Influence of ANN-based market price forecasting uncertainty on optimal bidding

Abstract

In today’s deregulated markets, forecasting energy prices is becoming more and more important. In the short term, expected price profiles help market participants to determine their bidding strategies. Consequently, accuracy in forecasting hourly prices is crucial for generation companies (GENCOs) to reduce the risk of over/underestimating the revenue obtained by selling energy. In this paper, the influence of the accuracy of ANN-based hourly energy price forecasting on the bidding strategy of GENCOs is assessed. First, a customized, recurrent Multilayer Perceptron is developed and applied to the 24-hour energy price forecasting problem, and the expected errors are quantified. Then, price profiles are used to compute the optimal bidding of realistic GENCOs, and the influence of forecasting errors on both the bidding strategies and the expected revenues is studied.