Multi-Objective Solar Farm Design Based on Parabolic Collectors
Zakiya Alfughi, Shahryar Rahnamayan, and Bekir Yilbas
Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology (UOIT)
2000 Simcoe Street, North Oshawa, Ontario L1H 7K4, Canada
The configuration of solar farms, in which solar collectors are arranged in rows, is related to field and collector characteristics and solar radiation data. The main parameters considered during the optimization of solar farm designs include the number of collector rows, the center-to-center distance between collectors, collector inclination angles, and the rim angles. Solar collectors can be subjected to shading depending on the spacing between the collector rows, collector height and angle, row length, and latitude of the solar field. This study aims to optimize solar farm design by ensuring the farm receives the maximum incident solar energy and incurs the minimum deployment cost. The proposed mathematical model for photovoltaic panels is presented in detail. A multi-objective evolutionary algorithm, a non-dominated sorting genetic algorithm-II (NSGA-II), is used to achieve an optimum solar farm design that incorporates parabolic trough panels. The performances of the parabolic and flat panels are also compared, and the findings are discussed in detail. Based on the obtained results, we can verify that the parabolic PV model could generate more energy than the flat model. However, at the same cost, the flat PV model generated more energy than the parabolic model. There is a trade-off between the absolute values of the various objectives, and a solution can be selected based on the customer’s requirements and desires.
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