147698 20260217205608.0 doi 10.1016/j.energy.2024.134164 sideral 141114 ART-2024-141114 eng Lujano-Rojas, Juan M. Universidad de Zaragoza (orcid)0000-0002-5801-0602 Efficient design of a hybrid power system incorporating resource variability 2024 Access copy available to the general public Unrestricted The optimal design of small-scale energy systems is a critical step in rural electrification projects, offering valuable insights for integrating renewable energy sources on a broader scale. This paper analyzes the sizing of isolated energy systems using the Genghis Khan Shark Optimizer, taking into account the variability of wind and solar resources across diverse scenario groups to enhance computational efficiency. Specifically, three scenario sets were utilized: training, validation, and testing. The training set was applied to assess the objective function across all optimization agents, while the validation set was used to independently evaluate the performance of the agent with the highest fitness score. Finally, the testing set was employed to verify the performance of the accepted solution. By selecting a limited number of scenarios for the training set and a moderate number for the validation and testing sets, we reduced the computational load associated with analyzing the entire population, allowing for greater focus on the most promising agent identified at each iteration. Results from a case study revealed that the proposed method identified an energy system configuration 8.05 % better than the configuration obtained using a genetic algorithm. info:eu-repo/grantAgreement/ES/AEI/PID2021-123172OB-I00 info:eu-repo/grantAgreement/EUR/AEI/TED2021-129801B-I00 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 9.4 2024 THERMODYNAMICS 3 / 79 = 0.038 2024 Q1 T1 ENERGY & FUELS 31 / 182 = 0.17 2024 Q1 T1 2.211 2024 Building and Construction 2024 Q1 Civil and Structural Engineering 2024 Q1 Electrical and Electronic Engineering 2024 Q1 Management, Monitoring, Policy and Law 2024 Q1 Energy Engineering and Power Technology 2024 Q1 Fuel Technology 2024 Q1 Industrial and Manufacturing Engineering 2024 Q1 Energy (miscellaneous) 2024 Q1 16.5 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Dufo-López, Rodolfo Universidad de Zaragoza (orcid)0000-0002-1490-6423 Artal-Sevil, Jesús Sergio Universidad de Zaragoza (orcid)0000-0001-7764-235X García-Paricio, Eduardo 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 313 (2024), 134164 [18 pp.] Energy Energy 0360-5442 8395807 http://zaguan.unizar.es/record/147698/files/texto_completo.pdf Versión publicada 2623432 http://zaguan.unizar.es/record/147698/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:147698 articulos driver 2026-02-17-20:47:43 ARTICLE