96086 20210902121604.0 doi 10.1016/j.renene.2019.11.048 sideral 114567 ART-2020-114567 eng (orcid)0000-0003-0231-8795 Pinto Maquilón, Edwin Samir Universidad de Zaragoza Evaluation of methods to select representative days for the optimization of polygeneration systems 2020 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/DGA/T55-17R info:eu-repo/grantAgreement/ES/MINECO/ENE2017-87711-R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 8.001 2020 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY 7 / 44 = 0.159 2020 Q1 T1 ENERGY & FUELS 16 / 114 = 0.14 2020 Q1 T1 1.825 2020 Renewable Energy, Sustainability and the Environment 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion (orcid)0000-0002-5161-7209 Serra, Luis M. Universidad de Zaragoza (orcid)0000-0001-7360-4188 Lázaro, Ana Universidad de Zaragoza 5004 590 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Máquinas y Motores Térmi. 151 (2020), 488-502 Renew. energy Renewable Energy 0960-1481 2417461 http://zaguan.unizar.es/record/96086/files/texto_completo.pdf Postprint 465723 http://zaguan.unizar.es/record/96086/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:96086 articulos driver 2021-09-02-08:37:12 ARTICLE