This paper proposes a new strategy for an independent system operator (ISO) to trade demand response (DR) with different DR aggregators while considering various operational constraints. The ISO determines the energy scheduling and reserve deployment in a pre-emptive market while setting DR contracts with the DR aggregators. The ISO applies a two-stage stochastic programming to cope with the uncertainty associated with wind power production. DR aggregators’ behavior is modeled through a profit maximization function. Aggregators determine their DR trading shares with ISO and customers through three DR options, including load curtailment, load shifting, and load recovery. A stochastic bilevel problem is formulated, in which in the upper level, the ISO minimizes the total operation cost, and in the lower level, the DR aggregator maximizes the profit. Afterwards, the problem is transferred to a single-level mathematical problem with equilibrium constraints by replacing the lower level program with its Karush–Kuhn–Tucker (KKT) conditions. As a result, the total operation cost is reduced using the proposed method comparatively to run the program without considering the lower level.