A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties

Abstract

Developing effective bidding strategies in electricity spot markets is crucial for wind-storage systems (WSS) to improve profits and mitigate risk. During real operations, the bidding strategies of WSS are significantly affected by external uncertainties and internal uncertainties. However, some existing studies only consider a single influencing factor or fail to account for the interaction between the market and participants. To address this problem, a stochastic-robust optimal bidding model (OBM) of WSS considering the combined impacts of external and internal uncertainties is proposed. The proposed model is structured as a bi-level optimization problem to reflect the interaction between WSS bidding and day-ahead market clearing. At the upper level, the bidding strategy of WSS is developed based on the market clearing results by using the stochastic-robust method to consider uncertainties. In the lower level, the clearing process of the day-ahead market is modeled to determine the clearing market price and quantity. To improve computational feasibility and robustness, the modified Column-and-Constraint Generation (C&CG) algorithm is applied to solve the multi-scenario problem efficiently. To validate the effectiveness and practicality of the proposed model, the paper conducts tests on the IEEE 118-bus system and a real-world large-scale system. Taking the IEEE 118-bus system as an example, compared to traditional methods, the proposed approach enables the WSS to achieve a 12% increase in average revenue and a 12.3% improvement in CVaR, indicating higher revenue with lower risk. The computation times of these two test systems are 20 min and 48 min, which can meet the requirements of practical day-ahead market operations.