Mostrar el registro sencillo del ítem
dc.contributor.author | Aguila-Leon, Jesus | es_ES |
dc.contributor.author | Vargas-Salgado Carlos | es_ES |
dc.contributor.author | Chiñas-Palacios, Cristian | es_ES |
dc.contributor.author | Díaz-Bello, Dácil | es_ES |
dc.date.accessioned | 2023-10-02T18:01:36Z | |
dc.date.available | 2023-10-02T18:01:36Z | |
dc.date.issued | 2022-09-01 | es_ES |
dc.identifier.issn | 0196-8904 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/197439 | |
dc.description.abstract | [EN] Energy management systems are usually used to integrate different energy sources into a coordinated microgrid system. However, given the variability of renewable sources and the complexity of calculating renewable resource availability and managing energy, it is not easy to incorporate efficient energy management models in a microgrid. This work focuses on developing a methodology to incorporate optimized artificial networks into a self-adaptable energy management system to improve microgrids performance. The proposed model consists of a set of artificial neural networks organized into a cascade configuration. A Particle Swarm Optimization algorithm optimizes each artificial neural network; the proposed model aims to estimate and provide information to the energy management system. The model is implemented in MATLAB/Simulink environment and fed with experimental data. Correlation analysis of system variables between the different artificial neural networks is performed to validate the proposed model. Simulated tests are performed with scenarios using experimental data, and an analysis of the system's response is performed in terms of the root mean squared error and linear regression. The results showed that, compared to related works, the proposed model reduced errors by 59% and 56% for single and multiple-step prediction of energy parameter estimators. Regarding the fitness of the power estimator from the EMM for the test scenarios, an 0.1245 RMSE was obtained. | es_ES |
dc.description.sponsorship | This study has been in part supported by the projects: "Design Of a Hybrid Renewable Microgrid System" and "Microred Inteligente Hibrida de Energias Renovables para Solucionar el Trilema Agua-Alimentacion-Energia en Una Comunidad Rural de Honduras" ID 2020/ACDE/000306. The authors also express their sincere appreciation to Universitat Polit`enica de Val`encia for performing the proposed algorithm's tests and measurements at the Renewable Energies Laboratory (LabDER) at the Institute of Energy Engineering. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Energy Conversion and Management | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Artificial Neural Network | es_ES |
dc.subject | Particle Swarm Optimization | es_ES |
dc.subject | AC Microgrid | es_ES |
dc.subject | Energy Management Model | es_ES |
dc.subject | Syngas Genset | es_ES |
dc.subject.classification | INGENIERIA ELECTRICA | es_ES |
dc.title | Energy Management Model for a Standalone Hybrid Microgrid Through a Particle Swarm Optimization and Artificial Neural Networks Approach | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.enconman.2022.115920 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//CIGE%2F2021%2F172//Modelado, experimentación y desarrollo de sistemas de gestión óptima para microrredes híbridas renovables/ | es_ES |
dc.rights.accessRights | Embargado | es_ES |
dc.date.embargoEndDate | 2024-09-01 | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.description.bibliographicCitation | Aguila-Leon, J.; Vargas-Salgado Carlos; Chiñas-Palacios, C.; Díaz-Bello, D. (2022). Energy Management Model for a Standalone Hybrid Microgrid Through a Particle Swarm Optimization and Artificial Neural Networks Approach. Energy Conversion and Management. 267:1-17. https://doi.org/10.1016/j.enconman.2022.115920 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.enconman.2022.115920 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 17 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 267 | es_ES |
dc.relation.pasarela | S\467975 | es_ES |
dc.contributor.funder | GENERALITAT VALENCIANA | es_ES |
dc.subject.ods | 07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos | es_ES |