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

Key Points

• The aim is to maximize revenue for hydropower units in the day-ahead market under price uncertainty.
• Developed a bi-layer quoting model with an outer layer for scheduling and an inner layer for pricing.
• Used dynamic programming for optimizing hydropower scheduling based on expected returns.
• Employed genetic algorithms to generate a tiered pricing strategy considering various electricity price scenarios.
• The model improved expected revenue by 2.4% compared to traditional pricing methods.
• Successfully accommodates operational constraints of hydropower units while optimizing pricing strategies.