Electricity Distribution Utilities Research Articles

Definition of Regulatory Targets for Electricity Default Rate in Brazil: Proposition of a Fuzzy Inference-Based Model

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.

Has benchmarking improved the performance of the Australian electricity distribution utilities? A meta-frontier model

This research evaluates the effects of performance benchmarking for public utilities using the Australian experience, providing empirical support for the significance of mechanism design in implementing revenue-cap regulation. We estimate the efficiency and productivity of Australian electricity distribution providers by utilizing the meta-frontier framework with panel data covering the period from 2007 to 2021. Our findings indicate that introducing benchmarking has facilitated technological progress and reduced efficiency gaps among companies. However, there is no statistically significant difference in the average efficiency between the periods before and after the reform, potentially due to strategic behavior exhibited by network service providers.

Day-Ahead Electricity Load Forecasting with Multivariate Time Series

The importance of day-ahead load forecasting cannot be overstated. Electricity load forecasting is highly important because it allows electric distribution utilities to increase their transmission efficiency and their revenues, increase the reliability of power supply, and correct decisions for future developments. In this paper, day-ahead load forecasting was studied using multivariate time series analysis. Traditional forecasting method such as seasonal autoregressive integrated moving average (SARIMA) was used. Machine learning algorithms such as Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) were also utilized to determine their applicability to short-term load forecasting. For SARIMA, only historical hourly load was needed while FFNN and LSTM required the addition of temperature data, hour of the day and day of the week, special events and previous hour load. In the prediction, FFNN and LSTM performed better with mean absolute percentage error (MAPE) of 1.80 and 1.75%, respectively, compared with SARIMA with a MAPE of 4.48%. The study demonstrated that machine learning like FFNN and LSTM outperformed the traditional SARIMA models, highlighting their effectiveness in applications such as short-term load forecasting in time series prediction.

Cyber Security for Power Distribution System

Electricity distribution utilities in India are facing major challenges such, as higher revenue realization gap and AT&Closses. One of the ways to overcome this challenge is by adoption of smart technologies and automating the networkfor increased visibility, monitoring and automated control. The adoption of smart technologies merges the conventionalpower distribution system with modern communicable devices having sensing and control features. The use of suchmodern communicable devices makes power systems vulnerable to cyber-attack. This paper presents the SCADA systemarchitecture for power distribution systems, NIST guidelines and framework, cyber-security for power systems andstandards for security implementation for telecontrol equipment and systems.

Security Implementation Testing of Remote Terminal Unit for Power Utility

The Indian power sector is undergoing a transformation by implementing digital technologies to deal with the major challenges of higher AT&C losses. Implementation of digital technologies requires the use of various communicable devices as a part of the Operational Technology (OT) system that enables real-time monitoring and control of the power sector. In order to access information from external networks, these technologies connect the OT systems with Information Technology (IT) systems, which exposes the OT systems to potential cyber-attacks. Therefore, it is essential to validate the security implementation of OT devices to mitigate the risk of cyber-attacks on OT systems. Electricity distribution utilities are implementing a substation automation system, and one of the major components of a substation automation system is Remote Terminal Unit (RTU). This paper explains the importance of cyber security requirements, OT system security challenges and security implementation testing of Remote Terminal Unit (RTU) as per IEC 62351 standards.

Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions

Non-technical losses (NTLs) are one of the main problems that electricity distribution utilities face in developing regions such as Latin America, the Caribbean, sub-Saharan Africa, and South Asia. Particularly in Brazil, based on the socioeconomic and market variables concerning all the distribution utilities, the National Electric Energy Agency (ANEEL) has formulated several specifications of econometric models for panel data with random effects, all aimed at determining an index that reflects the difficulty of combating NTLs according to the intrinsic characteristics of each distribution area. Nevertheless, given the exhaustive search for combinations of explanatory variables and the complexity inherent to defining regulatory NTL targets, this process still requires the evaluation of many models through hypothesis and goodness-of-fit tests. In this regard, this article proposes an automatic model-selection technique for panel data regressions to better assist the Agency in establishing NTL regulatory targets for the distribution of utilities in this country. The proposed technique was applied to panel data containing annual observations from 62 Brazilian electricity distribution utilities from 2007 to 2017, thus generating 1,097,789 models associated with the regression types in the panel data. The main results are three selected models that showed more adherence to the actual capacity of Brazilian distribution utilities to reduce their NTLs.

The Impact of Services Quality on Electricity Theft Reduction: An Empirical Analysis of Electricity Distribution Utilities in Pakistan

This study investigates the socioeconomic, administrative, and service quality determinants of electricity theft in Pakistan. The effect of service quality standards on electricity theft is estimated using panel data fr om eight electricity distribution utilities from 2006 to 2018. The results of utilizing the One step system Generalized Method of Moments estimator confirm the statistically significant impact of social, administrative, and service quality variables on the illegal use of electricity. Based on the findings, Pakistan's National Electric Power Regulatory Authority is advised to take measures to improve the quality of services. In contrast, the nation’s Central Power Purchasing Agency is advised to improve admi nistrative structure by separating retail function from distribution by electric distribution.

Wavelet LSTM for Fault Forecasting in Electrical Power Grids

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.

Automatic Recloser Adjustment for Power Distribution Systems

In this paper, a self-adaptive protection system is proposed for adjusting the time dial (TD) of reclosers found in electrical power distribution systems (EPDSs). The self-adjustment is carried out in both phase and ground protection. The methodology considers the fuses along the feeder to obtain the fuse-saving philosophy. This feature allows less interference on the protection system already implemented by the electrical power distribution utilities. This methodology also allows using other frameworks, such as the estimation of the fault distance. The software used for computer modeling and simulation was PSCAD™/EMTDC™. The flexibility provided by the method, which allows the incorporation of new logic and changes in decision-making, resulted in an adaptive method with a good performance against different locations, types, and fault impedance. It also resulted in better coordination between the protection devices (PDs). The results compared with well-coordinated conventional protection have shown significant performance gain with the adaptive system.

The detection of non-technical losses and electricity theft by smart meter data and Artificial Intelligence in the context of electric distribution utilities: A comprehensive review

The detection of non-technical losses and electricity theft by smart meter data and Artificial Intelligence in the context of electric distribution utilities: A comprehensive review

Will power be local? The role of local power organizations in energy transition acceleration

A salient problem faced by governments and industry alike is how to accelerate energy transitions to enhance affordability, accessibility, and greenhouse-gas reduction. Bringing together acceleration processes and spatial scale dynamics, this study highlights the potential for electricity distribution to play a keystone role in the energy transition. We present and examine survey data of electricity distribution utilities in a region of the U.S. to show how trends in decentralization and digitization are intertwined with decarbonization. These trends rebalance economic value toward distribution networks and away from centralized infrastructure. The survey data show that electricity distribution organizations are deploying local, renewable generation projects that produce electricity for one-third (1/3) less than the cost from a centralized generation-and-transmission entity. We suggest that this change and others are likely to transform distribution operators into more broad-based local power organizations. Although the cost advantage of distributed generation seemingly marks a future of local control and decentralized organizational forms, spatial scale dynamics indicate countervailing centralization trends, including that distribution networks may evolve to dependency on external digital, engineering, and capital providers. The outcome of the resulting conflicts will affect the potential for transition acceleration to be enabled or reduced.

Introducing Electric Bus Fleets in Rio de Janeiro City Methodology and Analysis

This paper describes the establishment of a methodology aimed at planning the electrification of public urban bus fleets in order to characterize the operational requirements of the battery recharge process. Specifically, it deals with obtaining the behavior of demand and the necessary electrical energy for battery recharging. The effects on the electrical system peak and the resulting environmental benefits are also evaluated by comparing with current internal combustion fleets. An innovative method is applied to a fleet in the city of Rio de Janeiro. The motivation for this work is due to the characteristics of the Brazilian energy mix that is highly favorable to the electrification of urban transport with greater energy efficiency, with a view to reducing the impacts of internal combustion drive and the respective atmospheric emissions that are harmful to the environment. Also, this work offers the steps to establish essential elements, as daily, month and year energy consumption in order to recharge an electric bus fleet and data for the transportation bus company contracts an electric power distribution utility in Brazil. In spite conservative premises were considered, the calculations show electric bus option attractive. The operational energy consumption of diesel bus fleet is 19.10 MJ/km more than three times the consumption of electric bus fleet, this is 6.10 MJ/km. Additionally, 153 ton of fossil CO2 atmospheric monthly emissions are avoided. The results obtained indicate the need for public policies in favor of electric power and the environmental benefits of adopting electric buses.

Two-phase optimization approach for maintenance workforce planning in power distribution utilities

This work addresses the maintenance workforce planning in electric power distribution utilities. A two-phase optimization procedure is proposed to consider the strategic (long-term) and tactical/operational (medium/short-term) planning. In the first phase (strategic planning), a bi-objective optimization problem is solved to locate depots and to size the number of repair teams at each depot. One of the objectives includes operating and capital expenses, while the other one is based on continuity indicators related to the interruption in the electrical energy supply. In order to solve this problem, we use a memetic algorithm which combines a metaheuristic with a mixed-integer linear programming (MILP) model. On the other hand, the second phase (tactical/operational planning) consists of solving other MILP model to determine, for each depot, the required number of electricians (now based on more detailed forecasts) and define their work schedules, minimizing the wage costs and hiring/firing expenses. The results show that a larger number of solutions can be found through the memetic algorithm compared to the purely metaheuristic approach. The proposed optimization algorithm is part of an innovative web-based system developed during a research and development project and it is now available for eleven Brazilian power distribution utilities.

Impact of Site Conditions and Requirements on Microgrid Design, Control, and Performance

The paper presents the protection design, stability, and control challenges associated with and insights drawn from two real-world microgrids. The presented microgrids were built within the electric utility distribution systems with the existing interconnected electrical loads and Distributed Energy Resources (DERs). Detailed analysis of the observed site protection and stability challenges, supported by an extensive set of site measurements and simulations, along with a systematic approach to troubleshooting the identified issues, is presented. The efficacy of the proposed and or implemented solutions is also discussed.

Deep learning for component fault detection in electricity transmission lines

Component fault detection and inventory are one of the most significant bottlenecks facing the electricity transmission and distribution utility establishments especially in developing countries for delivery of efficient services to the customers and to ensure proper asset audit and management for network optimization and load forecasting. For lack of technology and data, insecurity, the complexity associated with traditional methods, untimeliness, and general human cost, electricity assets monitoring, and management have remained a big problem in many developing countries. In view of this, we explored the use of oblique UAV imagery with high spatial resolution and fine-tuned deep Convolutional Neural Networks (CNNs) for automatic faulty component inspection and inventory in an Electric power transmission network (EPTN). This study investigated the capability of the Single Shot Multibox Detector (SSD), a one-stage object detection model on the electric transmission power line imagery to localize, detect and classify faults. Our proposed neural network model is a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults through a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision (mAP) of 89.61%. All the developed SSD-based models achieve a high precision rate and low recall rate in detecting faulty components, thus achieving acceptable balance levels of F1-score and representation. We have established in this paper that combined use of UAV imagery and computer vision presents a low-cost method for easy and timely electricity asset inventory, especially in developing countries. This study also provides the guide to various considerations when adopting this technology in terms of the choice of deep learning architecture, adequate training samples over multiple fault characteristics, effects of data augmentation, and balancing of intra-class heterogeneity.

Estimating the marginal cost of improving services quality in electricity distribution utilities of Pakistan

Services quality of electricity distribution utilities has become an issue of prime importance for policymakers since the introduction of incentive-based regulation in electricity markets around the globe. Incentives-based regulatory scheme rewards distribution utilities for cost-savings as well as for improving services quality, creating a conflict in reaching optimal services quality. Therefore, regulators have now started to design regulatory mechanisms by incorporating services quality in their incentive structures. In this regard, it is important for regulators to know about the cost of improving services quality for electricity distribution utilities. Thus, objective of this study is to present a comprehensive econometric framework to estimate the marginal cost of improving services quality associated with losses and interruptions. This study has used panel data for eight distribution utilities from 2006 to 2018. Results of One step system GMM indicate that distribution utilities exhibit diseconomies of scale which are higher than economies of density. Our results suggest that marginal cost of reducing distribution losses is Rs 44/kWh while the marginal cost of reducing 1 min interruption is Rs 0.02. We therefore suggest regulators to use the estimated marginal costs as a benchmark for designing effective incentives-based regulatory framework for distribution utilities in Pakistan.

Robust stochastic frontier analysis applied to the Brazilian electricity distribution benchmarking method

A Data Envelopment Analysis (DEA) method has been applied by the Brazilian regulator to set regulatory operational costs for 61 electricity distribution utilities. Recent studies show evidence that the current method still requires improvements. This study evaluates the use of Stochastic Frontier Analysis (SFA) as an alternative method. Pros and cons are evaluated. Results show that the SFA is more flexible to deal with outliers. However, the SFA has major convergence problems. Convergence issues can be overcome using Bayesian computations. This study advocates the use of both DEA and SFA as the best alternatives, as indicated by European regulators.

A social cost benefit analysis for the procurement of reactive power: The case of Power Potential

This paper quantifies the benefits of introducing reactive power markets that promote the participation of distributed energy resources (DER) in a coordinated way, between the electricity system operator and the electricity distribution utilities. The contribution that DER could make by displacing conventional network assets in supplying reactive power support is evaluated in the context of a case study, the Power Potential (PP) project in Great Britain. We discuss the rising need for absorptive (leading) reactive power in the PP trial area, driven by the rapid connection of renewable generation in an area of low demand growth. A social cost benefit analysis (SCBA) is performed to quantify the net benefits, with sensitivities regarding bid prices, % of DER participation, time horizons. Price information from the PP live trial conducted between January and March 2021 is also used to evaluate the robustness of the SCBA and to estimate benefits using actual prices. Our results suggest that energy consumers could save from 8 to 21% of business as usual asset costs by 2050. The introduction of actual bid prices increases these savings by around 3% of business as usual asset costs out to 2050. Potential sources of additional benefits on top of those identified in the SCBA are also discussed.

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.

Spillover Impact of Debt Takeover of Electricity Distribution Utilities: Evidence from India's Ujwal Discom Assurance Yojana (Uday)

Spillover Impact of Debt Takeover of Electricity Distribution Utilities: Evidence from India's Ujwal Discom Assurance Yojana (Uday)