PNNL

Abstract

To navigate competing objectives in energy infrastructure co-design, this report presents an integrated suite of tools, beginning with pyMOODS, a post-optimization analytics framework that helps stakeholders evaluate multi-criteria trade-offs and scenario performance among candidate designs. The computational demands of generating these candidate designs are managed by CAMEO, a modular, graph-based workflow architecture that coordinates containerized solvers across high-performance computing environments. Finally, these co-design principles are applied through a data center energy storage formulation that sizes grid-interactive batteries to balance capital investment against demand response, load smoothing, and voltage support capabilities.