| dc.description.abstract | Meeting future energy and environmental demands poses new challenges, with one of them being the uncertainty and variability in power production from renewable energies such as solar and wind power due to their dependency on weather, causing them to be referred to as intermittent energy sources. At the island of Smøla in Norway there is currently a wind park with an installed wind capacity of 150.4 MW. This report will look into the complementary characteristics of co-locating solar and wind power at Smøla by finding the optimal mix of solar and wind power for a larger capacity wind turbine that may be installed after the end of life of the current wind turbines. The optimal mix is adjusted in order to maximize the usage of the transmission line capacity that connects Smøla to the mainland, while keeping the curtailed power below a given percentage. The complementary characteristics will also be analysed using correlation analysis with Pearson’s correlation factor calculated at hourly, daily and monthly resolution. To accomplish this, irradiance and wind data was gathered from openly available sources, and then calculating solar power using models from literature and wind power with power curves for the specified wind turbine models. The power production calculations and correlation analysis were performed using python, and the power production scenarios was optimized using the excel optimization toolbox. The power production scenarios presented for the years 2017-2020 shows that by only installing the larger wind turbines results in 2824 GWh of power being delivered to Nordheim with an average transmission line usage of 56 % and 140 GWh of power being curtailed. With both solar and wind power the power delivered to Nordheim was 3105 GWh with wind power accounting for 85 % of the power produced and solar with 15 %. The average transmission line usage was 61.5 % and 163 GWh of power was curtailed. The correlation analysis shows that the complementary characteristics for solar and wind power calculated across the year is strongest for monthly timelines with a correlation factor of -0.58, which decreases as the resolution gets finer, with daily resolution being -0.34 and hourly resolution with -0.15. | |
