Authors : W. Ikonwa; E. C. Obuah

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/p3kvm92s

Scribd : https://tinyurl.com/4j553937

DOI : https://doi.org/10.38124/ijisrt/26feb749

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The growing adoption of photovoltaic (PV) distributed generation (DG) in Nigerian distribution networks introduces operational uncertainty arising from variable solar irradiance and fluctuating load demand. This work develops a probabilistic assessment framework that combines the Three-Point Estimation Method (3-PEM) with a Genetic Algorithm (GA) to evaluate and optimize PV integration in a radial distribution network. Load and PV uncertainties are incorporated into probabilistic load flow analysis to estimate expected voltage profiles and voltage violation probabilities. The GA is employed to identify optimal PV locations and capacities that enhance voltage stability and reduce network stress. The proposed approach was implemented on the Nigerian 11 kV Ayepe-34 bus radial feeder using MATLAB R2022a. Three PV-DG of size 300kW each was used in the simulation. Gaussian normal distribution was used for the stochastic load variation. The results show that Fixed PV locations were buses 14, 24 and 30. The optimal PV buses using GA were buses 17, 18 and 34. Results indicate that GA-optimized PV placement significantly improves voltage performance and lowers the probability of voltage violations compared to fixed and no-PV scenarios. The framework provides an efficient and practical planning tool for renewable energy deployment in Nigerian distribution systems.

Keywords : Genetic Algorithm, Three-Point Estimation Method, Distributed Generation, Load Variation.

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