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Bayesian approach for optimal PV system sizing under climate change

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  • Chen, Shin-Guang

Abstract

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.

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  • 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/j.omega.2011.07.013
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    References listed on IDEAS

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    1. 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.
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    2. 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, vol. 8(4), pages 1-20, March.
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    5. Ren, Zhengen & Paevere, Phillip & Chen, Dong, 2019. "Feasibility of off-grid housing under current and future climates," Applied Energy, Elsevier, vol. 241(C), pages 196-211.
    6. Sarhan, Ameen & Hizam, Hashim & Mariun, Norman & Ya'acob, M.E., 2019. "An improved numerical optimization algorithm for sizing and configuration of standalone photo-voltaic system components in Yemen," Renewable Energy, Elsevier, vol. 134(C), pages 1434-1446.
    7. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2020. "Cost minimization of large-scale infrastructure for electricity generation and transmission," Omega, Elsevier, vol. 96(C).
    8. Ramshani, Mohammad & Khojandi, Anahita & Li, Xueping & Omitaomu, Olufemi, 2020. "Optimal planning of the joint placement of photovoltaic panels and green roofs under climate change uncertainty," Omega, Elsevier, vol. 90(C).

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