(236d) Energiapy - a Decision-Making and Risk Management Tool for Multi-Scale Modeling and Optimization

This necessitates the explicit consideration of spatio-temporal characteristics and variabilities over
multiple scales in the analysis of proposed low-carbon transition scenarios. Future systems may
further involve integrated value chains of multiple sectors with a multitude of technology options
for power generation and energy storage, production of chemicals and synthetic fuels, and transportation.

To this end, holistic multi-scale systems tools can serve as platforms to resolve proposed transition
pathways, linking process design with scheduling decisions and supply chain planning. Such an
approach also enables trade-off analysis and the determination of sensitivity to fluctuations across
seemingly disparate scales.

In this work, we present the developments of energiapy, a python-based decision-making and risk
management tool for multi-scale modeling and optimization of systems to address the transition
of energy systems, infrastructure material, and energy feedstock towards a low carbon paradigm.
Through its various functionalities, energiapy draws from: (i) high-fidelity process models, (ii)
surrogate modeling techniques (iii) circular economy and GRI metrics [1], (iv) scenario reduction
methods [2], (v) bespoke algorithms [3], and (vi) online databases. Furthermore, energiapy allows
users to visualize resource availability and demands across various temporal and geographic scales
and resolutions to compare competing objectives and transition pathways, and validate model
outputs.

The key capabilities of the module are highlighted through a framework that utilizes 1) gPROMS
process models, 2) neural network based surrogate modeling, 3) hierarchical clustering for scenario
reduction, and 4) data from publicly-available databases to model a future hydrogen economy.