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Simulation study reveals that reservoir boundary conditions and horizontal permeability govern discharge efficiency in porous aquifers, highlighting design requirements for scalable renewable grid...
The deployment of renewable energy sources requires energy storage solutions to buffer mismatches between electricity supply and demand. Compressed air energy storage (CAES) is an appealing grid-scale scalable energy storage solution. Existing CAES models mostly focus on using human-made tanks or salt caverns as storage media, where the pressure responses are linear and instantaneous. However, such models are not applicable for CAES in porous media (PM-CAES), where gas pressure and flow rates are temporally and spatially changing and strongly depend on formation properties. This study investigates the feasibility of implementing an adiabatic CAES (A-CAES) system integrated with a solar power project in Australia. A daily operating schedule with 8 h of charging, 5.5 h of discharging, and a shut-in period was simulated over a 90-day summer season. A coupled CAES modeling framework is developed to integrate the surface power plant model, wellbore model, and subsurface geological storage model. Several scenarios of CAES in a saline aquifer are simulated to examine pressure and gas flow responses under varied key formation characteristics, including absolute permeability, anisotropy, wettability, and boundary conditions. Results highlighted that although target injection rates can be maintained, discharging performance is strongly influenced by boundary conditions, where closed-boundary systems outperform open-boundary cases. Reservoir permeability, especially in horizontal direction, is critical to enhance both injectivity and productivity. A 1,600-mD ( 1.58 × 10 − 12 m 2 ), open-boundary aquifer drilled with 13 wells could deliver 100 MW of continuous output for 5.5 h, corresponding to 550 MWh storage capacity, with a round-trip efficiency of 68.7%. This coupled CAES modeling framework considers formation properties to better guide optimized CAES design, well configuration, and charge/discharge scheduling.
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Bian et al. (2026) studied this question.
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