Interruption Cost Research Articles

Cost-effective reliability level in 100% renewables-based standalone microgrids considering investment and expected energy not served costs

Loss of load probability (LOLP) and expected energy not served (EENS) are commonly used in electrical power systems to evaluate reliability. LOLP defined as the probability that available generation capacity will be inadequate to supply customer demand. EENS defined as the expected amount of energy not being served to consumers by the system during the period considered due to system capacity shortages or unexpected power outages. Loss of Load Frequency (LOLF) is referred to a number of loss of load (LOL) event happened in the operation life span of the SMG. Loss of Load Reduction (LOLR) is defined as the required reduction in LOLF to obtain a specific reliability level. While power systems are designed to minimize LOLP and EENS, this is constrained by the total cost: investment cost, operation and maintenance cost, and cost of customer interruption (CCI). This research considers Standalone Microgrid (SMG), also known as Autonomous Microgrid which only operates in off-grid mode and cannot be connected to wider electrical power system. When designing a 100 % renewable energy integrated SMGs, it is crucial to determine the cost-effective reliability level (CERL). The CERL occurs when the total cost is minimum. This research proposes an approach to calculate the CERL for a fully renewable SMG. An analytical formulation is proposed to represent the LOLR needed to obtain a specific reliability level as a function of the required size of reliability improvement alternatives. The CCI is evaluated using LOLF and EENS indices. Finally, the total cost of the SMG system is evaluated for each reliability level. Consequently, the total cost of the SMG system is expressed as a function of reliability levels, and the minimum value of total cost and the corresponding reliability level are evaluated. In this research, a Monte Carlo Simulation (MCS) approach is used to find hourly LOLF, considering 25 years (219,000 h) of SMG lifespan, regression analysis is used for an analytical formulation, and mixed integer linear programming (MILP) is used for the investment decision making based on a cost minimisation approach. The result demonstrates that the CERL of the SMG system evaluated in the case study is 98.71 %.

Analysis of Optimal HVDC Back-to-Back Placement Based on Composite System Reliability

HVDC is a promising interconnection solution for connecting asynchronous systems and ensuring power control. In Indonesia, a remote industrial system in Sumatra is experiencing load growth and has the option to draw power from the Sumatra system. However, due to frequency differences, the use of HVDC is crucial. The Generation Expansion Planning has proposed six converters but not their interconnection points. This study will determine the most reliable interconnection locations. The chosen converters are modular multilevel converters (MMCs) with high modularity. The converter reliability modeling considers voltage levels, the number of modules, and redundancy strategies. This modeling is then implemented at the power system level to obtain the best placement at the available high-voltage (HV) substation options. Determining the best placement is based on the optimal reliability index. The optimal placement also includes the option to convert from HV to medium-voltage (MV) interconnection. MV interconnection offers higher flexibility but tends to be more expensive. The availability for HV converters is 99.69%, while for MV converters, it is slightly higher, at 99.81%. Additionally, converting from HV to MV reduces the SAIFI (system average interruption frequency index) from 0.2668 to 0.2284 occurrences per year, lowering the interruption cost from 7.804 million USD to 5.737 million USD per year. The sensitivity of interruption, investment, and maintenance costs shows that converting at least one HV converter to MV remains economical. In this case study, the optimal converter placement includes Area VI–2, recommended for conversion from HV to a more distributed MV configuration, improving reliability and economic efficiency.

Interruption Cost Estimation for Value-Based Reliability Investment in Emerging Smart Grid Resources

Growing uncertainty in supply and demand in power systems causes significant challenges in maintaining supply reliability at affordable costs. Power grids are expected to undergo substantial transformations to address these challenges with upgrades and integration of emerging smart technologies that require significant investment costs. A value-based reliability assessment of these grid technologies is necessary to justify the worth of these investments. A key parameter required in such an assessment is the cost of power interruptions originating from transmission system failures. The interruption cost data available in published reports and past surveys relate to generation inadequacy since generation facilities comprise the most capital-intensive investment of an electric utility. Customer interruptions due to a lack of generation mainly occur due to generation failures during the peak demand period, whereas interruptions due to transmission component failures can occur at other periods with specific probabilities. This paper presents a methodology to estimate the cost of outages originating from transmission asset failures, which proposes a sector period model for each customer sector to obtain associated demand-normalized interruption costs. The proposed method can also be used to decide investment in grid resiliency enhancement against extreme weather that mainly impacts the grid network facilities.

Machine-learning techniques for enhancing electricity theft detection considering transformer reliability and supply interruptions

Machine-learning techniques are extensively used in fraud detection and have a huge potential application in electricity theft detection. Electricity theft occurs when consumers manipulate their electricity consumption to be recorded as lower than the actual, resulting in reduced revenue for the electricity supplier. This study explores the application of advanced machine-learning techniques to detect electricity theft, focusing on the impact on supply of electricity to electricity usage pattern. We used a dataset that contains supply interruptions influenced by transformer reliability to construct a robust detection model. This model integrates data analytic techniques from decision trees, support vector machines, k-nearest neighbors, and logistic regression classifiers. After selecting the optimal classifier, we used aggregated parameters to improve theft-detection accuracy. A comprehensive sensitivity analysis was used to evaluate the effects of variations in transformer reliability factors on interruption, interruption costs, electricity revenue, and theft-detection performance. Our findings indicate that using optimal aggregated parameters significantly enhances detection accuracy 10.709 percent, 11.435 percent, 4.909 percent, and 2.097 percent from original parameters depend on the number of aggregation in 2-hour, 4-hour, 6-hour, and 12-hour, respectively. The lower transformer reliability leads to increased loss with percentage 66.87 percent, 46.98 percent, 17.22 percent and reduced theft-detection efficiency by 27.32 percent, 19.23 percent, 6.20 percent. depend on reliability factor20 percent, 50 percent, and 80 percent, respectively.

The cost of electricity interruption for manufacturing firms in Ethiopia: valuing outage by applying stated preference approach

ABSTRACT In sub-Saharan African countries, firms are suffering from frequent, long-lasting and random power outage, which makes planning and production activities difficult. Thus, understanding a firm’s cost associated with unreliable power supply is crucial for policymakers and potential investors in the energy sector. To this end, we estimated the economic cost of power interruption to manufacturing firms in selected cities of Oromia Regional State in Ethiopia using a mixed logit model. We collected data from a sample of 600 manufacturing firms. The result shows that a firm’s average cost of power interruption is equivalent to ETB 51,777 (~US$976) per month, which corresponds to a nine-fold of their current monthly electricity bill. In addition, the interruption costs amount to 2.22% of a firm’s gross monthly revenue. Moreover, we observed a considerable variation in our results, which be partly attributed to company size and type of industry where a company belongs to.

Optimal allocation of EV charging station and capacitors considering reliability using a hybrid optimization approach

This paper proposes a novel approach for strategically placing EV charging stations (EVCS) and capacitors within electrical distribution networks (EDN) to mitigate issues arising from the widespread adoption of Electric Vehicles (EVs). The goal is to maximize the net present value (NPV) by reducing energy losses and minimizing system interruption costs while ensuring reliability. To achieve this, the paper introduces a hybrid optimization technique called HGP_CS, which combines Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Cuckoo Search optimization methods. By employing various compensation coefficients to assess failure rates and identify strategies for enhancing NPV, the approach aims to optimize the placement of EVCS and capacitors within the EDN. Validation is carried out in IEEE 33-bus and 118-bus systems to demonstrate the performance of the proposed approach. It is found that the proposed approach, compared to the base case, reduced the energy loss cost by 34.19% and 31.57% and also the Expected Interruption Costs (EIC) by 17.26% and 13.41%, for IEEE 33-bus and 118-bus system, respectively. Additionally, the proposed method is economically beneficial as it can attain higher profits. In IEEE 33- bus and 118 bus systems, the annual net profit using the proposed approach is 36,805 $/year and 271,670.72 $/year, respectively. Thus, the proposed technique enhances the overall system performance and significantly boosts profitability, making it a compelling choice for optimizing the placement of charging stations and reactive power sources in networks.

A hybrid technique for impact of hybrid renewable energy systems on reliability of distribution power system

A hybrid method is proposed for the effect of hybrid renewable energy systems by using photovoltaic and wind turbines based on the reliability of power system distribution. The proposed hybrid technique is the combination of the golden jackal optimization (GJO) and white shark optimization (WSO), and hence, it is named the GJO-WSO technique. The important aim of this proposed technique is to lessen the costs of the distribution system for power outages while meeting the system's needs. The GJO-WSO technique examines how renewable-energy-sources (RESs) affect the dependability of power systems (PSs). The impact of distributed generation (DG) technologies in a power system is analyzed by utilizing reliability measures like energy not supplied and expected interruption cost, (EENS, ECOST). The market pricing, wind and solar system output power, and load uncertainty are analyzed. Next, the proposed technique's performance is evaluated with other techniques. In this, the proposed technique provides a higher accuracy of 99.68 %. So, it concluded that the proposed strategy provides optimum than the present techniques.

BASSAMI Mohammed, ADRIAUCH Amine, BENKACEM Mariem, AZZIOUAZ Fatima, MIMOUNI Hicham, RKAIN Ilham (2024), Mandibular Mass Revealing Vesicular Thyroid Carcinoma A Case Report. International Journal of Innovative Science and Research Technology (IJISRT) IJISRT24MAY099, 188-192. DOI: 10.38124/ijisrt/IJISRT24MAY099. https://www.ijisrt.com/mandibular-mass-revealing-vesicular-thyroid-carcinoma-a-case-report

Fault is a regular incidence on distribution power system because distribution lines are always exposed to the environment with high probability of fault occurrence which when it happens, can cause hazardous conditions, equipment failures, power instability, huge financial loss and other forms of setback. In order to avoid these setbacks, it is necessary to detect and locate the fault on the network as fast as possible so as to prevent power system damages and reduce system downtime. This research study designed a smart fault location system model that detected and located ground faults at the point of occurrence using a developed 33 kV Ubulu-Uku radial distribution system as a test feeder and then formulated fault location equations which resulted into one single equation for all ground fault types on the network. The designed algorithm was evaluated on MATLAB 2023a environment using different line impedances of 0.01 Ω, 0.15 Ω, 0.35 Ω, 0.50 Ω, and 0.65 Ω which produced various ground faults located along path 6 section 17, path 8 secAtion 21, path 8 section 21, path 4 section 12 and path 8 section 21 with distance of fault from the main substation obtained at 3.51 km, 3.93 km, 4.03 km, 4.81 km, and 4.21 km. The results presented show performance of the designed algorithm and can be encouraged for practical implementation with promising result which will achieve some benefits like precise fault location information analysis, reduce the overall response time spent by maintenance crew to locate fault and reduce cost of operational maintenance and supply interruptions.

Optimal adaptive decentralized under-frequency load shedding for islanded smart distribution network considering wind power uncertainty

The increasing share of renewable distributed generations has raised a challenge about the frequency stability. Under-frequency load shedding (UFLS) can deal with this challenge, but it usually considers a prorated load-shedding strategy and neglects the impact of uncertainty from renewable generation. This may result in unnecessary additional economic losses from the system blackout. In this paper, a risk-decision-based optimal adaptive decentralized UFLS method is proposed to coordinate the distribution of shedding load in the smart distribution network to avoid unnecessary additional economic losses, while considering the wind power uncertainty. Firstly, the uncertainty from wind power generation is modeled by confidence intervals and evaluated by the calculation of the conditional value at risk (CVaR). Then, to economically distribute the power deficit among load agents, a decentralized consensus on the incremental cost among decentralized agents is reached, where the interruption cost of the local agent varies with its load as a nonlinear relationship. Finally, a two-layer adaptive risk-decision decentralized UFLS control framework is proposed to enable the frequency stability and economic operation of the smart distribution network. With the proposed strategy, local information sharing and important global information discovery are achieved in a distributed way. Simulations on an improved IEEE 33-bus system show that the proposed strategy can effectively avoid rigid shedding and implement the optimal allocation of the system power deficit.

Economic Evaluation of Mitigation Methods Against Voltage Dips and Interruptions Based on Stochastic Reliability.

This article describes a method to select the techno-economic optimal method for the mitigation of voltage dips and interruptions. Because of the wide variety in mitigation equipment available on the market, the uncertainty of the grid reliability and the mostly unknown interruption cost, it is a very difficult task to determine which investment is the ‘optimal’ one for a certain location. One of the most challenging issues in finding this optimal solution, is comparing totally different protection methods, each with their degree of protection, and their specific costs. The theoretical approach is considered and the difficulties which make it impractical for use are stated. Also the used investment policy in the industries is briefly treated, based on performed surveys. Next a method is proposed to compare different mitigation methods, avoiding the drawbacks of the theoretical method, based on the reliability indices of the power supply. The influence of the grid reliability is discussed. This method is based on the stochastic character of the electricity distribution reliability.

Measuring the economic and societal value of reliability/resilience investments: case studies of islanded communities

ABSTRACT Large-scale disasters have exposed vulnerabilities in energy systems and interdependent infrastructure, underscoring the importance of proactively mitigating risks to critical infrastructure. This paper focuses on strengthening power system planning by incorporating the costs associated with prolonged and extensive power interruptions to bolster resilience. To achieve this, duration-dependent customer damage functions are developed, capturing the direct, indirect, and societal impacts of power interruptions, ranging from short, localized events to widespread and long-duration ones. The research methodology is applied to three islanded communities facing substantial resilience challenges with limited data availability. Three customer interruption cost surveys are conducted with local electricity customers, yielding valuable insights into duration-dependent customer damage functions for direct, indirect, and monetizable societal costs. Significantly, the power interruption cost estimates derived from these functions vary considerably from those in the contiguous U.S. and even between the different islanded communities, reflecting their distinct hazard profiles and geographical characteristics. In conclusion, this study demonstrates the potential of duration-dependent customer damage functions to enhance power system resilience. It also identifies several areas for further research, paving the way for a more robust and resilient power infrastructure.

Reliability evaluation of distribution network for educational purpose: An analytical approach to results analysis

Despite the decade-long development in distribution networks, they are still unable to fulfil society's increasing need for reliable power supply. Investment is necessary for reliability enhancement, which will increase the consumer supply costs. This paper presents a new strategy to enhance the reliability of the network based on network restructuring. This study offers the cost-worth reliability indices, followed by an associated mathematical model with the objective function to reduce the well-known reliability indices Expected Interruption Cost (ECOST) and Energy Not Served (ENS). A Modified Greedy Search Algorithm (MGSA) is introduced to the network optimization for global optimal configuration. Furthermore, to make the model more efficient and realistic, the suggested approach incorporates the effects of fuse failure probability. The impact of the system's islanded operation on the reliability indices is also evaluated. The simulation results show 3.33 % reduction in System Average Frequency Index (SAIFI) as compared with the Fast Adequacy Analysis Method. The results shows that the value of ENS using the proposed MGSA compared to SA and GA improved by 100 %. The application of the proposed strategy on the distribution network connected at BUS-2 of Roy Billinton Test System (RBTS), shows the accuracy and computational effectiveness of the proposed model compared to the state-of-the-art conventional simulation-based topology-variable-based approaches.

A Fine-grained Semi-supervised Anomaly Detection Framework for Predictive Maintenance of Industrial Assets

Reliable operation of industrial assets is of high priority for businesses where productivity determines the ability to deliver safety-critical products of high quality in a timely manner. The aerospace industry leads the demand for predictive maintenance (PdM). In the manufacturing space, unscheduled down time causes production delay and increases operational costs while introducing potential risks in product quality and on-time delivery. In field application of these products, unexpected breakdown of critical components can result in safety-critical events. Failure events are, therefore, extremely rare in industrial settings. Diverse operating conditions in the manufacturing environment and field applications contribute to the heterogeneous nature of data collected from these assets. This work presents an anomaly detection framework for PdM of industrial assets to address the practical challenges of scarce failure data sources and heterogeneous data across assets. We introduce a fine-grained modeling approach that efficiently accounts for individual asset differences in a semi-supervised fashion which requires only normal operation data for model training. The framework is demonstrated with an industrial 4.0 use case. Vibration sensor data from pumps in one of our manufacturing facilities is ingested to enable PdM with 2 weeks lead time using the proposed framework. This transforms unexpected breakdown time to scheduled maintenance, thereby reducing cost of delays and operation interruptions. The systematic implementation of the framework in the case study covers the practical production aspects including data quality evaluation, model training, optimization and daily serving of predictions. Furthermore, implementation challenges and recommendations are discussed based on the end-to-end solution implementation experiences.

The steep cost of HIV treatment interruptions.

The steep cost of HIV treatment interruptions.

Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks

The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms.

Midline Compared With Peripheral Intravenous Catheters for Therapy of 4 Days or Longer in Pediatric Patients

Peripheral intravenous catheters (PIVCs) frequently fail during treatment causing therapy interruption, pain, recatheterization, and additional health care costs. Midline catheters (MCs) may improve functional dwell time and reduce failure compared with traditional PIVCs. To compare device failure of MCs with PIVCs. This was a pragmatic, randomized clinical superiority trial with an embedded internal pilot study conducted from July 2020 to May 2022. The study took place in a quaternary pediatric hospital in Brisbane, Queensland, Australia. Inclusion criteria were patients aged 1 to 18 years requiring peripherally compatible intravenous therapy for 4 days or longer. Patients were randomly assigned 1:1 to receive a PIVC or MC, stratified by age (≤5 years, >5 years). One catheter was studied per patient. The primary outcome was all-cause device failure, defined as premature cessation of device function. Secondary outcomes included number of insertion attempts, insertion failure, pain (on insertion), procedural time, patient/parent satisfaction (with insertion), device dwell time, device complications during dwell time, additional vascular access devices required to complete treatment, clinician satisfaction (at removal), and health care costs. Of the 128 patients randomly assigned to study groups, 127 patients (median [IQR] age, 7 [2-13] years; 71 male [56%]) had a device inserted, with 65 (51.2%) in the PIVC group and 62 (48.8%) in the MC group. All patients were included in the intention-to-treat analysis. Device failure was lower in patients in the MC group (10 [16.1%]) compared with those in the PIVC group (30 [46.2%]; odds ratio [OR], 0.22; 95% CI, 0.10-0.52; P <.001). MCs were associated with fewer insertion attempts (mean difference [MD], -0.3; 95% CI, -0.5 to 0; P = .04), increased dwell time (MD, 66.9 hours; 95% CI, 36.2-97.5 hours; P <.001), and fewer patients required additional vascular access devices to complete treatment in the MC group (4 [6.5%]) and PIVC group (19 [29.2%]; OR, 0.17; 95% CI, 0.05-0.52; P = .002). Compared with PIVCs, use of MCs was associated with greater patient (9.0 vs 7.1 of 10; P = .002) and parent (9.1 vs 8.2 of 10; P = .02) satisfaction and lower health care costs (AUS -$151.67 [US -$101.13] per person; 95% credible interval, AUS -$171.45 to -$131.90 [US -$114.20 to -$87.95]). Findings suggest that MC insertion for patients requiring peripherally compatible intravenous therapy for 4 days or longer should be prioritized to reduce the resource intensive, expensive, and burdensome sequelae of device failure. Australia New Zealand Clinical Trials Registry, ACTRN12620000724976.

The mazing race: Effects of interruptions and benefits of interruption lags in a novel maze-like decision-making paradigm.

Interruptions are an inevitable, and often negative, part of everyday life that increase both errors and the time needed to complete even menial tasks. However, existing research suggests that being given time to prepare for a pending interruption-a lag time-can mitigate some of the interruption costs. To understand better why interruption lags are effective, we present a series of three experiments in which we develop and test a novel sequential decision-making paradigm, the mazing race. We find that interruption lags were only beneficial when participants had a clear strategy for how to complete the task, allowing them to avoid specific errors. In the final experiment, we attempted to use what we learned about the kinds of errors introduced by interruptions to develop a feedback-based intervention, aimed at dealing with situations in which interruption lags are not possible. We found that feedback was, only in certain situations, an effective replacement for an interruption lag. Overall, however, because the usefulness of interruption lags depend on the specific strategy a participant adopts, developing generic interventions to replace interruption lags is likely to be difficult. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Reliability-Oriented Planning Framework for Smart Cities: From Interconnected Micro Energy Hubs to Macro Energy Hub Scale

Energy hubs, as the prospect of future energy systems, are efficient and reliable frameworks for the generation, conversion, storage, and consumption of energy in multicarrier energy systems. Existing urban facilities in smart cities (SCs) can be considered as micro energy hubs (MIEHs), whereas the SC itself can be regarded as a macro energy hub incorporating its MIEHs. Moreover, the interconnection of MIEHs is particularly important to achieve high-reliability and economical smart city energy systems (SCESs). This article presents a framework for reliability-oriented expansion planning of multicarrier energy systems in SCESs. The proposed framework tends to improve energy not supplied and customer interruption cost indices for both heat and electrical loads, as well as the energy index of reliability. The proposed multicarrier energy system facilitates interoperability between different energy carrier systems to supply electricity and heat demand simultaneously. The interconnection of MIEHs is modeled through linearized alternating current optimal power flow and natural gas network constraints, whereas full interaction of the electricity and gas networks is realized by the power-to-gas systems and combined heat and power systems. The effectiveness of the proposed mixed-integer linear programming planning model is demonstrated in the Dättwil district (Switzerland) through four scenarios, analyzing the effect of MIEH interconnection, as well as the gas and electricity interaction within an MIEH. Simulation results confirm the effectiveness of the proposed method due to a significant reduction in total investment cost and a considerable improvement in reliability indices.

A decision-making model for efficient fair electricity rationing under major power outrage emergencies

Major power outages emergencies (MPOEs) are increasingly occurring with greater frequency and wreaking havoc, necessitating the effective decision-making for electricity rationing to mitigate economic losses incurred and maintain social stability. To address this issue, this paper proposes an efficient fair electricity rationing decision-making model under MPOEs, including two methods to quantify the efficiency and fairness of the electricity rationing. Firstly, the inoperability inputoutput model is employed to quantify the efficiency by assessing the business interruption costs caused by MPOEs. Secondly, the fairness is quantified by the fairness perception of the affected regions, which consider their bounded rational comparisons based on the Prospect theory. Then, the NSGA-II is utilized to solve the model. Finally, Sichuan MPOE in 2022 is designed as a numerical experiment to validate the proposed model, accompanied by corresponding discussions to demonstrate the impact of “supercities” on electricity rationing and the significance of the balance between efficiency and fairness.

A new hybrid CBSA-GA optimization method and MRMI-LSTM forecasting algorithm for PV-ESS planning in distribution networks

Renewable energy sources have gained significant attention as a priority option for cost reduction and emissions reduction from fossil fuels. However, their utilization introduces uncertainty and decreases reliability in distribution networks. To effectively harness these resources and mitigate their uncertainties, this paper proposes a model for determining the optimal size and location of photovoltaic (PV) systems and energy storage systems (ESS) in electrical energy distribution networks. The objective is to minimize network power losses, voltage deviations, customer interruption cost, PV system installation cost, and ESS installation cost. To achieve this, the study employs a hybrid forecasting model based on MRMI feature selection and LSTM deep neural network to forecast daily solar power and electrical load for 12 different days across various months of the year, considering the desired electrical energy distribution system. Subsequently, these predicted values serve as 12 scenarios to determine the optimal location and size of PV systems and ESSs. The optimization problem is tackled using a novel hybrid optimization method known as the Coot Bird Search Algorithm-Genetic Algorithm (CBSA-GA). The efficiency of the proposed algorithm is evaluated by comparing its results with GA, PSO, and CBSA algorithms. Among the optimization methods discussed in this paper, CBSA-GA excels in reducing the objective function by accurately determining the capacities of PV resources and ESS. Compared to scenarios where distributed generation (DG) is not integrated into the distribution network, the implementation of CBSA-GA algorithm results in significant improvements in the objective function for Cases 1 to 3. The reductions achieved in the objective function are 9.10 %, 11.88 %, and 24.39 % for Cases 1, 2, and 3, respectively. These findings highlight the effectiveness of CBSA-GA in optimizing the integration of DG resources, specifically in terms of reducing the objective function and improving system performance.