PROJECT PROFILE: University of Central Florida (ENERGISE)

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Title: Scalable/Secure Cooperative Algorithms and Framework for Extremely-high Penetration Solar Integration (SolarExPert)
Funding Opportunity: ENERGISE
SunShot Subprogram: Systems Integration
Location: Orlando, FL
Amount Awarded: $1,275,000
Awardee Cost Share: $513,424

This project focuses on the research and design of highly scalable technologies for utilities’ electric distribution systems to operate reliably and securely with extremely high penetration of distributed energy resources like solar. This includes a modular, plug-and-play, and scalable sustainable grid platform that allows for real-time operation and control of a large-scale distribution network, as well as advanced distribution operation and control functions to manage extremely high penetration of solar.

Approach

The research team will take a hierarchical approach in designing the sustainable grid platform. From the bottom up, the team will implement distribution load flow using simulation software. Within the distributed control and optimization layer, micro-phasor measurement units will be used to provide real-time measurements, with data providing prediction and correction for dynamic state estimation, which will complement existing quasi-static system estimation using supervisory control and data acquisition data. Photovoltaic forecasting and stochastic analysis will provide the forecasted solar power output and load profile for day-ahead planning and real-time operation. With these inputs, distributed stochastic optimal power flow will determine the optimal generation scheduling and distributed system state estimation will determine voltage phasors.

Innovation

The sustainable grid platform will be scaled up through advanced models and parallel computing to accommodate a 1-milion node test system. Specifically, the platform will include online distributed stochastic optimal power flow based on dynamic, real-time, distributed feedback control; online distributed system state estimation algorithms based on prediction-correction methods for time-varying convex optimization; distributed Volt/VAR optimization and frequency control algorithms based on distributed cooperative control and optimization; and distribution system restoration strategy based on distributed cooperative control of multi-agent systems.