Day-Ahead Financial Loss/Gain Modeling and Prediction for a Generation Company

In an electricity market, the main goal of a generation company (GenCo) is to maximize its profit, while encountering the uncertainty of the electricity price forecast. Different risk measures have been proposed to cope with this source of uncertainty. However, those are usually before-the-fact performance indices and cannot give a measure for the financial loss/gain (FLG) of a GenCo considering the electricity prices actually realized in the market. This paper focuses on this matter. The time series of FLG is first constructed given the real conditions of the electricity market. Then, the FLG time series is quantized using Silhouette criterion and k-means clustering approach. Subsequently, based on the historical values of the quantized FLG time series and relevant exogenous variables, its day-ahead values are predicted. The method proposed for day-ahead FLG prediction consist of conditional mutual information and sequential forward search as the feature selection technique and extreme learning machine as the forecasting engine. The effectiveness of the whole proposed approach, including the FLG time series construction, quantization approach, and the prediction method, is shown for a typicalGenCo using the real data of the PJM and Ontario electricity markets.