Day-ahead Bidding Strategies for Generators in Electricity Market Based on Bilevel Stochastic Optimization

In the day-ahead market, generators can optimize their bids to maximize profits. However, the existing research on bidding strategies of generators tend to ignore the variation in competitors’ bids. To bridge the gap, this paper proposes a method for forecasting competitors’ bids and develops a bilevel stochastic optimization model for generator’s strategic bidding, where scenarios are generated based on the predicted bids of competitors. The model is reformulated into a mathematical program with equilibrium constraints (MPEC) problem by using the Karush-Kuhn-Tucker (KKT) conditions of the lower-level problem and finally, it is converted into an easily solvable Mixed-integer linear programming (MILP) problem through linearization methods such as constraint learning. Case studies demonstrate the effectiveness of the proposed framework.