| Summary: | This thesis investigates how to optimize stable wind production along the coast of Norway. The research is carried out by studying how well a compound dependency model, consisting of a time series and copula model, for simulation of wind power data performs compared to historical data when optimizing a portfolio for wind power production areas. The weights for the areas in the portfolio are computed so that the areas with the most stable joint power production are included. The findings of this research will contribute to the understanding of how effective different optimization approaches for offshore wind park placements are and provide insights into the selection of optimal areas for offshore wind power development in Norway. The study's findings indicate that portfolio optimization performed on simulated data performs better than on historical data. Consequently, zero and low production values are reduced, and stability is increased for the portfolio made with simulated data. Moreover, Value at Risk (VaR) is argued to be a better performance measure for stable wind production than variance. The portfolio distribution when maximizing VaR is more left-skewed than the portfolio minimizing variance. Thus, maximizing VaR results in a higher variance, but less zero and low production values, and a higher average production which is argued to be more important. The positive effect of dispersed wind parks regarding stable wind production is evident. Following the pattern of diminishing correlation as distance increases, the optimal combination of wind parks includes places throughout the Norwegian coast. All areas are included in the optimal solution, but the most influential areas which should be prioritized are Sørlige Nordsjø 2, South of Kristiansand, West of Tromsø, and North of Tanafjorden. When the criteria for stable wind production is extended to include a penalty factor for low average production, diversification is partly de-prioritized to include areas with high average production, among these, more ...
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