Optimal Photovoltaic System Design with Multi-Objective Optimization

Optimal Photovoltaic System Design with Multi-Objective Optimization

Amin Ibrahim, Farid Bourennani, Shahryar Rahnamayan, Greg F. Naterer
Copyright: © 2013 |Pages: 27
DOI: 10.4018/ijamc.2013100104
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Abstract

Recently, several parts of the world suffer from electrical black-outs due to high electrical demands during peak hours. Stationary photovoltaic (PV) collector arrays produce clean and sustainable energy especially during peak hours which are generally day time. In addition, PVs do not emit any waste or emissions, and are silent in operation. The incident energy collected by PVs is mainly dependent on the number of collector rows, distance between collector rows, dimension of collectors, collectors inclination angle and collectors azimuth, which all are involved in the proposed modeling in this article. The objective is to achieve optimal design of a PV farm yielding two conflicting objectives namely maximum field incident energy and minimum of the deployment cost. Two state-of-the-art multi-objective evolutionary algorithms (MOEAs) called Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Generalized Differential Evolution Generation 3 (GDE3) are compared to design PV farms in Toronto, Canada area. The results are presented and discussed to illustrate the advantage of utilizing MOEA in PV farms design and other energy related real-world problems.
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In the last few decades, there has been a large number of studies on single-objective and multi-objective real-life applications (Talbi, 2009). However, most real-life problems are multi-objective problems by nature because they involve variant conflicting objectives. The development of efficient multi-objective metaheuristics such as evolutionary multi-objective algorithms played an integral role in the design of complex energy systems. This section presents the most recent optimization works applied to design solar energy systems.

Varun (2010) implemented a genetic algorithm for maximizing the thermal performance of flat plate solar air heaters to optimize various systems and operating parameters. The basic values like number of glass covers, Irradiance and Reynolds, plate tilt angle, and emissivity of plate are optimized for maximizing thermal performance.

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