A comparison of sizing methods for a long-term renewable hybrid system. Case study: Galapagos Islands 2031
This research compares different sizing methods to improve the current autonomous hybrid system in the Galapagos Islands in the year 2031, analyzing the loss of power supply probability (LPSP). In the first place, the energy consumed in the islands for the year 2031 has been obtained, using ANN arti...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/6162 |
| Acceso en línea: | https://pubs.rsc.org/en/content/articlelanding/2021/se/d1se00078k https://hdl.handle.net/10953/6162 |
| Access Level: | acceso abierto |
| Palabra clave: | LPSP Pattern Search Simulink Design Optimization Neuronal Network Pumped Storage 621.35 |
| Sumario: | This research compares different sizing methods to improve the current autonomous hybrid system in the Galapagos Islands in the year 2031, analyzing the loss of power supply probability (LPSP). In the first place, the energy consumed in the islands for the year 2031 has been obtained, using ANN artificial neural networks with Matlab, from fundamental parameters in the design of a multilayer perceptron neural network model. Second, the methods used for sizing the system are HOMER Pro and Simulink Design Optimization (SDO). The dynamic models of the different components of the hybrid system have been made in MATLAB/Simulink. The proposed hybrid system is composed of PV photovoltaic and WT wind, and lead-acid batteries, hydraulic pumping, and diesel generator as storage and support systems. Then, in order to design a sustainable system, a hybrid system has been dimensioned with renewable energy sources of an appropriate size. The LPSP values obtained are below 0.09% and 0.22%, which shows that the system has been optimally dimensioned. In addition, a cost analysis has been carried out, the values obtained from NPC and COE according to HOMER Pro are $ 183,810,067 and 0.26 $/kWh, and $ 233,385,656 and 0.25 $/kWh and using SDO are $ 148,523,110 and 0.25 $/kWh, $ 189,576,556 and 0.24 $/kWh for strategies I and II respectively of the proposed hybrid system. The data obtained shows that SDO's Latin Hypercube algorithm achieves a better optimization compared to HOMER Pro.Abstract text goes here. The abstract should be a single paragraph that summarises the content of the article. |
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