000096086 001__ 96086
000096086 005__ 20210902121604.0
000096086 0247_ $$2doi$$a10.1016/j.renene.2019.11.048
000096086 0248_ $$2sideral$$a114567
000096086 037__ $$aART-2020-114567
000096086 041__ $$aeng
000096086 100__ $$0(orcid)0000-0003-0231-8795$$aPinto Maquilón, Edwin Samir$$uUniversidad de Zaragoza
000096086 245__ $$aEvaluation of methods to select representative days for the optimization of polygeneration systems
000096086 260__ $$c2020
000096086 5060_ $$aAccess copy available to the general public$$fUnrestricted
000096086 5203_ $$aThe 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.
000096086 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T55-17R$$9info:eu-repo/grantAgreement/ES/MINECO/ENE2017-87711-R
000096086 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000096086 590__ $$a8.001$$b2020
000096086 591__ $$aGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY$$b7 / 44 = 0.159$$c2020$$dQ1$$eT1
000096086 591__ $$aENERGY & FUELS$$b16 / 114 = 0.14$$c2020$$dQ1$$eT1
000096086 592__ $$a1.825$$b2020
000096086 593__ $$aRenewable Energy, Sustainability and the Environment$$c2020$$dQ1
000096086 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000096086 700__ $$0(orcid)0000-0002-5161-7209$$aSerra, Luis M.$$uUniversidad de Zaragoza
000096086 700__ $$0(orcid)0000-0001-7360-4188$$aLázaro, Ana$$uUniversidad de Zaragoza
000096086 7102_ $$15004$$2590$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Máquinas y Motores Térmi.
000096086 773__ $$g151 (2020), 488-502$$pRenew. energy$$tRenewable Energy$$x0960-1481
000096086 8564_ $$s2417461$$uhttps://zaguan.unizar.es/record/96086/files/texto_completo.pdf$$yPostprint
000096086 8564_ $$s465723$$uhttps://zaguan.unizar.es/record/96086/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000096086 909CO $$ooai:zaguan.unizar.es:96086$$particulos$$pdriver
000096086 951__ $$a2021-09-02-08:37:12
000096086 980__ $$aARTICLE