Strategic bidding by predicting locational marginal price with aggregated supply curve

Price-makers in the generation sector can utilize offer data published by independent system operators (ISOs) for strategic bidding. Many research uses system-wide offer information for strategic bidding because individual offers are usually published with masked identifications. However, these methods are not applicable in markets with transmission congestion because the nodal information is not used. A research gap remains in using system-wide offer information and accessible nodal information for strategic bidding in markets with congestion. System-wide aggregated supply curves can be formed just with anonymous offers, and locational marginal price (LMP) is accessible nodal data containing the congestion information. This paper proposes a framework for price-makers to bid strategically by predicting LMPs with aggregated supply curves. A feature extraction method is proposed to make aggregated supply curves applicable for LMP prediction, and the maximal information coefficient is developed for feature selection. A convolutional neural network is combined with a long short-term memory network to model the impact of aggregated supply curves on LMPs. In this framework, price-makers can investigate the impact of their strategies on aggregated supply curves, predict LMPs with aggregated supply curves, and make the optimal bidding strategies. Numerical results based on the PJM-5 bus system and real data from the Midcontinent Independent System Operator validate the effectiveness of the proposed framework.