  Bilevel programming can be used to formulate many problems in the field of
power systems, such as strategic bidding. However, common reformulations of
bilevel problems to mixed-integer linear programs make solving such problems
hard, which impedes their implementation in real-life. In this paper, we
significantly improve solution speed and tractability by introducing decision
trees to learn the active constraints of the lower-level problem, while
avoiding to introduce binaries and big-M constants. The application of machine
learning reduces the online solving time, by moving the selection of active
constraints to an offline process, and becomes particularly beneficial when the
same problem has to be solved multiple times. We apply our approach to the
strategic bidding of generators in electricity markets, where generators solve
the same problem many times for varying load demand or renewable production.
Three methods are developed and applied to the problem of a strategic
generator, with a DCOPF in the lower-level. These methods are heuristic and as
so, do not provide guarantees of optimality or solution quality. Yet, we show
that for networks of varying sizes, the computational burden is significantly
reduced, while we also manage to find solutions for strategic bidding problems
that were previously intractable.
