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The economic profitability of future wave energy production along the Galician coast is assessed by analyzing the Levelized Cost of Energy (LCoE) under different Capital Expenditure (CapEx) scenarios and two discounts rates (5% and 10%). Wave resources for the near future under the RCP8.5 scenario are downscaled using SWAN, providing up to 75 m spatial resolution in coastal areas. The study's goal is to enhance the cost-effectiveness by selecting the most suitable wave energy converter (WEC) for each location. Fourteen WECs operating at different depths are considered. This analysis reveals that the Atargis device boasts the lowest LCoE for 64.2% of the coastal area, mainly in deep waters, with an LCoE of 77 €/MWh. In addition, the Oyster and Wave Dragon devices exhibit the lowest LCoE for 12.4% and 15.0% of the coastal area, respectively, excelling in shallow waters and near the coast, with values of 50 €/MWh and 97 €/MWh. These findings demonstrate the profitability of wave energy production along the Galician coast, even when considering a more conservative CapEx of 3 M€/MW, resulting in a cost of 140 €/MWh. This conclusion takes into account the evolving electricity prices in Spain, which reached 0.2068 €/kWh in the second half of 2023. • LCoE analysis of the economic profitability of future wave energy production under different CapEx. • Future wave energy resource downscaled using SWAN under RCP8.5 along the Galician coast. • Fourteen different types of wave energy converters were considered operating at different depths. • Atargis, Oyster and Wave Dragon devices displayed along the Galician coast generate the lowest LCoE (€70/MWh). • Future wave energy production prices along the Galician coast are competitive compared to current Spanish electricity prices. [ABSTRACT FROM AUTHOR]

This paper assesses the economic feasibility of offshore wind farms installed in deep waters considering their internal rate of return (IRR), net present value (NPV), and levelized cost of energy (LCOE). The method proposed has three phases: geographic phase, economic phase, and restrictions phase. The purpose of the geographic step is to obtain the input values, which will be used in the economic phase. Then, the economic parameters are calculated considering the inputs provided previously. Finally, the bathymetric restriction is added to the economic maps. The case study focused on the Cantabric and North-Atlantic coasts of Spain, areas that have not been studied previously in economic terms regarding floating offshore wind technology. Moreover, several alternatives have been considered, taking into account the type of floating offshore wind structure and the electric tariff. Results indicate which is the best floating offshore wind structure with respect to LCOE, IRR, and NPV, and where is the best location for the connection of a floating offshore wind farm in the region selected. [ABSTRACT FROM AUTHOR]

The aim of this work is to develop a software to calculate the economic parameters so as to determine the feasibility of a floating offshore renewable farm in a selected location. The software can calculate the economic parameters of several types of offshore renewable energies, as follows: one renewable energy (floating offshore wind-WindFloat, tension leg platform (TLP), and spar; floating wave energy-Pelamis and AquaBuoy), hybrid offshore wind and wave systems (Wave Dragon and W2Power), and combined offshore wind and waves with different systems (independent arrays, peripherally distributed arrays, uniformly distributed arrays, and non-uniformly distributed arrays). The user can select several inputs, such as the location, configuration of the farm, type of floating offshore platform, type of power of the farm, life-cycle of the farm, electric tariff, capital cost, corporate tax, steel cost, percentage of financing, or interest and capacity of the shipyard. The case study is focused on the Galicia region (NW of Spain). The results indicate the economic feasibility of a farm of floating offshore renewable energy in a particular location in terms of its costs, levelized cost of energy (LCOE), internal rate of return (IRR), net present value (NPV), and discounted pay-back period. The tool allows for establishing conclusions about the dependence of the offshore wind resource parameters, the main distances (farm-shore, farm-shipyard, and farm-port), the parameters of the waves, and the bathymetry of the area selected., Competing Interests: The authors declare no conflicts of interest