An analysis on the attractiveness of different grid configurations for offshore wind power investors

Attracting offshore wind power investors is important for the Norwegian government to reach its development goal for offshore wind power. This thesis presents an investment decision model to evaluate the investment incentive in offshore wind farms and hybrid projects under various grid configurations. Using a real-options approach, based on a binomial lattice method that embeds deferral options, the model derives the investment incentive and the optimal investment timing for investors in an uncertain environment. The results show that the grid configuration has a significant impact on the investors investment incentive. A three-market hybrid configuration is generally preferred over a radial connection to Norway and a twomarket hybrid by offshore wind power investors. I find that the offshore wind farm is more profitable in a hybrid configuration when it is connected to markets with higher prices than NO2, low price volatility, and a positive correlation with NO2. Under a historic electricity price level, the results suggests that subsidies are necessary to attract investors. However, using a simulated future electricity price level, offshore wind farm can become viable without subsidies. Additionally, the results show that a hybrid configuration does not necessarily reduce the offshore wind power project’s risk. Moreover, investors and regulators preference in grid configuration are partially aligned. A two-market hybrid is undesirable for wind farm investors but preferred by regulators, but both find a three-market hybrid configuration desirable. The conclusion of this thesis can provide useful guidelines for policy makers determining grid configuration, as this decision affect investment incentive for offshore wind power investors and the socio-economic benefit from offshore wind development

Short-term load forecasting in times of unprecedented price movements

In this thesis we aimed to find the best methods for short-term load forecasting in the Norwegian electricity market during times of unprecedented price movements. We answered three questions related to this aim. The first was which model achieved the most accurate forecast. The second was whether our proposed models outperform the official forecasts published on the Entso-E platform. The third question asked was if the price movements had any effect on the accuracy of the load forecast. We constructed two SARIMAX models, a Gradient boosted decision tree, a Random Forest, and a Multilayer perceptron model. Our findings show the two SARIMAX models to be most accurate. These models outperformed the forecasts published on the Entso-E platform in four out of the five Norwegian bidding zones, measured in MAPE and RMSE. Finally, we have shown that forecasting load with and without price information did not result in significant differences in accuracy. Our findings did not indicate an increase in difficulty of forecasting 2021 compared to 2019, neither for the three southern bidding zones with higher price increase nor the northern two zones.I denne masteroppgaven har vi forsøkt å finne den beste metoden for kortsiktig prognostisering av elektrisitets-etterspørsel i perioder med ekstreme prisbevegelser. Vi har besvart tre spørsmål knyttet til denne problemstillingen. Det første var hvilken modell som oppnår høyest nøyaktighet. Det andre var om våre modeller presterer bedre enn de publiserte prognosene på Entso-Es offentlig tilgjengelige data-plattform. Det tredje spørsmålet var om de ekstreme prisbevegelsene hadde noen effekt på nøyaktigheten av prognosene. Vi har laget to SARIMAX modeller, en Gradient boosting decision tree-, en Random Forest og en Multilayer perceptron-modell. Gjennom arbeidet har vi vist at de to SARIMAX-modellene presterer best. Disse modellene er mer nøyaktig enn prognosene publisert på Entso-Es plattform for fire av de fem norske strømregionene, målt i MAPE og RMSE. Til slutt har vi vist at prognoser gjort både med og uten prisinformasjon ikke gir signifikante forskjeller i nøyaktighet. Det ble heller ikke påvist en klar forskjell i vanskelighetsgraden av å prognostisere 2021 sammenlignet med 2019, verken for de sørlige prissonene med høy prisvekst eller de nordlige sonene med en lavere prisvekst.M-Ø

Short-term load forecasting in times of unprecedented price movements

In this thesis we aimed to find the best methods for short-term load forecasting in the Norwegian electricity market during times of unprecedented price movements. We answered three questions related to this aim. The first was which model achieved the most accurate forecast. The second was whether our proposed models outperform the official forecasts published on the Entso-E platform. The third question asked was if the price movements had any effect on the accuracy of the load forecast. We constructed two SARIMAX models, a Gradient boosted decision tree, a Random Forest, and a Multilayer perceptron model. Our findings show the two SARIMAX models to be most accurate. These models outperformed the forecasts published on the Entso-E platform in four out of the five Norwegian bidding zones, measured in MAPE and RMSE. Finally, we have shown that forecasting load with and without price information did not result in significant differences in accuracy. Our findings did not indicate an increase in difficulty of forecasting 2021 compared to 2019, neither for the three southern bidding zones with higher price increase nor the northern two zones