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Bilevel programming can be used to formulate many problems in the field ofpower systems, such as strategic bidding. However, common reformulations ofbilevel problems to mixed-integer linear programs make solving such problemshard, which impedes their implementation in real-life. In this paper, wesignificantly improve solution speed and tractability by introducing decisiontrees to learn the active constraints of the lower-level problem, whileavoiding to introduce binaries and big-M constants. The application of machinelearning reduces the online solving time, by moving the selection of activeconstraints to an offline process, and becomes particularly beneficial when thesame problem has to be solved multiple times. We apply our approach to thestrategic bidding of generators in electricity markets, where generators solvethe same problem many times for varying load demand or renewable production.Three methods are developed and applied to the problem of a strategicgenerator, with a DCOPF in the lower-level. These methods are heuristic and asso, do not provide guarantees of optimality or solution quality. Yet, we showthat for networks of varying sizes, the computational burden is significantlyreduced, while we also manage to find solutions for strategic bidding problemsthat were previously intractable.

Learning Active Constraints to Efficiently Solve Linear Bilevel Problems: Application to the Generator Strategic Bidding Problem