Strategic integration of adiabatic compressed air energy storage in urban buildings: Enhancing energy efficiency through gray wolf optimizer-enhanced dynamic simulation framework

Adiabatic Compressed Air Energy Storage (A-CAES) systems offer significant potential for enhancing energy efficiency in urban buildings but are underutilized due to integration and sizing challenges. This study introduces an innovative simulation-optimization framework using the Gray Wolf Optimizer (GWO) to design and size decentralized A-CAES systems tailored for urban applications. By developing a comprehensive time-dependent technique and exploring multiple energy management approaches, we optimize A-CAES configurations considering thermodynamic, economic, and environmental factors. Our findings demonstrate that integrating A-CAES with solar photovoltaic systems, optimized via GWO, substantially improves energy cost savings, enhances grid demand management, and reduces carbon emissions compared to traditional energy systems. Specifically, combined strategies focusing on both load management and solar integration yield the most cost-effective and environmentally beneficial outcomes. The optimized A-CAES systems exhibit high self-sufficiency in utilizing PV-generated power and maintain viable investment returns even under less optimal conditions. These results provide critical insights for policymakers, energy consultants, and property owners, supporting the adoption of innovative and sustainable energy solutions in urban buildings.