pyCAES a General Framework for CAES Modelling and Optimization

Compressed Air Energy Storage (CAES) is a mature yet evolving technology that aligns well with the needs of an energy system increasingly dominated by renewables. Site screening and early design optimization are key steps to generalize its use for both grid support and industrial symbiosis.

pyCAES is a software tool specifically developed for system design optimization and dynamic modeling. It is designed to capture complex thermodynamic interactions and perform sensitivity or what-if scenario analyses.

This tool provides valuable insights into optimal design choices and operational strategies, which are essential for de-risking investments in new CAES plants and ensuring a quantitative decision support system for early technology assessment.

The software’s capabilities are demonstrated through the modeling of a two-compressor, two-thermal storage (TES), two-stage turbine system. The results highlight the tool’s effectiveness in capturing complex operating conditions with diagnostic capabilities for each modeled component.

The novelty of pyCAES resides in its flexibility to switch easily between predefined components, different levels of complexity in modeling to prioritize speed or detailed simulation, and the possibility to couple with economic models for a viability study.