A study of the day-ahead-energy market auctions from a multi-objective perspective
Student thesis: Doctoral Thesis
In this study, we develop approaches for the market clearing problem in European day-ahead electricity markets. We first present the surplus maximization problem and extend it with pricing constraints that limit market loss or missed surplus associated with paradoxically accepted and rejected bids. We develop a Benders decomposition algorithm with price-based Benders infeasibility cuts to solve the problem. Our algorithm outperforms the state-of-the-art Benders decomposition algorithms and the primal-dual approach on practical-sized market instances. Then, we develop a multi-objective formulation of the problem with market surplus, market loss and market missed surplus objectives where the first one is to be maximized, and the last two are to be minimized. We develop a cone-based search algorithm to solve three-objective mixed-integer linear programming problems where at least one objective takes discrete values, and apply the algorithm on the three-objective day-ahead electricity market clearing problem. We examine the characteristics of the nondominated set of the problem and derive insights for market operators related to the design of the market rules.