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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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