectively. The results indicate that the model performs well in identifying power quality disturbances and achieves high accuracy.

Beyond the Meter: Understanding Consumer Satisfaction with Torrent Power in Bhiwandi

This thesis investigates consumer satisfaction with Torrent Power, an electricity distribution company operating in Bhiwandi, Maharashtra, India. It examines how various factors, including pricing, billing transparency, service quality, and grievance redressal, influence consumer perceptions and satisfaction. By employing a mixed-methods research design, this study explores the complexities of consumer-utility relationships in the Indian context. Findings reveal that while customers appreciate certain aspects of Torrent Power’s services, concerns about pricing, billing clarity, and power outages significantly impact overall satisfaction. Recommendations are provided to enhance customer experience and improve Torrent Power’s service delivery. This research contributes to the growing body of literature on consumer satisfaction in the electricity sector by providing insights into the specific challenges and opportunities faced by a distribution company operating in a developing country context. The findings of this study have implications for policymakers, industry practitioners, and consumer advocacy groups seeking to improve the performance of electricity distribution utilities.

Fuzzy Analytic Hierarchy Process–Technique for Order Preference by Similarity to Ideal Solution: A Hybrid Method for Assessing Vegetation Management Strategies under Electricity Distribution Lines to Prevent Deforestation Based on Ecosystem Service Criteria

This study evaluated vegetation management (VM) strategies under electricity distribution lines (EDLs) through ecosystem service (ES) criteria. Deforestation, worsened by insufficient VM practices, poses a threat to ecosystem stability. Using a hybrid FAHP (Fuzzy Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, ten VM strategies were assessed based on 15 ES criteria. The FAHP results identified biodiversity, timber resources, and erosion control as the most crucial criteria due to their significant weights. The TOPSIS analysis determined that VM6 (creation and restoration of scrub edges) was the most effective strategy, achieving a value of 0.744 for reducing deforestation and enhancing energy security. VM6 helps preserve forest cover and protect infrastructure by creating a “V”-shaped structures within the EDLs corridor. This study underscores the importance of ES-oriented VM strategies for sustainable vegetation management and deforestation mitigation. It also highlights the need for incorporating scientific, ES-based decision support mechanisms into VM strategy development. Future research should expand stakeholder perspectives and conduct a comprehensive assessment of ESs to ensure that VM strategies align with ecological and socio-economic sustainability. This study provides a framework for improving VM practices and offers directions for future sustainable energy management research. This study focuses exclusively on ecological criteria for evaluating VM strategies, neglecting other dimensions. Future research should use methods like ANP and fuzzy cognitive maps to explore inter-dimension relationships and their strengths. Additionally, employing SWARA, PIPRECIA, ELECTRE, and PROMETHEE for ranking VM strategies is recommended.

Application of data analysis techniques for characterization and estimation in electrical substations

With the continued growth of smart grids in electrical systems around the world, large amounts of data are continuously being generated and new opportunities are emerging to use this data in a wide variety of applications. In particular, the analysis of data from distribution systems (such as electrical substations) can lead to improvements in real-time monitoring and load forecasting. This paper presents a methodology for substation data analysis based on the application of a series of data analysis methods aimed at three main objectives: the characterization of demand by identifying different types of consumption, the statistical analysis of the distribution of consumption, and the identification of anomalous behavior. The methodology is tested on a data set of hourly measurements from substations located in various geographical regions of Colombia. The results of this methodology show that the analysis of substations data can effectively detect several common consumption patterns and also isolate anomalous ones, with approximately 4% of the substations being identified as outliers. Therefore, the proposed methodology could be a useful tool for decision-making processes of electricity distributors.

Flash Point Improvement of Mineral Oil Utilizing Nanoparticles to Reduce Fire Risk in Power Transformers: A Review

Transformers are crucial equipment in electrical distribution systems but have a significant potential for failure. Insulation materials, including transformer oil (TO), play a primary role in transformer failures. A fire involving the TO can lead to a large explosion, causing the main tank to rupture and resulting in extensive damage to the entire transformer and the surrounding area. Mineral oil (MO) is the most widely used type due to its availability and relatively low cost compared to other types of oil. However, MO has a critical disadvantage, which is its very low flash point. The low flash point makes MO highly flammable. When the oil fires in an enclosed space, such as a transformer tank, the pressure inside the tank increases, leading to a large explosion. Therefore, research on increasing the flash point of MO is highly necessary. The application of nanotechnology is a promising approach to increasing the flash point of base fluids. Research on the effect of nanoparticles (NPs) on flash points is very limited in the literature; thus, there is significant potential for further research in this field. The majority of studies indicate an increase in flash points with the addition of NPs to MO. There is only one study that shows a decrease in flash point, which is −1.33% compared to MO. From all the reviewed studies, it can be concluded that NPs are a potential solution to increase the flash point of MO. Despite their benefits, NPs require a thorough examination of health and environmental impacts, along with proper waste management, to ensure their advantages.

Reducing non-technical losses in electricity distribution networks: Leveraging explainable AI and three lines of defence model to manage operational staff-related factors

This study presents a multidisciplinary approach involving Explainable Artificial Intelligence (ExAI) and operational risk management to reduce Non-Technical Losses (NTL) in electricity distribution. It empirically explores how the activities of employees of utility companies contribute to NTL, a phenomenon often overlooked in existing empirical research. An ensemble classification algorithm is used to analyse utility operations data, and the SHAP explainability technique establishes the predictive significance of staff activities for NTL. Subsequently, these staff activities are mapped into risk cells using the BASEL II and III operational risk definitions, and the Three Lines of Defence (3LoD) model is developed for optimizing electricity distribution. The paper makes three original contributions to the literature: first, it empirically links staff operations to NTL; second, it maps NTL causes to Basel II/III operational risk categories; and finally, to the best of the authors’ knowledge, it is the first study to use the 3LoD model for electricity distribution optimization.

Recloser optimization in electrical distribution systems using reliability analysis with heuristic algorithm

Recloser optimization in electrical distribution systems using reliability analysis with heuristic algorithm

Optimal design of sizing and allocations for highway electric vehicle charging stations based on a PV system

The world’s demand for fossil fuels has recently increased significantly for both transportation and electric power generating sectors. Using these resources not only results in high costs and depletion of them but also increases greenhouse gas emissions and pollution. The electric vehicle (EV) market is growing globally, aiming to face climate change due to greenhouse gas (GHG) emissions and to reduce reliance on fossil fuels. However, the long charging time of EVs and the shortage of charging outlets limits the global adoption of EVs, especially on highways where the problem of accessibility to the electricity distribution grid appears. These issues can be faced by the good planning of charging infrastructure. However, this planning is a multidisciplinary field that includes electricity generation, transportation networks, EVs’ characteristics, and driver behavior. A methodology to provide the optimal locations and sizing of electric vehicle charging stations with their own electricity generation and storage using photovoltaic (PV) and energy storage systems on highways considering different factors is proposed in this paper. This paper takes a section of the western desert highway in Egypt connecting Assiut and Cairo cities as a case study. Four scenarios are proposed for the design of EV charging stations’ locations and sizing which are centralized charging stations, two-way charging stations, utilizing oil stations’ locations, and distributed fixed sizing charging points with a comparison between them. The work also discusses the potential effects of highway slope, wind speed, and number of passengers on the location problem. The results can be used to optimize the design of EV charging stations along highways for a completely sustainable system.