Helping Energy Models Transition to the Cloud
The Energy Technology Systems Analysis Program (ETSAP) is the longest running Technology Collaboration Programme of the International Energy Agency (IEA). The annual meeting of ETSAP and International Energy Workshop for the year 2017 was hosted at the beautiful campus of University of Maryland, College Park. I was invited to present to the Executive Committee (ExCom) of the ETSAP on how Energy Models can be made more accessible to the masses using Cloud Infrastructure.
When I transitioned from Energy Modeling career to IT infrastructure, I did not realize that I will be back helping energy modelers leverage my previous life skills and my current areas of expertise and passion.
The energy models are very specialized beasts – very knowledge and computationally intensive. And refactoring the code developed over decades to make it accessible using web-interface is very time and resource intensive. However, it is possible to develop a UI/Ux using modern web technologies and keep the back-end same.
I shared our recent effort in helping the North Carolina State University’s open source energy model TEMOA (model.temoacloud.com) migrate to the cloud infrastructure. The open source energy model has been developed by Prof Joseph DeCarolis and his team at NC State. Specifically, we designed and developed a Web-based interface using Django framework, that allows any un-initiated modeler familiarize him or herself with how the model works. A middleware was developed in Python and the model framework is in Pyomo and some open source solvers.
I also shared our experience helping one of the leading global energy modeling team KANORS leverage cloud infrastructure to facilitate running the global energy model to generate and analyze a very large number of scenarios efficiently.
While it will take some time for Energy Models and Modelers to catch up with the modern software development tools and platforms, it is possible to leverage cloud infrastructure to make their models accessible to masses. In addition, empirical experimentation can be used to identify optimal cloud infrastructure to run a specific model. Our team at SAM IT has extensive experience and can help clients leverage cloud computing to augment their modeling capabilities.