I-Corps: Translation Potential of an Artificial Intelligence (AI)-Driven control platform for localized, intelligent power systems

Award ID: 2630209

This I-Corps project is based on the development of a control platform for localized, intelligent power systems. Currently, many localized, intelligent power systems, called microgrids, rely on conventional supervisory control methods that make operational decisions using static rules and limited system awareness. This results in underutilized resources, inefficient battery utilization, unnecessary use of fuel-powered generators, increased operating costs, and reduced overall system performance. This technology provides an intelligent software platform that continuously evaluates system conditions and supports adaptive operational decision-making to improve overall management. Designed to complement existing and future microgrid installations without requiring major hardware modifications, the technology has broad potential across multiple applications. This may improve energy utilization, reduce operating costs, enhance energy resilience, and strengthen reliability. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of an intelligent control platform for energy microgrids. This technology integrates data-driven system modeling, predictive operational analytics, intelligent supervisory control algorithms, and data-driven decision support to improve coordination among energy resources, battery energy storage systems, backup generation assets, and electrical loads. Unlike conventional rule-based supervisory controllers that primarily respond to current operating conditions, the proposed approach incorporates predictive system awareness to evaluate dynamic operating scenarios and recommend more efficient operational strategies that improve overall system performance. Preliminary research has established the system architecture, supervisory control framework, operational workflows, and initial simulation-based feasibility necessary to demonstrate the technical viability of the proposed approach. This technology may provide a scalable, intelligent energy management platform capable of improving the performance, efficiency, and economic value of energy microgrids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman