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Optimal placement and sizing from standpoint of the investor of Photovoltaics Grid-Connected Systems using Binary Particle Swarm Optimization

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  • Gómez, M.
  • López, A.
  • Jurado, F.

Abstract

This paper introduces a Binary Particle Swarm Optimization based method to accomplish optimal location and size of a Photovoltaics Grid-Connected System (PVGCS) for distributed power generation. The main technical constraint is the maximum installed peak power, which is limited for utilities Power Distributor Company. The fitness function to be optimized is the profitability index. A fair comparison between the proposed algorithm and other methods is performed. For such goal, convergence curves of the average profitability index versus number of iterations are computed. The proposed algorithm reaches a better solution than genetic algorithms when considering similar computational cost (similar number of evaluations).

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:6:p:1911-1918
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    References listed on IDEAS

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    1. Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
    2. Arán Carrión, J. & Espín Estrella, A. & Aznar Dols, F. & Zamorano Toro, M. & Rodríguez, M. & Ramos Ridao, A., 2008. "Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2358-2380, December.
    3. Mondol, Jayanta Deb & Yohanis, Yigzaw G. & Norton, Brian, 2007. "The impact of array inclination and orientation on the performance of a grid-connected photovoltaic system," Renewable Energy, Elsevier, vol. 32(1), pages 118-140.
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    1. Yu, Shiwei & Wei, Yi-Ming & Fan, Jingli & Zhang, Xian & Wang, Ke, 2012. "Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization," Applied Energy, Elsevier, vol. 92(C), pages 552-562.
    2. Askarzadeh, Alireza & Rezazadeh, Alireza, 2013. "Artificial bee swarm optimization algorithm for parameters identification of solar cell models," Applied Energy, Elsevier, vol. 102(C), pages 943-949.
    3. Fu, Xueqian & Chen, Haoyong & Cai, Runqing & Yang, Ping, 2015. "Optimal allocation and adaptive VAR control of PV-DG in distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 173-182.
    4. Ullah, Hayat & Kamal, Ijlal & Ali, Ayesha & Arshad, Naveed, 2018. "Investor focused placement and sizing of photovoltaic grid-connected systems in Pakistan," Renewable Energy, Elsevier, vol. 121(C), pages 460-473.
    5. Wu, Xiaomei & Feng, Qijin & Bai, Chenchen & Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei, 2021. "A novel fast-charging stations locational planning model for electric bus transit system," Energy, Elsevier, vol. 224(C).
    6. Sommerfeldt, Nelson & Madani, Hatef, 2017. "Revisiting the techno-economic analysis process for building-mounted, grid-connected solar photovoltaic systems: Part one – Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1379-1393.
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    8. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).

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