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Optimal Photovoltaic System Design with Multi-Objective Optimization

Author

Listed:
  • Amin Ibrahim

    (University of Ontario Institute of Technology, Oshawa, Canada)

  • Farid Bourennani

    (University of Ontario Institute of Technology, Oshawa, Canada)

  • Shahryar Rahnamayan

    (University of Ontario Institute of Technology, Oshawa, Canada)

  • Greg F. Naterer

    (Memorial University of Newfoundland, St. John's, Canada)

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.

Suggested Citation

  • Amin Ibrahim & Farid Bourennani & Shahryar Rahnamayan & Greg F. Naterer, 2013. "Optimal Photovoltaic System Design with Multi-Objective Optimization," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 4(4), pages 63-89, October.
    • Handle: RePEc:igg:jamc00:v:4:y:2013:i:4:p:63-89
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    Cited by:

    1. Yin, Peng-Yeng & Cheng, Chun-Ying & Chen, Hsin-Min & Wu, Tsai-Hung, 2020. "Risk-aware optimal planning for a hybrid wind-solar farm," Renewable Energy, Elsevier, vol. 157(C), pages 290-302.
    2. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.

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