MIT’s Laboratory for Information and Decision Systems (LIDS) has been awarded $1,365,000 in funding from the Appalachian Regional Commission (ARC) to support its involvement with an modern project, “Forming the Smart Grid Deployment Consortium (SGDC) and Expanding the HILLTOP+ Platform.”
The grant was made available through ARC’s Appalachian Regional Initiative for Stronger Economies, which fosters regional economic transformation through multi-state collaboration.
Led by Kalyan Veeramachaneni, principal research scientist and principal investigator at LIDS’ Data to AI Group, the project will give attention to creating AI-driven generative models for customer load data. Veeramachaneni and colleagues will work alongside a team of universities and organizations led by Tennessee Tech University, including collaborators across Ohio, Pennsylvania, West Virginia, and Tennessee, to develop and deploy smart grid modeling services through the SGDC project.
These generative models have far-reaching applications, including grid modeling and training algorithms for energy tech startups. When the models are trained on existing data, they create additional, realistic data that may augment limited datasets or stand in for sensitive ones. Stakeholders can then use these models to know and plan for specific what-if scenarios far beyond what may very well be achieved with existing data alone. For instance, generated data can predict the potential load on the grid if a further 1,000 households were to adopt solar technologies, how that load might change throughout the day, and similar contingencies vital to future planning.
The generative AI models developed by Veeramachaneni and his team will provide inputs to modeling services based on the HILLTOP+ microgrid simulation platform, originally prototyped by MIT Lincoln Laboratory. HILLTOP+ shall be used to model and test recent smart grid technologies in a virtual “secure space,” providing rural electric utilities with increased confidence in deploying smart grid technologies, including utility-scale battery storage. Energy tech startups may also profit from HILLTOP+ grid modeling services, enabling them to develop and virtually test their smart grid hardware and software products for scalability and interoperability.
The project goals to help rural electric utilities and energy tech startups in mitigating the risks related to deploying these recent technologies. “This project is a strong example of how generative AI can transform a sector — on this case, the energy sector,” says Veeramachaneni. “With a view to be useful, generative AI technologies and their development must be closely integrated with domain expertise. I’m thrilled to be collaborating with experts in grid modeling, and dealing alongside them to integrate the newest and best from my research group and push the boundaries of those technologies.”
“This project is testament to the ability of collaboration and innovation, and we look ahead to working with our collaborators to drive positive change within the energy sector,” says Satish Mahajan, principal investigator for the project at Tennessee Tech and a professor of electrical and computer engineering. Tennessee Tech’s Center for Rural Innovation director, Michael Aikens, adds, “Together, we’re taking significant steps towards a more sustainable and resilient future for the Appalachian region.”