Robust offering and bidding curves of compressed air energy storage plant via stochastic p-robust optimization technique

Abstract

This work presents a stochastic optimization technique (SOT) for CAESP to handle uncertain data and generate bidding-offering curves contributing to the electricity markets. Furthermore, the robust bidding-offering curves are obtained using the proposed stochastic p-robust optimization technique (SPROT), a novel robust-level measurement technique. The robust-oriented form of CAESP is modeled via SPROT, and robust scheduling is obtained by minimizing the maximum relative regret (MRR) in the worst scenario and maximizing the total performance profit. To mitigate the relative regret (RR) imposed by uncertain parameters, the SPROT is employed in conjunction with stochastic problems. Using the SPROT in stochastic issues enables the CAESP operator to derive a robust strategy that yields consistent profitability across all scenarios. The results indicate that the anticipated profit of the stochastic model is $9,584.65. When the SPROT is implemented, the proposed approach yields a profit of $9,188.36, suggesting a 4.13% decrease in the predicted profit and a 43.22% reduction in the MRR.