| dc.description.abstract | In this thesis, we analyse how geographical diversification and a portfolio approach lowers the variability in wind power production. Understanding variance of wind power production will increase system reliability. Evaluating the covariance of power production in different parts of Norway will become relevant as the share of variable renewable energy increases in the power energy mix. We use historical wind measures from the Norwegian coastline to evaluate how to minimise the variance of theoretical wind power production. The findings suggest that when utilising weekly aggregated wind data, the wind power correlation is low when the distance between two wind sites is approximately 900 km or more. We see that the correlation between wind power locations decreases as the distances increases regardless of the time interval studied. Portfolio theory states that assets’ variance in a portfolio is not a problem if the assets do not covary. We argue that it is possible to handle wind power variability in the same way as stocks on the financial markets and that coordinating wind farms lowers the variability of wind power production. We present an optimal investment portfolio for onshore wind power production in Norway, utilising a Mean-Variance Portfolio (MVP). In the thesis, we have applied two different MVP approaches, first accounting for wind resources and second accounting for system demand. We find that how to best diversify wind locations differ depending on the optimisation problem. The empirical results reveal that geographical dispersion contributes to reducing variance in wind power production, associated with increased system reliability. | en_US |
