Biography

Chaoyue Zhao joined ISE in the fall of 2019 as an assistant professor. Prior to this appointment, she was appointed as the Jim & Lynn Williams Assistant Professor at Oklahoma State University.

 

Education

• Ph.D. Industrial and Systems Engineering, University of Florida, 2014
• B.S. Information and Computing Sciences, Fudan University, 2010

Research Statement

Dr. Zhao works on data-driven optimization methodologies to support strategic and operational planning in power systems management. She developed innovative data-driven approaches to enable effective decision-making under uncertainty for power system scheduling problems such as optimal power flow and unit commitment. Her analytical models advance scalable solution methods to improve cost-effectiveness, streamline daily power system operations, and mitigate system disruptions and risk.

Dr. Zhao has received multiple grants from the federal agencies such as the National Science Foundation, Department of Transportation and Argonne National Laboratory. She is the recipient of awards including the runner up of the Pritsker Doctoral Dissertation Award, and Energy Systems Division Outstanding Young Investigator Award in IISE.

Select publications

1. A. Bagheri, C. Zhao, “Distributionally robust transmission system reliability analysis under N − k security criterion,” IEEE Transactions on Reliability, 68(2), 653 - 662, 2019.
2. S. Babaei, C. Zhao, L. Fan, “Parameter learning of virtual power plants via machine learning and data-driven optimization”, IEEE Transactions on Power Systems, Early Access, 2019.
3. S. Babaei, C. Zhao, L. Fan, T. Liu, “Incentive-based coordination mechanism for backup renewable energy investment”, IEEE Transactions on Power Systems, 34(3): 1761-1770, 2019.
4. C. Zhao, R. Jiang, “Distributionally robust contingency-constrained unit commitment,” IEEE Transactions on Power Systems, 33(1): 94-102, 2018.
5. Y. Zhou, T. Liu, C. Zhao, “Backup Capacity Coordination with Renewable Energy Certificates in a Regional Electricity Market”, IISE Transactions, 55(8): 711-719, 2018.
6. A. Bagheri, C. Zhao, F. Qiu, J. Wang, “Resilient transmission hardening planning in a high renewable penetration era,” Accepted for publication of IEEE Transactions on Power Systems, 2018.
7. C.Zhao, Y.Guan,“Data-drivenrisk-aversestochasticoptimizationwithWasser- stein metric,” Operations Research Letters, 46(2), 262-267, 2018.
8. Y. Guo, C. Zhao, “Islanding-aware robust energy management for microgrids,” IEEE Transactions on Smart Grid, 9(2): 1301-1309, 2018.
9. A. Bagheri, J. Wang, C. Zhao, “Data-driven stochastic transmission expansion problem,” IEEE Transactions on Power Systems, 32(5): 3461-3470, 2017.
10. T. Ding, C. Zhao, “Robust total supply capability considering distribution net- work reconfiguration under N − k transformer contingency and the decomposition method,” IET Generation, Transmission & Distribution, 11(5): 1212-1222, 2017.
11. T. Ding, C. Zhao, T. Chen, R. Liu “Conic programming based Lagrangian relaxation method for DCOPF with transmission losses and its zero-gap sufficient condition”, IEEE Transactions on Power Systems, 32(5): 3852-3861, 2017.
12. T. Ding, C. Zhao, “Robust optimal transmission switching with the consideration of corrective actions for N − k contingencies,” IET Generation, Transmission & Distribution, 10: 3288-3295, 2016.
13. C. Zhao, Y. Guan, “Data-driven stochastic unit commitment for integrating wind generation,” IEEE Transactions on Power Systems, 31: 2587-2596, 2015.
14. C. Zhao, Q. Wang, J. Wang, Y. Guan, “Expected value and chance constrained stochastic unit commitment ensuring wind power utilization,” IEEE Transactions on Power Systems, 29: 2696-2705, 2014.
15. C. Zhao, Y. Guan, “Extended formulations for stochastic lot-sizing problem,” Operations Research Letters, 42: 278-283, 2014.
16. C. Zhao, Y. Guan, “Unified stochastic and robust unit commitment,” IEEE Transactions on Power Systems, 28: 3353-3361, 2013.
17. C. Zhao, J. Wang, J. P. Watson and Y. Guan, “Multi-stage robust unit commitment considering wind and demand response uncertainties,” IEEE Transactions on Power Systems, 28: 2708-2717, 2013.

Honors & awards

• National Science Foundation, “Collaborative Research: Power System Flexibility: Metric, Assessment, and Algorithm”, Principal Investigator, 2021-2024
• National Science Foundation, “Re-engineering for adaptable lives and businesses”, Co-Principal Investigator, 2021-2026
• National Science Foundation, “FW-HTF-P/Collaborative Research: Designing a Market-based Optimization Tool for the Future of Work: Balancing Remote Work and Community Vitality in Post-COVID American Cities”, Co-Principal Investigator, 2022-2023
• National Science Foundation, “Collaborative Research: Enhancing Power System Resilience via Data-Driven Optimization”, Principal Investigator, 2017-2020
• National Science Foundation, “Collaborative Research: Data-Driven Risk-Averse Models and Algorithms for Power Generation Scheduling with Renewable Energy Integration”, Principal Investigator, 2016-2019
• Department of Transportation, “Transportation Consortium of South-Central States (Tran-SET): Study the Impacts of Freight Consolidation and Truck Sharing on Freight Mobility”, Co-Principal Investigator, 2016-2017
• Argonne National Laboratory, “Data-Driven Optimization on Power Grid In- vestment, Operation and Resilience”, Principal Investigator, 2016
• Oklahoma Emergency Management, “Feasibility Study: Hazardous Material Movement Model for HazMat Transportation in Oklahoma, Co-Principal Investigator, 2015-2016

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