The availability of high-resolution historical data from the grid, smart inverters, and advanced metering infrastructure has made it possible to develop accurate models of the state of the power system, available renewable generation, demands, and user preferences. This project aims to develop proactive, data-driven energy management strategies that combine real-time measurements with predictions of these models to optimally control a vast number of Distributed Energy Resources (DERs) for improved reliability and efficiency. The efficacy of these strategies is evaluated using an advanced test environment available in the Future Smart Grid Technologies lab. Additionally, a state-of-the-art monitoring and control infrastructure will be developed for the smart grid which enables large-scale integration and seamless operation of geographically dispersed DERs. This infrastructure will support the execution of decentralized, proactive, and data-driven energy management strategies, which will play a pivotal role in enhancing efficiency, reliability, and cybersecurity of the smart grid.
DPMU 2018 Workshop Co-Chair
Conference/Workshop
ePerf 2018 Workshop Co-Chair
Conference/Workshop
Advances in Distribution System Monitoring
Book Chapter
EnergyBoost: Learning-based Control of Home Batteries
Conference Proceedings
Harmonic State Estimation in Distribution Systems with Smart Meter and DPMU Data
Scholarly Refereed Journal
Leveraging Sparsity in Distribution Grids: System Identification and Harmonic State Estimation
Scholarly Refereed Journal
On Identification of Distribution Grids
Scholarly Refereed Journal
Sparse Bayesian Harmonic State Estimation
Conference Proceedings