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An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments

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  • Kucuksari, Sadik
  • Khaleghi, Amirreza M.
  • Hamidi, Maryam
  • Zhang, Ye
  • Szidarovszky, Ferenc
  • Bayraksan, Guzin
  • Son, Young-Jun

Abstract

Finding the optimal size and locations for Photovoltaic (PV) units has been a major challenge for distribution system planners and researchers. In this study, a framework is proposed to integrate Geographical Information Systems (GIS), mathematical optimization, and simulation modules to obtain the annual optimal placement and size of PV units for the next two decades in a campus area environment. First, a GIS module is developed to find the suitable rooftops and their panel capacity considering the amount of solar radiation, slope, elevation, and aspect. The optimization module is then used to maximize the long-term net profit of PV installations considering various costs of investment, inverter replacement, operation, and maintenance as well as savings from consuming less conventional energy. A voltage profile of the electricity distribution network is then investigated in the simulation module. In the case of voltage limit violation by intermittent PV generations or load fluctuations, two mitigation strategies, reallocation of the PV units or installation of a local storage unit, are suggested. The proposed framework has been implemented in a real campus area, and the results show that it can effectively be used for long-term installation planning of PV panels considering both the cost and power quality.

Suggested Citation

  • Kucuksari, Sadik & Khaleghi, Amirreza M. & Hamidi, Maryam & Zhang, Ye & Szidarovszky, Ferenc & Bayraksan, Guzin & Son, Young-Jun, 2014. "An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments," Applied Energy, Elsevier, vol. 113(C), pages 1601-1613.
  • Handle: RePEc:eee:appene:v:113:y:2014:i:c:p:1601-1613
    DOI: 10.1016/j.apenergy.2013.09.002
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    References listed on IDEAS

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    1. Gómez, M. & López, A. & Jurado, F., 2010. "Optimal placement and sizing from standpoint of the investor of Photovoltaics Grid-Connected Systems using Binary Particle Swarm Optimization," Applied Energy, Elsevier, vol. 87(6), pages 1911-1918, June.
    2. Strzalka, Aneta & Alam, Nazmul & Duminil, Eric & Coors, Volker & Eicker, Ursula, 2012. "Large scale integration of photovoltaics in cities," Applied Energy, Elsevier, vol. 93(C), pages 413-421.
    3. Gastli, Adel & Charabi, Yassine, 2010. "Solar electricity prospects in Oman using GIS-based solar radiation maps," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(2), pages 790-797, February.
    4. Lukač, Niko & Žlaus, Danijel & Seme, Sebastijan & Žalik, Borut & Štumberger, Gorazd, 2013. "Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data," Applied Energy, Elsevier, vol. 102(C), pages 803-812.
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