TY - JOUR
T1 - Mitigation of carbon footprint with 100% renewable energy system by 2050
T2 - The case of Galapagos islands
AU - Arévalo, Paúl
AU - Cano, Antonio
AU - Jurado, Francisco
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/4/15
Y1 - 2022/4/15
N2 - 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.
AB - 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.
KW - 100% Renewable energy-system
KW - Artificial neural network
KW - EnergyPLAN
KW - Galapagos islands
UR - https://editorial.ucuenca.edu.ec/omp/index.php/ucp/catalog/book/96
U2 - 10.1016/j.energy.2022.123247
DO - 10.1016/j.energy.2022.123247
M3 - Artículo
AN - SCOPUS:85123760340
SN - 0360-5442
VL - 245
JO - Energy
JF - Energy
M1 - 123247
ER -