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Evaluation of methods to select representative days for the optimization of polygeneration systems

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  • Pinto, Edwin S.
  • Serra, Luis M.
  • Lázaro, Ana

Abstract

The optimization of polygeneration systems considering hourly periods throughout one year is a computationally demanding task, and, therefore, methods for the selection of representative days are employed to reproduce reasonably the entire year. However, the suitability of a method strongly depends on the variability of the time series involved in the system. This work compares the methods Averaging, k-Medoids and OPT for the selection of representative days by carrying out the optimization of grid-connected and standalone polygeneration systems for a building in two different locations. The suitability of the representative days obtained with each method were assessed regarding the optimization of the polygeneration systems. Sizing errors under 5% were achieved by using 14 representative days, and the computational time, with respect to the entire year data, was reduced from hours to a few seconds. The results demonstrated that the Averaging method is suitable when there is low variability in the time series data; but, when the time series presents high stochastic variability (e.g., consideration of wind energy), the OPT method presented better performance. Also, a new method has been developed for the selection of representative days by combining the k-Medoids and OPT methods, although its implementation requires additional computational effort.

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  • Pinto, Edwin S. & Serra, Luis M. & Lázaro, Ana, 2020. "Evaluation of methods to select representative days for the optimization of polygeneration systems," Renewable Energy, Elsevier, vol. 151(C), pages 488-502.
    • Handle: RePEc:eee:renene:v:151:y:2020:i:c:p:488-502
    DOI: 10.1016/j.renene.2019.11.048
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    as
    1. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    2. Chen, Shin-Guang, 2013. "Bayesian approach for optimal PV system sizing under climate change," Omega, Elsevier, vol. 41(2), pages 176-185.
    3. Carvalho, Monica & Lozano, Miguel A. & Serra, Luis M., 2012. "Multicriteria synthesis of trigeneration systems considering economic and environmental aspects," Applied Energy, Elsevier, vol. 91(1), pages 245-254.
    4. Tremeac, Brice & Meunier, Francis, 2009. "Life cycle analysis of 4.5Â MW and 250Â W wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 2104-2110, October.
    5. Wakui, Tetsuya & Yokoyama, Ryohei, 2015. "Optimal structural design of residential cogeneration systems with battery based on improved solution method for mixed-integer linear programming," Energy, Elsevier, vol. 84(C), pages 106-120.
    6. Schütz, Thomas & Schraven, Markus Hans & Fuchs, Marcus & Remmen, Peter & Müller, Dirk, 2018. "Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis," Renewable Energy, Elsevier, vol. 129(PA), pages 570-582.
    7. Peters, Jens F. & Baumann, Manuel & Zimmermann, Benedikt & Braun, Jessica & Weil, Marcel, 2017. "The environmental impact of Li-Ion batteries and the role of key parameters – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 491-506.
    8. Tapia-Ahumada, K. & Pérez-Arriaga, I.J. & Moniz, E.J., 2013. "A methodology for understanding the impacts of large-scale penetration of micro-combined heat and power," Energy Policy, Elsevier, vol. 61(C), pages 496-512.
    9. Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Bernal-Agustín, José L., 2014. "Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems," Applied Energy, Elsevier, vol. 115(C), pages 242-253.
    Full references (including those not matched with items on IDEAS)

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