An Assessment of Wind Power Forecasting Models and its Financial Implications for the Traders Master’s thesis in Industrial Engineering and Management

De Vrije Energie Producent (DVEP) is an energy supplier and Balance Responsible Party (BRP) in the Dutch energy market. They are responsible for buying and selling energy on behalf of their customers. To do so, they have to nominate the expected electricity production and consumption of their portfolio for every hour of the following day. Hereby, it is crucial to predict these two volumes as precisely as possible. Forecasting the demand side is rather straightforward. The production side, however, is much more complicated for a wind based portfolio because of uncertainty. A bad forecast can become costly due to imbalance costs and it is thus desirable to have the wind power forecast as precise as possible. Being as precise as possible, however, is not always the most beneficial strategy, as profitable imbalance prices may be harvested otherwise. This is the topic of the second part of the project. Combined, this translates into the following research question: Which model, based on historical market and weather data, can provide the most accurate and profitable day-ahead electricity bidding when considering a wind-based electricity portfolio? From literature we find that normalized bias (NBIAS), normalized mean absolute error (NMAE) and normalized root mean square error (NRMSE) are appropriate measures to assess the forecast performance. The research at hand is done for a share of DVEP’s wind portfolio, which includes 15 wind parks and a capacity of 55.1 MW and considers the period from 01-07-2018 till 01-07-2019. We firstly compare the two current forecasters and find that they have very similar results, with an NMAE of 7.47% and 7.13% for Forecast 1 and 2, respectively. When this wind power portfolio is considered as a whole, we can not state that both forecasts are significantly different. Besides, when we take the wind speed or day hour into account and rearrange the data based on this, it can be concluded that Forecast 2 outperforms Forecast 1. Wind direction and temperature are also tested, but deliver less explicit results. This is also substantiated with findings in case of big forecast difference between Forecast 1 and 2. After this, we develop other strategies based on Forecast 2 to determine the best bidding volume for the day-ahead bidding. In order to find the ideal bidding volume that deviates from a strategy of zero imbalance, earlier research stressed that forecasting of prices is crucial. However, due to the characteristic of the Dutch energy market being a dual pricing market, this is a very difficult task. To find the ideal bidding strategy for the day-ahead market, we use two approaches: The point forecasts and the probabilistic forecast. The point forecasts includes next to the above mentioned forecasts also an average of both. For the probabilistic strategy we use empirical distributions of both historical prices and production data related to the forecast volume of Forecast 2. These distributions create scenarios for which the bidding volume is optimized: This is done without restriction, but also with restrictions due to VaR and ES. From the probabilistic strategies the strategy of VaR 0 with a dependent price/production resampling was found to be the best, however this approach was still not better than the day-ahead bidding of the point forecasts, from which Forecast 2 was the best. We conclude that it is wise to use Forecast 2 as input for the day-ahead bidding instead of the currently used Forecast 1.