Title

Using of Evolutionary Optimization Algorithms in Hybrid Renewable Energy Systems

Date of Award

8-2021

Degree Name

Doctor of Philosophy

Department

Electrical and Computer Engineering

First Advisor

Dr. Johnson A. Asumadu

Second Advisor

Dr. Massood Z. Atashbar

Third Advisor

Dr. Chris Cho

Keywords

Optimization algorithms, hybrid renewable energy systems, total net present cost, biomass generator, particle swarm optimization (PSO), remote areas

Abstract

While hybrid renewable energy systems (HRES) increase reliability, they are the technologies used to optimize to ensure cost-effectiveness and technical feasibility. Hence, a comprehensive mathematical model for the basic version of the Hybrid Renewable Dilemma has been developed to implement heuristic algorithms for solving major problems. The model's precision lets the system be used for broader issues that are more complicated.

The Harmony Search (HS), Jaya, and Particle Swarm Optimization (PSO) algorithms in this dissertation are used to optimize the design of a hybrid off-grid renewable energy system comprising a collection of wind/photovoltaic/biomass/battery systems to provide the necessary electrical demand for a remote region in Kingdom of Saudi Arabia using heuristic approaches in a cost-effective, productive, and reliable way. The MATLAB software has been used in all simulations to converge to an optimal solution for the sizing problem. A primary assessment of the proposed system was made using the HOMER program to assess the boundaries of the numbers of the system components that would be included in the proposed system.

The outcomes of the three algorithms are compared, and the one with the most techno-economic optimum unit sizing of the HRES was determined. The hybrid system’s reliability and efficiency are calculated using two factors: the maximum allowable loss of power supply probability (LPSPmax) and minimum allowable excess energy fraction (EFFmin).

A statistical procedure based on the use of an independent t-test sample to be used in the evaluation of different optimization algorithms was presented in this dissertation to assist in determining the best algorithm.

To resolve the optimum configuration of a hybrid renewable energy system, this procedure was applied to five heuristic meta-detection algorithms, namely particle swarm optimization (PSO), Harmony Search (HS), Firefly algorithm (FA), Cultural Algorithm (CA), and Flower Pollination Algorithm (FPA).

The outcomes of the five algorithms (minimum TNPC) were evaluated using the suggested procedure. The results of the analysis indicated that FA was the best in achieving the optimum solution among the five algorithms, followed by FPA.

Access Setting

Dissertation-Abstract Only

Restricted to Campus until

8-15-2031

This document is currently not available here.

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