Description

The planning, real-time monitoring and security of future power grids requires modeling and analysis capabilities beyond those available presently. Existing models are becoming obsolete as new technologies such as renewables and power electronic based devices become more prevalent, and traditional power flow simulation algorithms lack the robustness and scalability that is needed to represent combined transmission and distribution systems over all dynamics and contingencies. Our work is based on a unique equivalent split circuit formulation that that enables adaptation and application of techniques that were developed for circuit simulation to robustly analyze power grids. Our software tool, SUGAR, provides a foundation for: i) incorporating transmission and distribution models that capture true physics behavior; ii) unifying steady state, dynamics and transient analyses; iii) assessing feasibility and solution of optimal power flow conditions.

Lab Members

  • Aayushya Agarwal
    Graduate Student
    Optimization and Modeling for Power Systems and Digital Twins
  • Elizabeth Foster
    Graduate Student
    Optimization of Distribution Grids
  • Meshach Hopkins
    Graduate Student
    Digital Twin Systems
  • Shimiao (Cindy) Li
    Graduate Student
    State Estimation, Anomaly Detection, and Machine Learning for Power Systems
  • Tim McNamara
    Graduate Student
    Power System Simulation and Optimization
  • Naeem Turner-Bandele
    Graduate Student
    Stochastic Modeling and Simulation of Power Systems

Recent Publications

  1. P. Donti, A. Agarwal, L. Pileggi, Z. Kolter, Adversarially Robust Learning for Security-Constrained Optimal Power Flow, Neural Information Processing Systems, 2021

  2. T. McNamara, A. Pandey, A. Agarwal, L. Pileggi, Two-Stage Homotopy Method to Incorporate Discrete Control Variables into AC-OPF,  Power Systems Computation Conference (PSCC), June 27-July 1, 2022

  3. A. Agarwal, P. Donti, L. Pileggi, Employing Adversarial Robustness Techniques for Large-Scale Stochastic Optimal Power Flow, 22nd Power Systems Computation Conference (PSCC), Porto, Portugal, 2022

  4. A. Agarwal, L. Pileggi, Efficient Steady State Analysis of the Grid Using Electromagnetic Transient Models, 22nd Power Systems Computation Conference (PSCC), Porto, Portugal, 2022.

  5. E. Foster, A. Pandey, L. Pileggi, Three-Phase Infeasibility Analysis for Distribution Grid Studies,  Power Systems Computation Conference (PSCC), June 27-July 1, 2022.

  6. Agarwal, L. Pileggi, “Large Scale Multi-Period Optimal Power Flow with Energy Storage Systems Using Differential Dynamic Programming,” IEEE PES Transactions on Power Systems, 2021.

  7. (Cindy) Li, A. Pandey, B. Hooi, C. Faloutsos, and L. Pileggi, “Dynamic Graph-Based Anomaly Detection in the Electrical Grid,” IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2021.31328

  8. A. Agarwal, A. Pandey and L. Pileggi, Fast AC Steady-State Power Grid Simulation and Optimization Using Prior Knowledge, (Best Paper Award) IEEE PES General Meeting, July 25-29, 2021.

  9. S. (Cindy) Li, A. Pandey, and L. Pileggi, A WLAV-based Robust Hybrid State Estimation using Circuit-theoretic Approach, (Best Paper Session) IEEE PES General Meeting, July 25-29, 2021.

  10. N. T. Bandele, A. Pandey and L. Pileggi, Analytical Inverter-Based Distributed Generator Model for Power Flow Analysis, IEEE PES General Meeting, July 25-29, 2021.