Arévalo, PaulEras-Almeida, AndreaCano-Ortega, AntonioJurado-Melguizo, FranciscoEgido-Aguilera, Miguel Ángel2025-10-032025-10-032021-10-300378-7796https://doi.org/10.1016/j.epsr.2021.107660https://www.sciencedirect.com/science/article/pii/S0378779621006416?via%3Dihubhttps://hdl.handle.net/10953/6164The present study focuses on the planning of electrical energy for the Galapagos islands using different renewable energy technologies for the year 2031 in order to reduce diesel consumption and achieve 100% renewable energy share. To do that, a long-term load demand forecast study on the Santa Cruz and Baltra islands (Galapagos), applying Artificial Neural Networks, is done. Then, to optimize the islands’ power system, mostly based on diesel generation, different renewable energy technologies together with energy storage systems, such as batteries and hydraulic pumping, have been analyzed through the application of the HOMER Pro software. For this purpose, two energy control models have been developed to gradually reduce the operating hours of the diesel power plant. The results show that these islands can achieve 100% renewable penetration with the following configuration: photovoltaic/wind turbines/batteries/pumping hydroelectric storage system/diesel generators (only as back-up). This implies increasing the photovoltaic capacity by 26 MWp and installing a pumped hydraulic storage of 374 MWh. In this way, the electricity generation cost would be reduced from $ 0.32/kWh to $ 0.23/kWh, and the CO2 emissions would decrease by 16,000 tons of CO2 into the environment.engCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/Galapagos IslandsRenewable energyPumped-storageDesalinizationHybrid power systemNeural networkinfo:eu-repo/semantics/article621.35info:eu-repo/semantics/openAccess