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Bayesian approach for optimal PV system sizing under climate change


  • Chen, Shin-Guang


This paper proposes a novel statistical approach for optimally sizing a stand-alone photovoltaic (PV) system under climate change. Traditionally, the irradiation profile of a typical day or year is used to size PV systems. However, facing the global warming crisis as well as the fact that no two years would have the same weather condition for a single site, this often makes the traditional way failed in the extreme weather conditions. This paper presents a method to statistically model the trend of climate change year by year and put it into the sizing formula, so that the results are optimal for the current weather condition and confidential for the future as well. Hence, the suitable sizes for the PV array and the number of batteries are obtained by pure computation. This is different from the traditional simulation-based sizing curve method. An economic optimization procedure is also presented. In addition to the capital and maintenance costs, a penalty cost is introduced when service fails. A new statistic-based reliability index, the loss of power probability, in terms of threshold-based Extreme Value Theory is presented. This index indicates the upper bound reliability for applications and provides rich information for many extreme events. A technological and economic comparison among the traditional daily energy balance method, sizing curve method and the proposed approach is conducted to demonstrate the usefulness of the new method.

Suggested Citation

  • Chen, Shin-Guang, 2013. "Bayesian approach for optimal PV system sizing under climate change," Omega, Elsevier, vol. 41(2), pages 176-185.
  • Handle: RePEc:eee:jomega:v:41:y:2013:i:2:p:176-185
    DOI: 10.1016/

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    References listed on IDEAS

    1. Yang, Hongxing & Wei, Zhou & Chengzhi, Lou, 2009. "Optimal design and techno-economic analysis of a hybrid solar-wind power generation system," Applied Energy, Elsevier, vol. 86(2), pages 163-169, February.
    2. Zhou, Wei & Lou, Chengzhi & Li, Zhongshi & Lu, Lin & Yang, Hongxing, 2010. "Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems," Applied Energy, Elsevier, vol. 87(2), pages 380-389, February.
    3. Sherali, Hanif D & Staschus, Konstantin, 1984. "Solar energy in electric utility planning: A linear programming approach," Omega, Elsevier, vol. 12(2), pages 165-174.
    4. Regnier, Eva, 2008. "Doing something about the weather," Omega, Elsevier, vol. 36(1), pages 22-32, February.
    5. Fragaki, A. & Markvart, T., 2008. "Stand-alone PV system design: Results using a new sizing approach," Renewable Energy, Elsevier, vol. 33(1), pages 162-167.
    6. Papadopoulos, Agis & Karagiannidis, Avraam, 2008. "Application of the multi-criteria analysis method Electre III for the optimisation of decentralised energy systems," Omega, Elsevier, vol. 36(5), pages 766-776, October.
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    Cited by:

    1. Wei, Yi-Ming & Mi, Zhi-Fu & Huang, Zhimin, 2015. "Climate policy modeling: An online SCI-E and SSCI based literature review," Omega, Elsevier, vol. 57(PA), pages 70-84.
    2. repec:eee:energy:v:126:y:2017:i:c:p:392-403 is not listed on IDEAS
    3. Ying-Yi Hong & Yuan-Ming Lai & Yung-Ruei Chang & Yih-Der Lee & Pang-Wei Liu, 2015. "Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables," Energies, MDPI, Open Access Journal, vol. 8(4), pages 1-20, March.


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