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Investor Presentaiton

Energies 2019, 12, 3658 6 of 37 A considerable amount of the Pg is consumed to pump the large seawater flow rates through the OTEC plant. The net power Pnet should be calculated, which is usually about 30% of the Pg [36,37]. The Pnet can be expressed by the following equation considering AT design = 20 °C and the other losses presented in [34]: Pnet QcwpCpEtg 3Y AT2-0.18AT2 de 8T (2(1+y) - design 0.12 (21) 275 AT² 12 design}. (4) 2.2.2. Wave Energy The wave dataset was obtained using the operational global ocean analysis and forecast system of Météo-France that is available for the CMEMS (Copernicus Marine Environment Monitoring Service) center. The model had a horizontal resolution of 1/12° (~9 km) and 3-hourly instantaneous fields of integrated wave parameters. The global wave system of Météo-France is based on the third-generation wave model MFWAM. It uses the computing code ECWAM-IFS-38R2 with a dissipation term [38]. The 2-min gridded global topography data ETOPO2/NOAA were used to generate the model's mean bathymetry. The dataset uses three years of data to estimate the wave climatology along the Brazilian coastline (between 2015 and 2018). The power density P was calculated using the significant wave height H, and the wave energy period Te as follows: Pg2 P = -Hs²Te 64π (5) where p and g represent the seawater density (1025 kg.m³) and gravity acceleration (9.806 m.s²), respectively; Hs is the significant height (m); and Te is the energy wave period (s). This simplified expression uses deep-water approximation [39], which fits well most of the modeled domains; however, more sophisticated techniques as well as in situ measurements are required to precisely determine the shallow water wave climate. 2.3. Metrics The variability of the available ocean renewable energy in time is an important issue due to its impact on the capacity factor, which, consequently, affects the economy of the ocean energy system. Two different metrics were used to address the seasonal and temporal variability of the Brazilian coastline. The seasonal variability (SV) index [40] can be expressed as follows: Ps,max - Ps,min SV = P ' year (6) where Ps,min and PS,max are the mean wave power of the least and the most energetic seasons, respectively, and the Pyear is the annual mean power. Greater values of SV imply a larger seasonal variability; however, it should be noted that this is the variability of the energy resources relative to their mean level on a three-month seasonal time scale [40]. The temporal variability of the energy at a site or region can be evaluated by the coefficient of variation (COV) [40], which is expressed as 10.5 (P-P) COV (P) SD(P(t)) mean (P(t)) P where SD is the standard deviation, and the over-bar denotes the time-averaging. A COV equal to zero leads to a fictitious power time series with absolutely no variability, while COV (P) = 1 and 2 imply that the standard deviation of the time series is equal to and twice the mean value, respectively.
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