Homer Pro-Based Approach for Designing and Optimizing a Grid-Tied Hybrid Renewable Energy System for a Rural Community

Abstract

One of the most important elements in our planet is energy, there is a daily rise in the global demand of energy and so renewable sources of energy need to be employed to meet this demand. We understand that renewable sources of energy are not continuous; to resolve these issues, research have proven that combining several renewable sources in a hybrid form would compensate for the discontinuity and decrease emission of harmful chemicals to the environment. Based on the epileptic power supply issues for residents in Sango Ota, an alternative source of power is suggested in this study. Sango Ota is a fast-developing area with population increasing daily, and load demand needs to be met for residential and commercial activities. This paper presents the optimal planning, design, operation, and techno-economic assessment of a sustainable hybrid renewable energy Microgrid system using Sango Ota, Ogun State, Nigeria as a case study. The sources of renewable energy considered in this study are wind turbines, solar photovoltaic (PV), diesel generator and storage system (battery). The study employs HOMER Pro application and algorithm to simulate models of the design. This study helps to proffer a solution or an alternative means of generating power at a lesser cost with several options as backup. The study focuses mainly on the residential whose lifetime is 25 years. The Return on investment (ROI) of this study is positive, which indicates that the project is viable, profitable, efficient and should yield a payback after 4 years. This study has been able to show that a sustainable power supply can be generated using hybrid renewable energy sources from wind, solar, diesel generator and battery. Enough energy was generated to meet the load demand, it was cost effective and the capital would be regained in 4 years. Auto size Gen was preferable for the simulation as it is flexible and adjustable to meet the load demand.