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Optimization of Hybrid Renewable Energy System for a Sustainable Poultry Farm

Wekpa Spencer Chimezunum, Prof Roland Uhunmwangho, Engr Dr Chizindu Stanley, Esobinenwu, I

Abstract

This paper presents the design and performance evaluation of an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm integrated into a hybrid photovoltaic, wind, and battery system tailored for a sustainable poultry farm in Mgbumodu community, Rumuji in Rivers State, Nigeria. MATLAB/Simulink simulations compared ANFIS with Perturb-and-Observe (P&O) and incremental Conductance (Inc.Cond.) methods under varying solar irradiance and wind speeds. Results obtained shows that the proposed hybrid renewable energy system can meet the daily energy demand of 4497.035kWh for the poultry farm and 53964.42kWh yearly with a renewable fraction of 94.3%, reduced the transient response time to 0.13seconds, stabilized battery state of charge between 48-85%, and achieved the lowest levelized cost of energy (0.20$/kWh) for an optimized integrated hybrid renewable energy system in Mgbumodu community, Rumuji with 55.8% diesel savings.

Keywords

Hybrid renewable energy systemANFIS ControlMPPTPhotovoltaic SystemWind

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