Hybrid Energy Storage Dispatch: A Bi-Level Look-Ahead Learning-Assisted Model

Compressed Air Energy Storage (CAES) and Cryogenic Energy Storage (CES) are emerging as promising technologies for sustainable grid-scale applications. To surmount the capacity and geological limitations of traditional CAES systems, this study capitalizes on the hybridization of above-ground CAES with CES, utilizing energy conversion between compressed and liquid air. Here, we develop a comprehensive mathematical model for the operation of the hybrid CAES-CES plant, incorporating discrete constraints to manage internal energy transfers and coordination. The model is leveraged to develop the: i) look-ahead dispatch schedule over the following days to enhance adaptability in managing stored energy to maximize benefits, and ii) strategic behavior in electricity markets through unified offers/bids submission. The dispatch problem is structured as a bi-level optimization, with the lower-level addressing market-clearing processes and the upper-level handling storage profit maximization. We reformulate the bi-level setup into a mixed-integer programming model using a mathematical program with equilibrium constraints. To mitigate the computational burden associated with the large number of integer variables in the optimization, we implement a learning-assisted framework for warm-starting these variables. Numerical results show that the hybrid plant can yield up to a 9.08% profit improvement over the standalone alternative under the look-ahead strategy. Further, results demonstrate that under the bi-level setup, the warm-start strategy effectively reduces computation time by 29.30% and 13.35% in the 24- and 118-bus networks, respectively.