This paper addresses the multi-source uncertainties faced by horizontal pumped storage-wind-solar (HWS) hybrid systems in the day-ahead market by proposing a hybrid stochastic-robust optimization model for bidding and scheduling. The model employs a scenario-based method to capture the randomness of wind and solar power output, utilizes Information Gap Decision Theory (IGDT) to handle the epistemic uncertainty in runoff inflow forecasting, and constructs a price-acceptance probability function based on historical statistics to characterize the market mechanism. By maximizing the system’s tolerable uncertainty immunity gap, the model co-optimizes generation schedules, pumped-storage operation, and market bids while ensuring that revenue under the worst-case inflow scenario does not fall below a predefined threshold. Simulation results based on an actual project in Hubei Province demonstrate that the proposed method effectively balances revenue and risk, showing significant advantages in both revenue stability and robustness compared to the system before retrofitting. This study provides practical decision-making support for hybrid systems with horizontal pumped storage participating in electricity markets.
Zheng et al. (Sun,) studied this question.