A Hybrid Evolutionary Algorithm for Strategic Bidding in Day-Ahead Market with Flexible Demand

Bi-level optimization is a widely used tool for modelling the strategic bidding problems in electricity markets. Traditionally, bi-level optimization problems can be solved after converting them to single-level Mathematical Problems with Equilibrium Constraints (MPEC) by Karush-Kuhn-Tucker (KKT) conditions. However, the non-convex and non-linear operating variables of the generators render KKT conditions and MPEC unavailable in strategic bidding optimization problems. To address this problem, this paper proposes a hybrid evolutionary algorithm to solve the bi-level optimization strategic bidding problem in a day-ahead electricity market with flexible demand by transforming the original lower level mixed-integer non-linear problem (MINLP) into mixed-integer linear problem (MILP). The case study result demonstrates the ability of the proposed method to solve the bi-level optimization problem and find a more profitable bidding strategy compared to the benchmark case with a competitive behaviour.