A bi-layer quoting model for hydropower units participating in the day-ahead market considering multiple electricity price scenarios

To maximize the revenue of a hydropower station in the uncertain day-ahead market， this paper proposes a bi-level model to formulate the stepped bidding curve for each hydropower unit， ensuring compliance with operational constraints. The model features a nested structure consisting of an outer model for station-level scheduling and an inner model for bidding optimization. The outer model utilizes dynamic programming to determine the short-term optimal scheduling for the hydropower station， considering hydraulic constraints and integrating the expected revenue feedback from the inner model. The inner model first addresses electricity price uncertainty using multiple electricity price scenarios. Subsequently， based on an optimal unit commitment， it develops a unit bidding strategy to generate these curves， taking into account unit-specific constraints and bilateral contract obligations. A genetic algorithm is employed to optimize the bidding curves， with the station's expected revenue serving as the fitness value. The results of a case study demonstrate that this model increases the expected revenue of a hydropower station by 2.4% compared with the current conservative approaches， providing a valuable decision-support tool for market participation.