Optimization of Electric Energy Sales Strategy Based on Probabilistic Forecasts

Abstract

In this paper we propose an optimization scheme for a selling strategy of an electricity producer who in advance decides on the share of electricity sold on the day-ahead market. The remaining part is sold on the complementary (intraday/balancing) market. To this end, we use probabilistic forecasts of the future selling price distribution. Next, we find an optimal share of electricity sold on the day-ahead market using one of the three objectives: maximization of the overall profit, minimization of the sellers risk, or maximization of the median of portfolio values. Using data from the Polish day-ahead and balancing markets, we show that the assumed objective is achieved, as compared to the naive strategy of selling the whole produced electricity only on the day-ahead market. However, an increase of the profit is associated with a significant increase of the risk.

Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case

“…Probabilistic forecasting involves computing quantiles, intervals or the whole predictive density rather than simple point predictions based on the conditional mean. As pointed out by [27], the probabilistic approach serves several purposes such as stochastic unit commitment, power supply planning, the prediction of equipment failure, and the integration of renewable energy sources, (see, e.g., [28]). The literature on probabilistic electricity forecasting is quite limited [2], particularly compared to that of probabilistic forecasting in general [29] or probabilistic renewable energy forecasting [30][31][32][33].…”