In an innovative move for the energy sector, the Massachusetts Institute of Technology’s Laboratory for Information and Decision Systems (MIT LIDS) has been awarded $1.365 million in funding from the Appalachian Regional Commission (ARC) to develop AI-driven generative models for better smart grid modeling.
The grant facilitates the launch of the project titled “Forming the Smart Grid Deployment Consortium (SGDC) and Expanding the HILLTOP+ Platform,” an initiative that will leverage generative artificial intelligence (AI) to create advanced grid modeling and training algorithms for energy tech startups.
MIT LIDS, led by Principal Research Scientist Kalyan Veeramachaneni, will collaborate with a team of universities and organizations led by Tennessee Tech University. The team, which includes collaborators from Ohio, Pennsylvania, West Virginia, and Tennessee, will focus on creating AI-driven generative models for customer load data.
The generative models created through this project have a wide range of applications. They can be trained on existing data to generate additional, realistic data that can enhance limited datasets or replace sensitive ones. This data can be used to predict and plan for specific scenarios, such as how the load on the grid might change if an additional 1,000 households adopt solar technologies, or how the load might vary throughout the day.
The AI models developed by the team will guide modeling services based on the HILLTOP+ microgrid simulation platform, originally prototyped by MIT Lincoln Laboratory. This platform will be used to model and test new smart grid technologies in a safe, virtual environment, providing rural electric utilities with increased confidence in deploying smart grid technologies, including utility-scale battery storage.
Energy tech startups will also benefit from the HILLTOP+ grid modeling services by enabling them to develop and virtually test their smart grid hardware and software products for scalability and interoperability. This project aims to mitigate the risks associated with deploying these new technologies, especially for rural electric utilities and energy tech startups.
The goal of this project is to exemplify how generative AI can transform the energy sector. “Generative AI technologies and their development have to be closely integrated with domain expertise”, said Veeramachaneni. The project is a testament to collaboration and innovation and is expected to drive positive change in the energy sector.
In conclusion, this project highlights how AI can revolutionize the energy sector and the power of collaboration in driving innovation. The integration of AI in grid modeling serves as an example of the transformative power of AI in various sectors.
