Dynamic Characteristics-Based Capacity Optimization Strategy for Hybrid AA-CAES and Battery Storage Systems in Source-Grid-Load-Storage Integrated Base

Abstract

Advanced adiabatic compressed air energy storage (AA-CAES) is a promising large-scale energy storage technology, offering a long lifespan, low maintenance, and high safety. However, its slower response speed limits its ability to handle the rapid fluctuations of wind and solar power. Combining AA-CAES with battery storage in a hybrid system provides an optimal solution for integrated energy bases, prompting the need for robust capacity planning. Existing AA-CAES planning strategies, developed primarily for grid-connected applications, often neglect AA-CAES’s dynamic characteristics, making them unsuitable for hybrid contexts. To address this issue, this paper proposes a capacity optimization strategy that incorporates AA-CAES’s dynamic behavior into a cost-minimization model with operational constraints. Using historical wind, solar, and load data, the proposed approach is compared with conventional battery-only configurations. The case study demonstrates that the proposed CAES-Li hybrid energy storage system achieves 30-45% annualized cost reductions compared to traditional Li-ES configurations, with sensitivity analyses revealing critical optimization pathways through efficiency enhancements and technology cost reductions.