000135173 001__ 135173
000135173 005__ 20250923084423.0
000135173 0247_ $$2doi$$a10.1016/j.renene.2024.120540
000135173 0248_ $$2sideral$$a138513
000135173 037__ $$aART-2024-138513
000135173 041__ $$aeng
000135173 100__ $$0(orcid)0000-0002-5801-0602$$aLujano-Rojas, Juan M.$$uUniversidad de Zaragoza
000135173 245__ $$aDesign of small-scale hybrid energy systems taking into account generation and demand uncertainties
000135173 260__ $$c2024
000135173 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135173 5203_ $$aThe adoption of energy systems powered by renewable sources requires substantial economic investments. Hence, selecting system components of an appropriate size becomes a critical step, which is significantly influenced by their distinct characteristics. Furthermore, the availability of renewable energy varies over time, and estimating this availability introduces considerable uncertainty. In this paper, we present a technique for the optimal design of hybrid energy systems that accounts for the uncertainty associated with resource estimation. Our method is based on stochastic programming theory and employs a surrogate model to estimate battery lifespan using a feedforward neural network (FFNN). The optimization analysis for system design was conducted using a genetic algorithm (GA) and the poplar optimization algorithm (POA). We assessed the effectiveness of the proposed technique through a hypothetical case study. The introduction of a surrogate model, based on an FFNN, resulted in an approximation error of 9.6 % for cost estimation and 20.6 % for battery lifespan estimation. The probabilistic design indicates an energy system cost that is 25.7 % higher than that obtained using a deterministic approach. Both the GA and POA achieved solutions that likely represent the global optimum.
000135173 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-123172OB-I00$$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-129801B-I00
000135173 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000135173 590__ $$a9.1$$b2024
000135173 592__ $$a2.08$$b2024
000135173 591__ $$aGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY$$b18 / 102 = 0.176$$c2024$$dQ1$$eT1
000135173 593__ $$aRenewable Energy, Sustainability and the Environment$$c2024$$dQ1
000135173 591__ $$aENERGY & FUELS$$b34 / 182 = 0.187$$c2024$$dQ1$$eT1
000135173 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135173 700__ $$0(orcid)0000-0002-1490-6423$$aDufo-López, Rodolfo$$uUniversidad de Zaragoza
000135173 700__ $$0(orcid)0000-0001-7764-235X$$aArtal-Sevil, Jesús Sergio$$uUniversidad de Zaragoza
000135173 700__ $$0(orcid)0000-0003-2457-0422$$aGarcía-Paricio, Eduardo$$uUniversidad de Zaragoza
000135173 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000135173 773__ $$g227 (2024), 120540 [17 pp.]$$pRenew. energy$$tRenewable Energy$$x0960-1481
000135173 8564_ $$s7883022$$uhttps://zaguan.unizar.es/record/135173/files/texto_completo.pdf$$yVersión publicada
000135173 8564_ $$s2663029$$uhttps://zaguan.unizar.es/record/135173/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135173 909CO $$ooai:zaguan.unizar.es:135173$$particulos$$pdriver
000135173 951__ $$a2025-09-22-14:37:40
000135173 980__ $$aARTICLE