Building supply functions under uncertainty for a day-ahead electricity market

Abstract

In this paper we present a new methodology to find the optimal bids for a day-ahead electricity market. A portfolio analysis is developed in order to obtain the set of 24 supply functions that the generating utility should submit to maximise its expected profit. As all participants submit their bids simultaneously, the generating utility does not know its competitors and demand bids. However, we assume that historical data analysis and market experience can provide an arbitrary number of residual demand scenarios. For each scenario, profit is maximised deterministically taking into account the thermal units constraints. The solution provides an optimal pair quantity-price for each hour. Repeating the process for every scenario, each hour will contain a set of profit-maximising points. We propose to build the hourly supply functions by finding the piece-wise linear approximation of the scatterplot formed by these optimal points. A numerical example is presented where the proposed methodology has been applied to a real size hypothetical case.