Average Interruption Duration Research Articles

Assessing Grid Reliability for Optimal Integration of System-Integrated Modular Advanced Reactor Technology (SMART) for Kenya: A Case of the Nairobi Distribution Network

The integration of advanced nuclear technologies like the System-Integrated Modular Advanced Reactor (SMART) offers a transformative approach to enhancing grid reliability in developing countries. This study focuses on the Nairobi distribution network in Kenya, a critical region characterized by an evolving energy mix of geothermal, hydro, solar, and wind power sources, comprising 220 kV and 132 kV transmission lines feeding 66 kV, 33 kV, and 11 kV distribution lines to various load centers. Using ETAP 22 simulation software, the research evaluates the impact of SMART integration through three scenarios: baseline operation, grid compensator-only operation, and SMART-SMR integration. Key reliability indices such as the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), and Expected Energy Not Supplied (EENS) were analyzed. Results reveal that integrating a 100 MW SMART reactor reduces SAIDI by 2.8% (5.0433 h/customer-year) and SAIFI by 5.1% (0.0516 interruptions/customer-year), while maintaining voltage profiles within 98.21–98.91% of nominal. This analysis bridges gaps in prior research by demonstrating SMART’s ability to stabilize power grids in emerging economies, providing critical insights for policymakers aiming to achieve reliable and sustainable energy systems.

Evaluating the Policy Implications of Renewable Portfolio Standards on Grid Reliability in the United States: An Instrumental Variable Quantile Analysis

This paper investigates the influence of Renewable Portfolio Standards (RPSs) on the reliability of grids while considering key indicators, including the System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and Customer Average Interruption Duration Index (CAIDI), both without and with the inclusion of Major Event Days (MEDs). The study adopted the instrumental variable quantile regression (IVQR) method for estimation purposes. The first-stage 2SLS results confirm the robustness and validity of the instruments used, including the Democrats’ Participation Rate, Real Average Household Income, and Renewable Energy Capacity, which show strong relevance and satisfy the conditions of homogeneity. Our findings demonstrated that the adoption of RPS policies has significantly decreased the interruption of power in all of the considered quantiles. The impact is more potent in states facing more severe reliability challenges. The results proved the important role of the policies of RPSs in improving the stability of grids in the context of states having relatively poor reliability. In summary, the obtained findings demonstrate significant insights related to the policies of renewable energy in the resilience of infrastructure. Finally, the findings suggest that the government authorities should promote the RPS as a tool for enhancing the performance of grids in different states.

The Intelligent Sizing Method for Renewable Energy Integrated Distribution Networks

The selection of the optimal 35 kV network structure is crucial for modern distribution networks. To address the problem of balancing investment costs and reliability benefits, as well as to establish the target network structure, firstly, the investment cost of the distribution network is calculated based on the determined number of network structure units. Secondly, reliability benefits are measured by combining the comprehensive function of user outage losses with the System Average Interruption Duration Index (SAIDI). Then, a multi-objective planning model of the network structure is established, and the weighted coefficient transformation method is used to convert reliability benefits and investment costs into the total cost of power supply per unit load. Finally, by using the influencing factors of the network structure as the initial population and setting the minimum total cost of the unit load as the fitness function, the DE algorithm is employed to obtain the optimal grid structure under continuous load density intervals. Case studies demonstrate that different load densities correspond to different optimal network structures. For load densities ranging from 0 to 30, the selected optimal network structures from low to high are as follows: overhead single radial, overhead three-section with two ties, cable single ring network, and cable dual ring network.

Enhancing Fault Detection and Location Identification using GIS-based Techniques: A Case Study of Arba Minch Distribution System

ABSTRACT Fault detection and location in distribution systems are critical for maintaining reliable electric power supply and minimizing outage time. The Arba Minch District distribution department of Ethiopian Electric Utility (EEU) currently employs a manual trial-and-error approach for fault detection and location, which is inefficient, time-consuming, and ineffective in resolving faults. This study evaluates the current approach and proposes an enhanced system utilizing Geographic Information System (GIS) technology to improve fault detection and location in the Arba Minch distribution system, specifically the Shecha feeder. The implementation of GIS-based fault detection and location aims to address the limitations of the manual method and mitigate economic losses associated with feeder faults. ETAP GIS simulations successfully detected and located faults, achieving effective reduction in fault detection and location time compared to the conventional method. By optimizing fault detection and location time, the System Average Interruption Duration Index (SAIDI) for the feeder was significantly reduced. Consequently, the GIS system highly minimizes the economic losses associated with energy not supplied. The results demonstrate the potential of GIS technology in enhancing fault detection and location, ultimately improving the reliability and efficiency of the distribution system.

Reliability and line loading enhancement of distribution systems using optimal integration of renewable energy and compressed air energy storages simultaneously under uncertainty

Recently, with the significant growth of load demand, further interest in renewable energy sources (RESs) incorporated into electric distribution systems (DSs) has become important. However, the output powers' stochastic fluctuations of the photovoltaic (PV) and wind turbine (WT) generation sources, as well as the load demand, result in obvious operational challenges related to the reliability and line loading of these systems. Therefore, this present study is intended to enhance the load-oriented reliability indices (LORIs), the customer-oriented reliability indices (CORIs), and the line loading index (LLI) of the DSs by assigning the optimal integration of RESs and compressed air energy storages (CAESs) through the determination of both the optimal placement and capacities of RESs and CAESs simultaneously, as well as the optimal charging and discharging powers of the CAESs and their initial state of charge based on the red-tailed hawk (RTH) optimization technique. The Monte Carlo Simulation (MCS) and the Scenario-based Reduction (SBR) uncertainty methods are implemented to address the uncertainties of RESs and loading. The influences of the linear failure rate of DS's feeders and the installation of protective devices are also demonstrated. The efficiency of the intended methodology is demonstrated on the IEEE 69-bus and IEEE 33-bus DSs associated with voltage-dependent, time-varying mixed loads. The outcomes obtained from the suggested RTH are compared to those of other reported optimization techniques to validate its effectiveness. The results reveal that the optimal integration of RESs along with CAESs in the IEEE 69-bus test DS can improve the energy not supplied (ENS), system average interruption duration index (SAIDI), system average interruption frequency index (SAIFI), average service unavailability index (ASUI), and LLI by 1.09 %, 1.86 %, 1.73 %, 1.89 %, and 64.53 %, respectively.

Reliability and security improvement of distribution system using optimal integration of WTDGs and SMESs considering DSTATCOM functionality based on an enhanced walrus optimization algorithm

This paper aims to improve the customer-oriented reliability indices (CORIs), load-oriented reliability indices (LORIs), and the security of the electric distribution system (EDS). This is achieved through the optimal placement and sizing of wind turbine distributed generators (WTDGs) and superconducting magnetic energy storages (SMESs), which incorporate DSTATCOM functionality. The LORIs include energy not supplied (ENS) and average energy not supplied (AENS), while the CORIs consist of the system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), average service unavailability index (ASUI), and customer average interruption duration index (CAIDI). The network security index (NSI), which assesses the risk of current flow in lines approaching critical levels, is also examined. A multi-objective function based on optimized weight factors is developed to simultaneously reduce NSI, ASUI, ENS, SAIDI, and SAIFI using an enhanced walrus optimization algorithm (EWaOA) along with sensitivity factors analysis. This optimizer is an improved form of the traditional Walrus Optimization Algorithm (WaOA), designed to balance exploration and exploitation stages better, thereby avoiding local optima and improving overall performance. The EWaOA algorithm's effectiveness is tested on seven benchmark functions and compared with the conventional WaOA and other recent algorithms. The paper also explores the discharge as well as charging real power in addition to initially SOC of SMESs. The proposed method is applied to the IEEE 33-bus EDS, considering a mixed time-varying voltage-dependent (TVVD) load model. The results indicate that the optimal integration of WTDGs and SMESs with DSTATCOM functionality significantly enhances the reliability and security of the tested EDS.

A new multi-objective-stochastic framework for reconfiguration and wind energy resource allocation in distribution network incorporating improved dandelion optimizer and uncertainty

Improving the reliability and power quality of unbalanced distribution networks is crucial for ensuring consistent and reliable electricity supply. In this research, multi-objective optimization of unbalanced distribution networks reconfiguration integrated with wind turbine allocation (MORWTA) is implemented considering uncertainties of networks load, and also wind power incorporating a stochastic framework. The multi-objective function is defined by the minimization of power loss, voltage sag (VS), total harmonic distortion (THD), voltage unbalance (VU), energy not-supplied (ENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and momentary average interruption frequency (MAIFI). A new improved dandelion optimizer (IDO) with adaptive inertia weight is recommended to counteract premature convergence to identify decision variables, including the optimal network configuration through opened switches and the best location and size of wind turbines in the networks. The stochastic problem is modeled using the 2m + 1 point estimate method (PEM) combined with K-means clustering, taking into account the mentioned uncertainties. The proposed stochastic methodology is implemented on three modified 33-bus, and unbalanced 25-, and 37-bus distribution networks. The results demonstrated that the MORWTA enhanced all study objectives in comparison to the base networks. The results also demonstrated that the IDO had superior capability to solve the deterministic- and stochastic-MORWTA in comparison to the conventional DO, grey wolf optimizer (GWO), particle swarm optimization (PSO), and arithmetic optimization algorithm (AOA) in terms of achieving greater objective value. Moreover, the results demonstrated that when the stochastic-MORWTA model is considered, the power loss, VS, THD, VU, ENS, SAIFI, SAIDI, and MAIFI are increased by 18.35%, 9.07%, 10.43%, 12.46%, 11.90%, 9.28%, 12.16% and 14.36%, respectively for 25-bus network, and also these objectives are increased by 12.21%, 10.64%, 12.37%, 9.82%, 14.30%, 12.65%, 12.63% and 13.89%, respectively for 37-bus network compared to the deterministic-MORWTA model, which is related to the defined uncertainty patterns.

Eval_uasi Pemasangan LBSM Loopring untuk Menurunkan SAIDI pada Penyulang PKR 16 dan Riyal di Wilayah Kerja ULP Pulung Kencana

The reliability and continuity of electric power supply are heavily reliant on the maneuverability of existing feeders. To enhance this capability, the installation of LBSM (Load Break Switch Motorized) devices is essential for feeders lacking the ability to maneuver to alternative feeders. This LBSM installation will be implement on the Riyal and PKR 16 feeders at ULP Pulung Kencana. The Riyal feeder receives power from the Mesuji Substation, while the PKR 16 feeder is supplied by the Pakuon Ratu Substation. Both feeders currently lack switches for maneuvering in the event of disturbances or maintenance, which significantly contributes to high SAIDI (System Average Interruption Duration Index) rates. To determine the optimal installation points for the LBSM, a thorough analysis was conducted, prioritizing the closest proximity between the two feeders and incorporating topologic calculations using the Digsilent PowerFactory 15.1 software. This approach ensures optimal performance of the distribution network. The installation of the LBSM loopring is anticipated to significantly improve network maneuverability, thereby reducing the SAIDI rates at ULP Pulung Kencana.

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

Fault indicator placement in distribution systems: Improving the criteria of quality of service

Distribution utilities need to ensure fast fault location to improve the energy supply service, given that, in the event of an outage scenario, the service restoration process starts after the fault has been localized. Minimizing the time required for fault location leads to a reduction in the system average interruption duration index (SAIDI), a crucial metric that all utilities strive to meet. The use of fault indicators can facilitate the fault location process in distribution systems. The challenge lies in determining the optimal placement of these fault indicators to facilitate the fault location process effectively. This paper presents a metaheuristic-based framework that takes into account both commercial and operational criteria to optimize the placement of fault indicators in distribution systems. This tool considers essential practical factors to assist utilities in achieving minimal SAIDI by optimizing the allocation of fault indicators. The method is tested for a 135-bus distribution feeder to verify its practicability. The numerical results demonstrate that the proposed methodology improves the expected SAIDI, leading to better energy supply service for customers.

Comparative analysis of the reliability assessment of commercial and residential feeders in the power distribution utility of Nigeria

Reliability studies serve as valuable tools for assessing and optimizing system performance. Utilities with higher reliability indices are more likely to achieve break-even points due to significantly reduced downtime. This paper explores a comparative assessment of two 11 kV feeders supplying electricity to residential and commercial customers, addressing concerns about distribution system reliability in Nigeria and its impact on the country's GDP. The study involves a comprehensive reliability analysis, utilizing a flowchart to outline procedural steps and employing the ETAP Software program for data analysis collected over a one-month period from a power utility company. The data encompass operational parameters such as day-hourly consumption, outage records, and network equipment data. Results indicate higher reliability indices in the commercial feeder compared to the residential feeder, with the Customer Average Interruption Duration Index (CAIDI) being lower in the commercial feeder. The research underscores the significance of reliability assessment in improving operational efficiency, facilitating maintenance planning, and enhancing customer satisfaction.

Statistical characterization of reliability indices in medium voltage networks using a Monte Carlo-based method

Distribution companies are compelled to provide high-quality services to their customers in compliance with existing regulations. Key performance indicators such as system average interruption duration index and system average interruption frequency index are commonly employed to assess the quality of service provided by these utilities. In certain countries, distribution system operators may face penalties or receive bonuses based on their performance within specified thresholds in their reliability indices. This paper introduces a novel, integrated algorithm, based on Monte Carlo simulations, for conducting a risk assessment in real MV networks using local data from the distribution system operator. The proposed technique is tested in a real MV network, and the obtained results are analyzed both at feeder level and for the entire network. The feeder-level results might be used to identify the sections of the network with the worst quality of power supply, based on the estimated KPIs. The overall results of the study offer valuable insights into the likelihood of specific KPI values using probability density function. The Weibull distribution and log-logistic distribution are found to be those with the best-fitting probability density functions for the two indices evaluated in this work. Furthermore, a penalty-based rate approach is presented as an application of the proposed method to assess the economic risk of the MV network. The findings reveal that the test system is expected to incur penalties amounting to 7.55 million dollars per year.

Reliability and Availability of Renewable Energy Sources in Kenya: A Review

The research sought to evaluate the reliability and availability of renewable energy sources in Kenya by examining the available literature regarding the state of energy production and consumption of the renewable energy sources. Several studies have been conducted to establish the capacity of the renewable electrical power sources including hydroelectric power, geothermal sources, wind power, solar energy and biofuels. Regarding production of electricity, the existing capacity is barely able to keep up with demand. The research used systematic review as the methodology of collecting relevant data for analysis. Given that more than 38% (2021) of Kenya’s electricity comes from hydropower, the situation is particularly difficult during the dry months of the year when water levels are low. Capacity gaps are then compensated by expensive thermal generation based on fossil fuels. Geothermal power has the capacity to be the most significant source of renewable energy with a potential of up to 7000MW. The reliability indices of the renewable energy sources, based on System Average Interruption Frequency Index (SAIFI) are at 1.98 (SAIFI) and at 4.37, based on the Customer Average Interruption Duration Index (CAIDI), which are below the international global standards. The research concluded that increased consumption of electricity, availability of alternate energy sources, amount of restoration time and number of planned interruptions have a significance influence on the reliability of a renewable energy source, with restoration time being the most significant. It is therefore recommended that new technologies and innovations that lead to reduced power interruptions and greenhouse gas emission be adopted.

Reliability Evaluation of Radial Distribution System with different locations and ratings of Photovoltaic

The primary objective of this work is to examine how strategically positioning Photovoltaic (PV) units can have effect on reliability of a distribution system. Feeder-1 of RBTS BUS2 system is used for the reliability evaluation. The study utilizes FMEA technique to evaluate the reliability indices, System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Energy Not Supplied (ENS) and Average Service Availability (ASAI) linked to electrical interruptions in two different situations such as no PV unit and PV units are placed at different locations with different ratings along the feeder. The outcomes of installing PV units at different points along the distribution feeder are examined and compared. The prime focus lies in identifying the reliability based optimal placement of the PV unit, a determination made based on the reliability indices. By systematically analyzing and comparing the reliability indices under varying conditions, the research aims to provide insights into how the strategic positioning of PV units can lead to a substantial enhancement in the overall reliability of the distribution system. The findings contribute to the broader understanding of integrating renewable energy sources for reliability improvement within distribution systems and offer practical guidance for decision-makers in the field of energy distribution and planning.

Reliability Analysis of a Typical 33kV Distribution Network Using MATLAB (A Case Study of Ile-Oluji 33kV Distribution Line)

The significance of a dependable and sufficient electrical power supply in societal development cannot be overstated. The distribution of electrical power holds a crucial position in the power system chain, representing the final stage where it reaches consumers. At this phase, various types of losses, including technical loss and theft, among others are associated. The Ile-Oluji community is situated in Ondo State, a south-western part of Nigeria, A region which is host to federal, state, local and private institutions. This community grapples with inconsistent power supply experiencing intermittent trips without a clear originating cause from components in the Ile-Oluji injection substation and its feeder in Ondo. This study analyses the power distribution patterns in this locality focusing on three primary elements (transformer, switch gear and supply line) extracting data on; failures, outage time, numbers of customers and total hours for the year 2019 to 2022. Reliability indices were employed to assess its performance utilising the MATLAB software. Codes were crafted to extract these indices; Availability, Failure rates, Mean Time to Repair (MTTR), Mean Time Between failure (MTBF), System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and Customer Average Interruption Duration Index (CAIDI). The results show that there is a strong relationship among the reliability indices. It is adduced that the lower the failure rate, the higher the availability of a system. It was concluded that the power system switchgear recorded the highest failure rate amongst the three elements with 0.037341 hrs/year followed by supply line with 0.022541 hrs/year and the transformer with 0.0148 hrs/year in 2022. In corollary, transformer has the highest availability of 0.94441 followed by supply line with 0.81681 and switchgear with 0.78004 in 2021, switchgear recorded the highest failure rates because it is responsible for executing both forced and planned outages.

Analyzing river disruption factors and ecological flow in China's Liu River Basin amid environmental changes.

Water resources variability and availability in a basin affect river flows and sustain river ecosystems. Climate change and human activities disrupt runoff sequences, causing water environmental issues like river channel interruptions. Therefore, determining ecological flow in changing environments is challenging in hydrological research. Based on an analysis of long-term changes in hydrological and meteorological variables and interruption conditions in the semi-arid Liu River Basin (LRB), this study summarizes the controlling factors of river interruption at different temporal and spatial scales and proposes a framework to determine ecological flow under changing environments. Hydrological model and the monthly optimal probability distribution were used to determine the optimal ecological runoff of LRB. The results showed that from 1956 to 2017, precipitation and potential evapotranspiration in the basin showed no significant decreasing trend, but the streamflow significantly decreased, and the downstream interruption worsened, with an average annual interruption duration of 194days at Xinmin Station from 1988 to 2017. The controlling factors of river interruption are as follows: (1) soil and water conservation measures in the upstream significantly reduce the runoff capacity; (2) the operation mode of the controlling reservoir in the middle reaches changes from "all-year discharge" to "winter storage and spring release" to "combined storage and supply," severing the hydraulic connection between upstream and downstream; and (3) siltation in the downstream river channel coupled with over-extraction of groundwater increases the seepage capacity of the river. The monthly ecological flow of Naodehai Reservoir was determined by considering the monthly seepage losses after reconstructing the natural runoff using the SWAT model and determining the optimal probability distribution function for monthly runoff. The findings are important for downstream LRB ecological restoration and for determining the ecological flow of other river basins in changing environments.

Effects of integration of distributed generation on reliability in distribution system

The reliability of power supply to consumers is the main factor that is to be considered while designing, planning and operation of a distribution network. This problem can be solved by incorporating distributed generation (DG) at the consumer side itself. In order to enhance service availability, power quality, reliability, and loss reduction, DG is positioned optimally at the consumer end within the system. The optimal DG location and size is to be calculated in such a way that the total loss in the system should be minimal and cost savings should be maximal. In this study, for the investigation of effects of DG on reliability of distribution system, a practical 11 KV feeder with 41-bus, was employed. The selected practical feeder is modelled using the power world simulator (PWS) software and reliability of the system is verified by calculating various reliability indices such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), average system utility index (ASUI) and average service availability index (ASAI). After applying proposed loss sensitivity factor (LSF) method, the active and reactive power loss reduction are 41.30 % and 38.64 % respectively, least bus voltage is enhanced to 0.9405 pu from 0.8978 pu and the reliability indices are also improved.

Service Restoration in the Medium Voltage Grid Minimizing the SAIDI Contribution After Primary Substation Failures

One of the most widely used reliability measures for the electric grid is the System Average Interruption Duration Index (SAIDI), i.e. how many minutes the average customer is without power per year. Primary substation failures may significantly increase SAIDI, since they affect a large number of customers. This may be avoided by improving the service restoration process, which requires solving complex non-convex combinatorial problems. Hence, this paper proposes an algorithm for smart, SAIDI-optimizing service restoration in meshed MV grids after primary substation failures. We first formulate service restoration as a Mixed-Integer Linear Programming (MILP) problem by using a linearization of the power flow equations that is proper for meshed grids and by applying exact Big M relaxations for linearizing other involved non-convexities. The decisions include among others the travel routes of the field crew and account for the grid security constraints. Then, we propose a low complexity heuristic iterative method that optimally solves the MILP problem.

Improving the reliability of power distribution substations equipment for life extension by considering Condition Based Maintenance

Maintenance of aged power equipment is a proven technique for establishing systems reliability and longevity. It is extensively relevant when combined with Condition Based Maintenance (CBM) approaches for predicting power system infrastructure life, especially with imminent obsolescence. The research focuses on distribution companies with mixed aged power infrastructure, which continuously degradate. A significant setback in older systems is the scarcity of replacement parts leading to unplanned investment in substantial infrastructure due to unexpected failed components that are no longer in production. To guarantee excellent infrastructure health in older equipment, utility companies should consider applying Condition Based Maintenance (CBM) using Artificial Neural Network (ANN) to stabilize older equipment and restore malfunctioning assets. Studies have shown that the life span of older equipment can be extended when adequate maintenance strategies are put in place through the prediction of possible failures using equipment inspections and risk matrix. CBM's strategy improved the Key Performance Indicator (KPIs) System Average Interruption Duration Index (SAIDI) has improved by lowering the average monthly customers power interruptions by 19,15 % lesser time spent without electricity. The System Average Interruption Frequency Index (SAIFI) was improved by lowering the average monthly power interruptions frequency by 20,63% lesser outage frequency. The Distribution Supply Loss Index (DSLI) was improved by dropping the average monthly power losses by 7,5% power losses saved. The Customers Average Interruption Duration Index (CAIDI) was improved by lowering the average customer interruption by 36.1% improvement from the average period before the CBM optimization. The Average System Availability Index (ASAI) was improved by shortening the average annual interruption durations by 1,36%. The ASUI was improved by increasing the average duration of electrical power availability by 1.28%. The optimization of CBM was measured over 24 moths period.

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.