Electricity Distribution Companies Research Articles

Digital transformation of Egyptian electricity distribution companies: moving towards digital governance

Digital transformation of Egyptian electricity distribution companies: moving towards digital governance

Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased.

THE RELIABILITY ASSESSMENT OF POWER TRANSFORMERS

This research investigated the assessment of power transformer reliability with emphasis on the transmission network within Rivers State in Nigeria, focusing on the perspectives of electricity consumers, organizational personnel, and business operators using descriptive survey. The study encompassed the entire population of 725,372 electricity consumers in the Port Harcourt Electricity Distribution Company (PEDC) in Rivers State, which included both households and business owners. To select a representative sample, the Convenience Sampling Technique was employed, resulting in the inclusion of 390 electricity consumers in Rivers State. Data collection utilized the Consumers Perception of Electricity Power Transformer Reliability (COPEPT) questionnaire and Structured Interview, with the instrument's reliability established through the test-retest technique, yielding a reliability coefficient for each investigation. Research questions were addressed through the weighted mean score (WMS) analysis. Key findings indicated dissatisfaction among electricity consumers in Rivers State, primarily attributed to factors such as transformer age, overall condition, uncontrolled overloading, adverse weather conditions, and inadequate transformer capacity to meet increasing demand. Addressing these issues, different approaches including upgrading or replacing outdated transformers, implementing limits on transformer loading, introducing robust earthing systems, and increasing transformer capacity, were recommended to enhance consumer satisfaction and overall reliability. The study further revealed that persistent power transformer failures resulted in power outages, adversely impacting businesses, households, communication, and contributing to reduced production and national income. Based on these findings, recommendations in the form of strategies were provided, emphasizing a comprehensive analysis of power surge control during adverse weather conditions, a plan for upgrading or replacing outdated transformers, an assessment of power transformer capacity needs, and collaboration with relevant stakeholders to develop strategies mitigating the negative economic impact and enhancing communication with consumers.
 Keywords: Reliability Assessment, Power Transformers, Rivers State and Transmission Network.

A Conceptual Model to Measure Digital Maturity Level in Electricity Distribution Companies

Digitalization, while being one of the primary objectives in today's business world, increases its significance with technological advancements. Recently, there has been a need for tools to determine the levels of digitalization within certain standards and metrics for companies seeking to understand and accelerate their digitalization process, and to determine how these companies should proceed to address their shortcomings. When specifically looking at electricity distribution companies, the technological advancements such as distributed production, storage, and electricity vehicles make the digitalization process not just a choice but a necessity to operate distribution systems reliably and effectively. This study proposes a conceptual model specifically for electricity distribution companies for the first time, by researching digital maturity models developed for other sectors in the literature.

Long-term Load Forecasting for Optimal Power System Planning and Decision-Making

Forecasting the future load growth of an area based on its load demand is often a proactive measure to ensure a steady electricity power supply to that area. The study focused on long-term load forecasting for power system planning, specifically examining the electric load demand from consumers on distribution transformers within Port Harcourt City, located in Rivers State, Nigeria. The study encompassed a comprehensive review of both statistical and artificial intelligence-based approaches. Historical load data for distribution transformer readings spanning 2008 to 2017 were acquired from the Port Harcourt Electricity Distribution Company (PHEDC) and subjected to analysis using the curve-fitting technique. For the period between 2015 and 2030, a yearly load forecast simulation was conducted using the Fourier Series model, implemented with MATLAB software. This simulation aimed to provide insights into future load demand, facilitating careful and informed decision-making in the investment, operation, and maintenance of power system equipment. The effectiveness of the forecasting investigation was assessed using the Root Mean Square Error (RMSE), confirming the efficiency, reliability, and validity of the employed model. The study's forecasted results are presented as a valuable guide and practical tool for policymakers and the utility company (PHEDC) to enhance proper planning and decision-making processes. Considering the observed trend in the results, it is suggested that installing additional transformer units in the region would be necessary to alleviate the loads on existing overloaded transformer units within the power system network.

Smart Vehicle to Grid Energy Management Algorithm for Electric Vehicle Supported by Photovoltaic Energy Sources

The paper demonstrates grid improvements with results considering the integration of smart charging algorithms of electric vehicles (EVs) and photovoltaic (PV) energy sources to effectively manage energy flow. It aims to minimize energy costs by leveraging the bidirectional energy transfer capability of EVs and optimizing their interaction with the grid and PV sources. The proposed algorithm takes into account various parameters such as transformer load ratios, solar power generation, and the state of charge of vehicle batteries. The algorithms are implemented by acquiring one year’s real-time data of a predetermined transformer in a neighborhood from the electricity distribution company. The number of EV is estimated for the years 2025 and 2030. Buildings with energy management systems (BEMS), which charge EV with a controlled charging algorithm, provide renewable energy support with PV and reduce the transformer load during peak hours with the V2G algorithm. BEMS is not applied in buildings without EV or PV. Simulation results demonstrate that as the number of EVs increases, renewable energy source support and controlled charging algorithm alone are not sufficient. However, it demonstrate that energy costs and grid stress are successfully minimized by using the V2G algorithm in addition to controlled charging algorithm and PV.

Analysis of Ukraine power grid cyber-attack 2015

Analysis of Ukraine power grid cyber-attack 2015

An evaluation of 2013 privatization on Benin Electricity Distribution technical and workforce performance

Purpose This study aims to investigate the impact of the 2013 privatization of Nigeria’s energy sector on the technical performance of the Benin Electricity Distribution Company (BEDC) and its workforce. Design/methodology/approach This study used a questionnaire-based approach, and 196 participants were randomly selected. Analytical tools included standard deviation, Spearman rank correlation and regression analysis. Findings Before privatization, the energy sector, managed by the power holding company of Nigeria, suffered from inefficiencies in fault detection, response and billing. However, privatization improved resource utilization, replaced outdated transformers and increased operational efficiency. However, in spite of these improvements, BEDC faces challenges, including unstable voltage generation and inadequate staff welfare. This study also highlighted a lack of experience among the trained workforce in emerging electricity technologies such as the smart grid. Research limitations/implications This study’s focus on BEDC may limit its generalizability to other energy companies. It does not delve into energy sector privatization’s broader economic and policy implications. Practical implications The positive outcomes of privatization, such as improved resource utilization and infrastructure investment, emphasize the potential benefits of private ownership and management. However, voltage generation stability and staff welfare challenges call for targeted interventions. Recommendations include investing in voltage generation enhancement, smart grid infrastructure and implementing measures to enhance employee well-being through benefit plans. Social implications Energy sector enhancements hold positive social implications, uplifting living standards and bolstering electricity access for households and businesses. Originality/value This study contributes unique insights into privatization’s effects on BEDC, offering perspectives on preprivatization challenges and advancements. Practical recommendations aid BEDC and policymakers in boosting electricity distribution firms’ performance within the privatization context.

A multi‐layer–multi‐player game model in electricity market

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

Evaluation of Harmonic Currents and Network Impedance using Norton Model in an Unidentified Network Configuration

This paper presents a study that focuses on the evaluation of the analysis of harmonic currents emitted by loads and the harmonic impedance of a real electrical network using the Norton model. The primary advantage of employing this model lies in its ability to address scenarios where the complete network configuration, particularly from the distribution side, is not known. By calculating currents and voltages at the supply-side for two different operating conditions, the harmonic current content and the network impedance can be evaluated. To induce variations in operating conditions, a switching shunt capacitor is utilized. The results show that the fifth and the seventh harmonics clearly appear with values of 189.382 A and 135.073 A, respectively, while the other harmonics have values close to zero. The harmonic impedance also shows the presence of the fifth and the seventh harmonics with values of 1865.681 Ω and 4289.3 Ω, respectively. The main contribution of this paper focuses on converting the real electrical network into numerical data that can be accepted by the software. The components of the network model have to be carefully selected to understand the harmonic analysis and to success the simulation, which is conducted using the EMTP-ATPDraw program. The results of this investigation can be used as a database for electrical transmission and distribution companies in case of making any changes in the network, such as installation of new loads or cables, or in case of making measurements.

Comparative Analysis of DG Units with Shunt Capacitive Banks on the Improvement of Voltage Profiles in Distribution Networks.

Electrical distribution systems’ reliability is a critical parameter for electrical networks and their end-users. Among the different parameters associated with these systems’ reliability, voltage profile is most commonly affected in the electrical network due to different internal and external parameters such as lines and feeders’ impedance, system overloads, and increments on end-users. A poor voltage profile can have adverse impacts on the end user, such as long-term harm to their electric and electronic devices. For both consumers and companies that provide electricity, a low voltage profile thus leads to a loss of power. The Distributed Generation (DG) system is a viable option to improve voltage profiles while conserving the environment by unlocking the usage of any renewable energies following recent technological advancements and environmental concerns. This article proposes an alternative method to enhance voltage profiles in a distribution network (Abuja Electricity Distribution Company, AEDC) by comparing the most effective approach for power losses and voltage profile compensation through DGs and capacitor banks. Detailed simulations are conducted to explain and validate the results with the optimum system voltage improvement and loss reduction. This work has found that the voltage profiles are more enhanced with DG units compared to capacitor banks. The power losses were reduced from the initial value of 1196.4 kW to 762.4 kW with a saved energy of 36.3% with DG-unit as compared to FC with a power loss reduction of 1005.2 kW (energy saved of 16.0%).

Sizing and analysis of a standalone photovoltaics system for a three-bedroom residence in Nigeria

<span>The intermittency of electricity supply from conventional sources, increase in fuel prices and constant emission of greenhouse gases by non-renewable energy sources are major challenges faced by energy users. Energy from reneweable sources have advantages over the traditional (non-renewable) sources of energy. This paper presents the sizing and analysis of a standalone photovoltaic (PV) system for a 3-bedroom residence situated at Obollo-Nsukka (6.876°N, 7.403°E, 389 m) in Nigeria. The energy requirements of such a residence are 8.14 kWh/day and analysis have shown that the cost of constructing the PV system is ₦2,838,040 Nigeria naira (NGN). The cost of maintaining such a system within a lifetime of 20 years is between 159,328 NGN/year to 1,895,918 NGN/year. Comparing the levelized cost of energy (LCOE) of enugu electricity distribution company (EEDC) which is 66.5 NGN/kWh to the LCOE of the standalone PV system which is between 102,124 NGN/kWh to 419 NGN/kWh it was found out that the cost of electricity from the PV system is more than that of the conventional grid. <br /> The PV system provides feasible solution to the intermittency issues of the conventional grid in Nigeria. Hence, this technology only technically viable for residential electrification purposes in Nigeria.</span>

Application of Power System Reliability and Outage Cost Indices on 33/11 kV Distribution Injection Substation: A Case Study of Asaba Business District

The energy satisfaction that consumers can obtain from a power service provider solely depends on how reliable the feeder to which such customers are connected guarantees adequate and secure energy delivery. This research work is, therefore, to assess the reliability indices and the cost of expected electricity not supplied to the Asaba metropolis through the Benin Electricity Distribution Company (BEDC) Asaba 33/11 kV distribution injection substation. The method used consists of computing the System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), Customer Average Interruption Duration Index (CAIDI), Average Service Availability Index (ASAI), and Expected Energy Not Supplied Index (EENS) of the four feeders emanating from the substation and radiating into the metropolis. This is achieved by first collecting the outage hour, frequency of outage, and expected energy at the point of outage from the station's hourly logbook. The input data is fed into a program written in a MATLAB/OCTAVE environment. The values of the most important reliability index, ASAI, or availability, computed on the SPC, Anwai, Express, and Asaba Commercial feeders are 0.24, 0.23, 0.23, and 0.76 p.u., respectively, in 2020, while in 2021, these values are 0.32, 0.31, 0.33, and 0.83 p.u., respectively. Using the ASAI standard value from IEEE Standard 1366-2003, which is 0.999 as a benchmark, shows that all the feeders can be categorized as less reliable in the years under study. The annual cost of expected energy not supplied during power interruptions to Asaba electricity consumers in 2020 is $2,426,187.046 and $2,214,669.91 in 2021, respectively.

Performance analysis of protection devices in electrical power distribution system: Eko Electricity Distribution Company as a case study

One of the major challenges hindering the effective operation of the electricity distribution system is the malfunctioning of the protection scheme. Therefore, in this study, the performance of an interrupter (oil circuit breaker (OCB)) on the Eko Electricity Distribution Company (EKEDC) network was assessed. Five-year outage data (2014 to 2018) regarding tripping of the interrupter on fault and health conditions were collected from the EKEDC Agbara/Badagry Business Unit. The tripping frequency was compared. Inspection of selected OCBs within the considered network was conducted over the study period. A breakdown voltage (BDV) test was carried out on six oil samples each for 11 and 33 kV OCB using Megger OTS60PB. The highest tripping frequency of 75 and 10 were observed in 2014 and 2018 respectively when the interrupter tripped on fault and healthy conditions. The lowest tripping frequency was, however, observed in 2018 with values of 46 and 7 respectively when the interrupter tripped on fault and healthy conditions. The inspection conducted revealed carbonized OCB contacts. The BDV test showed that two oil samples each violated the standard value of 40 kV for 11 and 33 kV OCBs respectively. This study indicated that the defective oil samples required filtration or complete replacement.

Mu’amalat Challenges between Kaduna Electricity Distribution Company (KAEDCO) Staff and Muslim Customers in Gusau

The attitude of staff in most of the public enterprises and Governmental organizations in Nigeria usually determines the effective, efficient, fair and credible relationship between the organization and its clients (customers). Thus, a good representation by the staff of any corporate utility company or industry is an essential element of good governance and sustainable business enterprise. Also, Islamic traditions entail that good moral qualities and ethical values elevate the status of man and make him excel among others in all his endeavours religiously, economically, socially and politically. This research aims to explain the Islamic laws of transaction and mutual dealings (Fiqh al-Mu’amalat), their meaning, sources, scope, as well as ethical and moral qualities of the Prophet Muhammad (S.A.W) that are worthy of emulation by all Muslims in their dealings with one another. The research will also identify some of the Mu’amalat challenges and unethical behaviours exhibited by both parties which are detrimental to their overall progress and provide some possible suggestions and recommendations on how to establish a better relationship between KAEDCO staff and Muslim customers in the Gusau metropolis. The researchers employed both qualitative and quantitative approaches to collect and collate all the necessary data that are relevant and suitable for the research. The results of the findings discovered some of the key areas where problems usually arise between the said parties. It also identified certain rights, obligations and responsibilities of both so that each will operate within his jurisdictions. The teachings of the Islamic law of Mu’amalat have been carefully discussed from the Qur’an, Sunnah and the views of Muslim jurists as a guiding path of ensuring better correspondence between the Staff of KAEDCO and the Muslim customers in Gusau Metropolis. The findings of the study revealed that good service delivery and quick response to customer’s complaints are among the key measures of enhancing a better relationship between the said parties. The paper recommends that both parties should base their Mu’amalat on the basic teachings of Islamic law of transaction as a means of fostering good relationships.

Local-level ownership of electricity grids: An analysis of Germany's distribution system operators (DSOs)

Electricity grids are crucial for the energy transition. Despite its importance for renewable energy, the literature on grid ownership at the local level in Germany lacks up-to-date knowledge. Existing research does not state the extent to which municipalities own distribution system operators (DSOs). Based on a novel dataset on grid operators in 10,800 German municipalities, this paper can provide for the first time an overview of the current ownership of electricity distribution companies. Although the electricity infrastructure is decentralised, ownership remains centralised in the hands of Germany's leading energy companies and concentrated in affluent population centres.

Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants

Varying power generation by industrial solar photovoltaic plants impacts the steadiness of the electric grid which necessitates the prediction of solar power generation accurately. In this study, a comprehensive updated review of standalone and hybrid machine learning techniques for PV power forecasting is presented. Forecasting solar generation is of importance for the sustainability of grid power and also to achieve the UN sustainable development targets by 2030. The comparison of techniques shows that grouping datasets based on input feature similarity, results in higher accuracy. Long-Short Term Memory (LSTM) is found to perform better than other deep learning networks for all time horizons. The Gate Recurrent Unit (GRU), with few trainings, is found to be better for small datasets than LSTM. Based on the more complicated data patterns, a novel architecture of the Deep Learning Network model, with the capability to analyze and forecast is presented considering factors influencing industrial solar power generation. The study is of importance to researchers, solar industry, and electricity distribution companies for sustainable development worldwide.

Proposing a new optimized forecasting model for the failure rate of power distribution network thermal equipment for educational centers

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables to the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

Comparative analysis of deep learning techniques based COVID-19 impact assessment on electricity consumption in distribution network

Energy is a fundamental human need for several activities. Energy can be impacted by several factors ranging from technical to social and environmental. The impact of COVID-19 outbreak on the energy sector is enormous with serious global socioeconomic disruptions affecting all economic sectors, including tourism, industry, higher education, and the electricity industry. Based on the unstructured data obtained from Eko Electricity Distribution Company this paper proposes three deep learning (DL) models namely: Long Short-Term Memory (LSTM), Simple Recurrent Neural Network (SimpleRNN), and Gated Recurrent Unit (GRU) were used to analyse the effect of COVID-19 pandemic on energy consumption and predict future energy consumption in various district in Lagos, Nigeria. The models were evaluated using the following performance metrics namely: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). On overall, the lowest MAPE, MAE, RMSE, and MSE of 0.120, 71.073, 93.981, and 8832.466 were obtained for LSTM in Orile, SRNN in Ijora, and GRU in Ijora, respectively. Generally, the GRU performed better in predicting energy consumption in most of the districts of the case study than the LSTM and SimpleRNN. Hence, GRU model can be considered the optimal model for energy consumption prediction in the case study. The importance of having this model is that it can help the government and other stakeholders in economic planning of electricity distribution networks.

Estimated energy consumption and billing modelling using power availability recorder

Accurate estimation and billing of unmetered facilities and homes by the energy providers in Nigeria has been a serious nightmare to the rural and other consumers. The unjust billing due to inaccurate and wrong estimations have resulted to rancor, facilities damage and economic hardship among the people due to poor equipment and unreliable consumption data. This research work presents accurate energy consumption and billing model using power availability recorder (PAR) which keeps track of how long electricity lasts in a building or facility at discrete time scale. Two residential buildings situated at the University of Nigeria, Nsukka, and one facility each in two chosen communities in Nsukka, were used as commercial and residential electricity consumers respectively for this study. The readings from the PAR were obtained in discrete scale (mini-seconds), and were later converted to seconds and hours. Similarly, the readings from the Energy Consumption meter were taken in kWh. These daily results taken simultaneously at 6 am and 6 pm were used to calculate the Availability Factor (AF), Load Use Factor (LUF) and Average Load (AL) in kW. With PAR as energy meter in significant number of houses within the chosen communities, AF, LUF, and AL for different facilities in the community were obtained and used for the energy billing model development. A relationship, which depends on availability and load use factor, has been suggested in this study with a proportionality constant value of 14.78 kWh/day/customer for use in obtaining better estimates of the bills chargeable to unmetered customers. This will control the black-out relay period usually encountered by consumers instead of the grossly unjust estimated bills currently being used by Nigeria's electricity distribution companies.