Electricity Distribution Sector Research Articles

Artificial Intelligence-Assisted Methodology for Dataset Reduction Applied to the Establishment of Power Interruption Limits in Brazil

Definitions of methodologies to regulate the quality of electricity supply services are a topic under active discussion in Brazil and worldwide. There are various ways to define limits and quality service goals. In Brazil, the regulation of limit indicators for consumer unit sets is carried out by the National Electric Energy Agency. Its latest revision took place in 2014, under the framework of Public Announcement No. 29/2014. The primary contribution of this research is the proposition of an artificial intelligence-assisted methodology, specifically utilizing machine-learning techniques capable of organizing and selecting the most relevant attributes for representing similar consumer sets. Tests were conducted with real data from the 2020 system. The results demonstrated that this methodology can select attributes from different categories, achieving data representativeness and clustering scores superior to those attained with attributes selected by the current ANEEL methodology. Furthermore, the proposed methodology exhibits greater replicability compared to the current approach. These outcomes contribute to the modernization of quality regulation in the electricity distribution sector, benefiting all stakeholders in the industry.

Hybrid fuzzy and gated recurrent network based artificial intelligence approach for fault diagnosis and prognosis of transformers using dissolved gas analysis

In the electrical energy transmission and distribution sector, power transformers play an important role. Early fault diagnosis and prognosis are essential to ensure continuous operation and also to prepare a proper maintenance schedule based on the requirements. The occurrence of a fault in the transformer will lead to the formation of various gases inside the transformer tank. For fault diagnosis in the transformer, Dissolved Gas Analysis (DGA) is an excellent method. An Artificial Intelligence (AI) based fault diagnosis and prognosis system using dissolved gases in transformer oil is helpful to predict the health state of the transformer well in advance. Hence, based on the fault severity level, the remaining useful life of the transformer, fault type and current state of the transformer can be estimated effectively by imparting AI to the existing system. A Two-Tier Fuzzy Logic Controller (TTFLC) is proposed in this article to find the type of fault and health index (HI) of the transformer. For further fault prognosis, an effective Gated Recurrent Network (GRN) based deep learning enabled future learning estimator is used for predicting the Criticality Index (CI) of the Transformer. The performance of the proposed method is evaluated for both data from the IEEE data set and expert data collected from the southern Tamil Nadu region. The proposed system shows better results even in multivariate, complex process systems. The diagnosis accuracy of the proposed system is obtained as 95.28% and it compared with conventional methods such as Rogers Ratio Method (RRM), Duval Triangle Method (DTM) and Duval Pentagon Method (DPM) and other AI based methods such as Radial Basis Neural Network (RBNN), k-nearest neighbors (KNN). The diagnosis accuracy of other conventional and AI based methods are less than 90% for the collected dataset.

GENERATIVE AI IN ELECTRICITY DISTRIBUTION: A QUALITATIVE EXPLORATION

Purpose- The purpose of this study is to explore the application and potential of generative artificial intelligence (AI) within the context of electricity distribution companies. The study aims to investigate how these advanced AI technologies, particularly Generative Adversarial Networks (GANs), can address the sector's pressing challenges, such as load forecasting, power outage prediction, and preventive maintenance. Methodology- The study employs a qualitative case study methodology, providing an in-depth analysis of real-world applications of generative AI within electricity distribution companies. The selection of cases represents a wide variety of experiences and contexts, facilitated by both primary data collected through semi-structured interviews with key personnel within the organizations and secondary data derived from an extensive review of company reports, public documentation, and industry publications. The gathered data was systematically analyzed using thematic analysis to identify and report recurring patterns and themes. Findings- The analysis reveals that generative AI has been successfully implemented in various operational aspects of electricity distribution. The first case study presents how GANs have significantly improved load forecasting accuracy in an Eastern Turkish electricity distribution company. The second case study from Southern Turkey showcases how GANs have been used for predicting power outages, thereby aiding efficient resource allocation, reducing downtime, and enhancing customer satisfaction. Lastly, the third case from Northern Turkey demonstrates how generative AI has contributed to effective preventive maintenance of distribution equipment, improving overall system reliability. Conclusion- Based on the analysis findings, it may be concluded that generative AI holds transformative potential for the electricity distribution sector. While the implementation of these technologies is associated with challenges such as data privacy, security, and the requirement of technical expertise, the benefits in terms of improved accuracy, system reliability, and resource efficiency provide a strong justification for their adoption. The paper underlines the importance of an interdisciplinary collaboration between AI researchers, electrical engineers, industry professionals, and policymakers for furthering the adoption of these technologies. As the field of generative AI continues to evolve, it is expected to have an even greater impact on the electricity distribution sector, thereby opening up exciting opportunities for future research and application. Keywords: Generative artificial intelligence (ai), electricity distribution companies, generative adversarial networks (gans), load forecasting, outage prediction, preventive maintenance JEL Codes: M40, M41

A balanced scorecard to enable electricity distribution sector reforms in India

The performance of the State-owned electricity distribution companies (DISCOMs) that distribute 96% of the electricity in India is critical for the country’s energy security. However, the current studies are not suitable for assessing the performance of India’s DISCOMs in an integrated manner since their underlying models are fragmented. This study presents the development and application of an integrated FOBC framework which is a balanced scorecard including 32 key performance indicators related to finance, operations, business processes, and customer service. This framework is used to benchmark the performance of 40 DISCOMs in 17 States on a like-to-like basis leading to the identification of nine DISCOMs for restructuring. The FOBC framework can be used by the regulators and the government to benchmark DISCOM performance in a holistic manner. This study will be useful for policymakers to implement power distribution sector reforms to enhance energy security while decarbonizing the power sector efficiently.

Nexus Between Risk in Outsourced Activities and Operational Performance of Electricity Distribution Company in Nigeria

The emergence of globalization has made outsourcing become one of the most used corporate strategies for providing top‐notch services to various service industry which the electricity distribution sector is not left out. The study aims at examining the outsourced risk and operational performance of electricity distribution company in Nigeria using Benin Electricity Distribution Company (BEDC) as a case study. The study employed survey research design with a sample size of 14 business managers who were selected purposively. The data collected were analyzed using correlation analysis to determine the risk inherent outsourced activities posed are associated with operational performances. The findings of the study shows that the Pearson correlation value r = 0.333 between security & privacy risk and operational performance indicates a positive and low relationship at (P=0.245; P>0.05) significant level, the Pearson correlation value r = 0.311 between operational risk and operational performance indicates a positive and low relationship at (P=0.279; P>0.05) significant level , and the Pearson correlation value r = 0.543 between financial risk and operational performance indicates a positive and moderate at (P=0.045; P<0.05) significant level in the study area. In line with the findings, the study concludes that all risks factors are contributing element to operational performance except for financial risk having a moderate influence on operational performance in the study area and recommends that as much as risk is an important factor in business, the services of outsourced vendors should be cautioned so as not to expose the organization to too much of risk.

Efficient resource utilization of the electricity distribution sector using nonparametric data envelopment analysis and influential factors

This study aims to investigate the efficient resource utilization of the electricity distribution sector in Pakistan and identifies the influential factors affecting it. In Pakistan, there is excess production capacity and under-utilized power plants, which pose a challenge to the economy due to their burden on the exchequer account and the high power costs. The present study employs the most recent 11 distribution firms panel data from Pakistan for the period 2016–2020 to evaluate the performance ranking of distribution utilities. The study employs standard frontiers, composite indexes, and a novel DEA-windows analysis to measure the efficiency of the distribution utilities. The results of the analysis show that electricity sales, consumer density, and industrial consumers have a positive impact, while distribution losses have a negative association with the efficiency of the distribution utilities. These findings provide valuable insights for policymakers and industry stakeholders in improving the resource utilization and overall performance of the electricity distribution sector. The study highlights the implications of the results in terms of reducing greenhouse gas emissions, which are one of the main contributors to global warming. In conclusion, this study provides an essential contribution to the literature on efficient resource utilization in the electricity distribution sector in Pakistan.

Estimation of Electric Arc Incident Energy in Electric Power Distribution Grids

The occurrence of an electric arc is an inherent possibility when working in a live line. Thermal risk is considered one of the most relevant risks associated with this type of event, so every worker who performs daily activities in the electricity distribution sector is subject to this risk to a lesser or greater degree. The quantification of the thermal risk is based on estimating the energy generated by the electric arc, and focuses on a surface called incident energy. Therefore, the incident energy estimate is used to define thermal protection strategies applicable to workers who carry out activities in the distribution sector. In light of this, the present work proposes a review of incident energy estimation methods for electric arcs in electric power distribution grids, highlighting the applicability of each of these methods in terms of voltage, short circuit type, and environment. Finally, five case studies are presented in which adequate methods are used to estimate the incident energy. Their results concerning incident energy and selected protective equipment (PPE) are compared. From the review of existing incident energy estimation methods in the literature and the case studies presented, the importance of selecting a suitable method for the type of system analyzed, considering electric, spatial, and intervention aspects, is observed. Finally, considering that the primary objective of incident energy estimation is to determine the need and selection of arc-rated PPE, the selected estimation method must be aligned and integrated with the clothing selection procedures and applicable PPE.

Reliability Estimation of Low-Voltage Power Distribution Systems

Many accidents always happen in the electricity distribution sector, which requires the use of control strategies in this field. Therefore, this study presented a risk assessment method based on a fuzzy Bayesian network (FBN) and William Fine method in low-voltage power distribution systems to reduce the structural uncertainties of the studies and examined the reliability of these systems to achieve proper performance in emergencies. The first step is to develop a fault tree (FT) and event tree (ET) in bow tie (BT) format. In this study, fuzzy theory and expert judgment were used to qualitatively analyze the root events affecting the system failure and determine the relevant probabilities. Then, deductive and inductive reasoning was done after determining the likelihood of basic events (BEs) and establishing the Bayesian network (BN). Then, the final risk was estimated using William Fine’s method and fuzzy logic. The results of a case study in a low-voltage power distribution system showed that there were 36 BEs and 20 intermediate events (IEs) for system failure, and the reliability of the system was obtained as 0.96654476 and 0.96654476 based on the FT and FBN, respectively. According to the results of BE25 (no installation of the earth) and BE18 (contact of metal objects with the live wire), the most critical events were based on FBN. The results showed that FBN simulation and its combination with static methods such as BT and William Fine provide a suitable method for the accurate identification of distribution systems. Therefore, the approach of the present study can help the decision makers and analysts of the electricity industry to prevent possible failures due to system changes.

Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market

In stochastic frontier analysis, the conventional estimation of unit inefficiency is based on the mean/mode of the inefficiency, conditioned on the composite error. It is known that the conditional mean of inefficiency shrinks towards the mean rather than towards the unit inefficiency. In this paper, we analytically prove that the conditional mode cannot accurately estimate unit inefficiency, either. We propose regularized estimators of unit inefficiency that restrict the unit inefficiency estimators to satisfy some a priori assumptions, and derive the closed form regularized conditional mode estimators for the three most commonly used inefficiency densities. Extensive simulations show that, under common empirical situations, e.g., regarding sample size and signal-to-noise ratio, the regularized estimators outperform the conventional (unregularized) estimators when the inefficiency is greater than its mean/mode. Based on real data from the electricity distribution sector in Sweden, we demonstrate that the conventional conditional estimators and our regularized conditional estimators provide substantially different results for highly inefficient companies.

Solar Cell Effects on Electrical Power Networks

Recently, interest in solar energy has increased for many reasons, however, connecting solar power stations in distribution networks may cause many problems, so it was necessary to study the impact of connecting photovoltaic systems to distribution networks to provide solutions feasible in practice. This study includes analyzing the performance of a solar power plant with a capacity of 17.2 kWh connected to the low voltage network of the distribution networks according to Egyptian PV-LV code-2014, simulation using MATLAB/Simulink, and cost analysis for connecting SSPV 17.2 kW to LV distribution networks. The system model was built on MATLAB/Simulink and simulated under daily weather conditions to test the system's operational performance. Power quality parameters are measured using C.A. 8336 power quality analyzer at the inverter output side for the PV system at the Minya Electricity Distribution Sector in Minya Governorate, Egypt. The system produces a 29.314 MWh and reaches its initial cost in 7.2 years. The electrical energy produced by the system is directly injected into the low voltage network for loads of the Minya Electricity Distribution Sector building without batteries or storage devices (on-grid).

The VAT rate hike, government spending, economic growth and sectoral effects: CGE analysis

The paper examines the sectoral and macroeconomic effects of the increase in the VAT rate and the additional increase in government spending. For this porpoise, the computable general equilibrium model of the Russian economy has been developed. We have found that the VAT rate hike leads to the output decrease in most industries, including the manufacturing sector. Despite the positive macroeconomic effect from the government spending increases, the sectoral structure is deteriorating, and there is not enough additional growth in demand to offset the negative effect of the increase in the VAT rate, in particular for the industrial sector. In the VAT rate hike and government spending increase scenario, the negative effects of the VAT hike are exacerbated for sectors such as agriculture and trade due to higher costs of production factors as a result of additional government demand. Favorable conditions are being created for the raw materials and the construction sectors as well as the electricity, heat, water and gas distribution sector that have already developed dynamically. Beyond these sectors, the multiplier effects of government spending do not actually extend. The industrial sector is failing to realize its potential. Balanced and sustainable economic growth thus requires additional efforts on the part of industrial policy.

Estimation of Generated Electricity for the Solar Power Plant by Using Polynomial Regression

The estimation of electrical energy that can be supplied from the renewable energy sources becomes a weighty matter regarding to increasing the share of these sources in the electrical energy sector all over the world. The installed power of power plants in Turkey has reached 100334 MW. Depending on the installed power, progress has been made in the electricity transmission and distribution sector over the years. Thus, there exist 21 distribution regions in electricity distribution in Turkey. Some distribution regions have reached 25 MWs in terms of solar installed capacity. While estimating the electrical energy demand for any distribution region, the total electrical energy that can be supplied by the solar power plants (SPP) in that region should be taken into account. In accordance with the legislation in Turkey, the authorized electricity retail sales company in each distribution region has to enter the daily estimation of the electrical energy, to be obtained from SPPs, located in its own region, on the web site of EPIAS (Energy Markets Operation Joint Stock Company). In this study, the estimation of electricity generation of the 990 kW solar power plant belonging to Osmaniye Special Provincial Administration and located in the town of Cevdetiye for the days 24.08.2021 and 25.08.2021 was carried out with the polynomial regression algorithm. Approximately 85% performance metric was got from the polynomial regression algorithm

Performance Assessment of Indian Electric Distribution Utilities Using Data Envelopment Analysis (DEA)

One of the most important links in the electricity market is the electricity distribution sector as it involves interfacing the end customers and providing the revenue that is required for running the entire chain of the electricity sector. Besides, it provides power to the individual consumers at relatively lower voltages by absorbing high voltage power from the transmission sector and involves revenue collection from the consumer sector. The primary aim of the distribution sector, as stated by the Provisions of Tariff policy, Government of India, 2016, is to provide reliable, quality and sustainable power at reasonable rates. Despite the restructuring of Indian Power Sector post implementation of Electricity Act 2003, the electricity distribution utilities, in the recent past, are experiencing poor financial results irrespective of their ownership (public, private or joint sector). This has been attributed to various factors related to economic, environmental, physical, social and technical streams related to the operation of the utilities. The performance of these electricity distribution utilities can be assessed using Data Envelopment Analysis (DEA) tool which is extensively used in the recent past by the researchers to analyze the performance of entities of other sectors. The successful employment of DEA for evaluating the electricity distribution utilities lies with the proper selection of input and output variables that are chosen for the analysis. The current work analyzes the relative comparative performance of electricity distribution utilities across India and identifies inefficient utilities besides providing peers/reference, slack analysis for effective strategic improvement.

A Review of the Characteristics and Challenges of Electricity Distribution in Ghana: Case Study of the Northern Electricity Distribution Company

This paper presents a review of the characteristics and challenges of electricity distribution in Ghana with an emphasis on the Northern Electricity Distribution Company (NEDCo) in the northern half of Ghana. NEDCo is used as a case study due to the peculiarity of its geographical area of operation and customer characteristics which better highlights the challenges of electricity distribution in Ghana. NEDCo’s electricity distribution operations cover approximately 64% of the total landmass of Ghana and contain the most deprived communities and consumers, who are extensively dispersed. In addition to the scattered settlements, NEDCo has a predominantly lifeline customer base that pay tariffs below the average cost of service. These peculiarities, together with regulatory complexities, have contributed to many challenges, such as high system losses, high cost of service and poor network reliability, in the electricity distribution sector in Ghana.

Regulatory Reforms and Technology Adoption in the Indian Electricity Distribution Sector: An Interplay of Actors, Institutions, Governance Structure and Technology

Regulatory Reforms and Technology Adoption in the Indian Electricity Distribution Sector: An Interplay of Actors, Institutions, Governance Structure and Technology

Performance Effects of Network Structure and Ownership: The Norwegian Electricity Distribution Sector

Transmission and distribution networks are capital intensive segments of the electricity sector and are generally considered natural monopolies. Due to their non-competitive nature, these are subject to independent regulation to prevent the abuse of monopolistic power and to induce competitive behaviour. Effective economic regulation of the electricity networks has become a key target in most developed economies after the 1980s. In Norway, incentive regulation and efficiency benchmarking were introduced in 1997. In Norway, the electricity grid is divided into three levels, namely, central, regional and distribution networks. In this paper, we study two overlooked aspects when analysing the performance of electricity networks: vertical integration and ownership structure. We use a stochastic frontier analysis approach to analyse the performance of Norwegian electricity distribution utilities for the period 2007–2014. We observe that vertical integration between distribution and regional transmission implies higher cost inefficiencies. This indicates that the efficiency gains due to separate management of the networks exceed the economies of coordination from vertical economies of scope. In addition, we find that council ownership entails higher efficiencies. This could be explained by the state having an interest in high-voltage electricity networks, rather than low-voltage ones, and the decentralised model from which the now centralised system was once developed.

Unveiling benefits through franchising in Indian power distribution: addressing the resources scarcity

PurposeThe Indian power distribution companies are increasingly recognizing franchising for reviving their high loss-making rural pockets. The motivation for franchising has been a reduction of the franchisor's resource scarcity by bringing in operational efficiency and improved service quality to end consumers. However, there is a dearth of evidence on the influence of the franchisee's operations in addressing the resource scarcity of franchisors in predominantly rural areas. This study contributes towards filling the research gap.Design/methodology/approachA qualitative embedded multiple case study was conducted. The cases comprised two rural franchisees operating towards attaining the common goal. The study was built on archival analysis, personal observations and semi-structured interviews with the franchisors and franchisee officials across the organization's hierarchical levels. A conceptual model based on the review of prior literature formed the initial set of coding for the study. The data were presented based on within-case and across-case analysis.FindingsThe analysis revealed that the contract design impacts the requisite operational efficiency achievement. This variation could be elaborated by factors, such as system adaptation across organizational hierarchy, autonomy and independence, review and feedback systems, monitoring, a professional's attitude, bureaucracy, adaption with the local areas, risk sharing, incentives and compensation structure.Research limitations/implicationsThe study findings could be generalized to the extent of similar socio-economic conditions, prevailing governance mechanisms and law and orders. Additionally, since the law does not mandate the regulatory commissions to scrutinize the performance of the franchisees, the study was built on data shared by the franchisees and the discom. Further, this study considered the performance of only two performing franchisees. Matching these actualities with the discoveries of this study remains a continuing project as participation of private players is increasingly being recognized. Therefore, the insights drawn from this study could be used to improve the franchise model and can be scaled up across the nation, regions and sectors.Originality/valueThere is a dearth of literature on franchising in electricity distribution. This study is one of the first studies on studying the franchise system in the electricity distribution sector through the application of a well-accepted management theory.

Efficiency analysis for performance evaluation of electric distribution companies

In power systems, the electricity distribution sector requires proper regulation to guarantee power supply security, electricity tariffs modicity and universal service to customers. Generally, electric utilities operate differently and are distinguished in terms of costs, quality of supply, market and network size, and other aspects, that affect their efficiency. In this context, the Data Envelopment Analysis has been used in electricity distribution regulation to define efficiency scores and compare practices. The Data Envelopment Analysis application sometimes comes with weight restrictions and negative variables that modify the original methodology which affects the efficiency scores. The main goal of this paper is to evaluate weights restrictions influence on efficiencies results and to perform a sensitivity analysis of efficiency scores using additional benchmarking techniques. We apply the Cross-Efficiency Analysis and the Ratio-based Efficiency Analysis benchmarking methods, in order to provide relevant quantitative information to compute relative efficiency scores and perform peer evaluations among utilities even if they are outside of the efficient frontier. The Brazilian electricity distribution system is selected as study case. Brazil has strong diversity in terms of economic development, climate and geography, and the current procedure adopted by the regulator determine efficiency metrics for all distribution companies based on their operation cost. Results from our analysis show that the diversity of concession areas significantly influence the stability of efficiency scores. Moreover, considering the approach proposed here it is possible to identify an efficiency relationship among all the distribution companies and not only using the ones that are in the efficiency frontier.

The Impact of Smart Prepaid Metering on Non-Technical Losses in Ghana

The high incidence of electricity theft, meter tampering, meter bypassing, reading errors, and defective and aged meters, among others, increases utility losses, especially non-technical losses (NTL). A utility in Ghana piloted a non-technical loss reduction program in 2019 to replace postpaid meters with anti-tamper, anti-fraud, and anti-theft smart prepaid meters. By using customer-level residential billing panel data from 2018 to 2019 obtained from the utility, we assess the effectiveness of this program using the difference-in-differences fixed-effect approach. On average, the results indicated that the reported amount of customers’ monthly electricity consumption increases by 13.2% when any tampered postpaid meter is replaced with a smart prepaid meter, indicating the NTLs by customers. We further employed quantile difference-in-differences regression and observed that reported energy consumption has increased for all households except those at the lower quantile (25th quantile). We conclude that smart prepaid metering could be a remedy to reduce NTLs for the electricity distribution sector in areas where electricity theft is rampant.

Deregulation, efficiency and policy determination: An analysis of Australia's electricity distribution sector

In 1998, the Australian electricity distribution was deregulated with the aim of promoting competition and reducing retail prices. However, since then these outcomes have not transpired, which raises the question of whether there may be underlying causes leading to inefficiencies within the power distribution industry. To assess the performance of the electricity distribution system and sources of (in)efficiency since deregulation, we employ Simar and Wilson's (2007) double bootstrap data envelopment analysis truncated regression approach. The results suggest that most distributors were operating well below efficient levels for the period concerned. In the second-stage analysis, the results indicate that reliability promotes efficiency suggesting that a focus should be placed on the continuous supply of energy. Specifically, inefficient distributors can improve network reliability by replacing aged poles and the expansion of market size could encourage healthy competition.