Mitigation of carbon footprint with 100% renewable energy system by 2050: The case of Galapagos islands
In this paper, a technical-economic study of the 100% renewable energy sources in the Galapagos islands is done. Historical consumption data for 2011-2020 have been considered to forecast the load curve. To achieve this goal, the load is forecasting by Nonlinear autoregressive exogenous neuronal net...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2022 |
| 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:10953/6155 |
| Acceso en línea: | https://doi.org/10.1016/j.energy.2022.123247 https://www.sciencedirect.com/science/article/pii/S0360544222001505?via%3Dihub https://hdl.handle.net/10953/6155 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial neural network EnergyPLAN Galapagos islands 100% Renewable energy-system 621.35 |
| Sumario: | In this paper, a technical-economic study of the 100% renewable energy sources in the Galapagos islands is done. Historical consumption data for 2011-2020 have been considered to forecast the load curve. To achieve this goal, the load is forecasting by Nonlinear autoregressive exogenous neuronal network model for 2030 and 2050. The study focuses on supplying the energy demand of the islands with renewable sources, analyzing possible scenarios based on the current electricity system. The methodology studies the capacity of renewable sources to balance supply and demand through dispatch simulations using the EnergyPLAN software. The results show energy flows, costs and long-term energy balances (2050), with 100% renewable energy from several wind and photovoltaic combinations. Moreover, The precision of the demand forecast was 98.12% with a mean square error of 0.013%. The total annual cost decreases while the capacities of the renewable sources increase to a certain point of equilibrium. As salient features of the developed approach, various sensitivity analyzes are presented that allow understanding the uncertainties, scope and limitations of the proposed models. |
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