Wind power bidding strategy by harmonizing neural network and genetic algorithm

Abstract

As the importance of wind power as a renewable energy resource increases, the need for strategic management has increased for wind power producers (WPPs). However, many countries are still beginning in the renewable market, such as South Korea. Therefore, in this study, we discuss a bid determination model for a WPP that can be easily applied with limited prices and forecast information. Most previous studies attempted to minimize forecast error and then decide bids based on the predicted wind power generation. However, our model aims to maximize the total profit of a WPP directly. In our profit-focused neural network (NN) model, the general NN predicts generation forecasts at first. Next, based on the forecasts, WPP's bid for the next 24 hours is determined by a genetic algorithm to maximize WPP's profit. The bid based on the profit-focused NN model achieves 0.3-4.6% more gains than that of a general NN's bid under a variable ratio of a day-head and balancing market prices and penalty cost. These practical ranges signify that our model remains profitable and robust in various conditions.