Power System Optimization, Mixed Integer Programming, Optimization Algorithms, and Machine Learning
Ph.D. from the University of Connecticut (UConn)
Bachelor's degree from Shenyang Jianzhu University (SJZU)
Dr. Jianghua Wu received his Ph.D. from the University of Connecticut (UConn) in 2023. He is currently a Research Scientist with the Shenzhen Research Institute of Big Data. His main research interests are power system optimization with distributed energy sources, grid optimization considering renewable resources and energy storage systems, mixed-integer programming algorithms, and the integration of traditional optimization and machine learning.
1. J. Wu, P. Luh, Y. Chen, M. Bragin and B. Yan, “A Novel Optimization Approach for Subhourly Unit Commitment with Large Numbers of Units and Virtual Transactions,” IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3716–3725, Sep. 2022.
2. J. Wu, P. Luh, Y. Chen, B. Yan and M. Bragin, “Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,” IEEE Transactions on Power Systems, Jan. 2023. (Accepted)
3. J. Wu, Z. Wang, Y. Chen, B. Yan and M. A. Bragin, P. B. Luh, “A Hybrid Machine Learning and Optimization Approach with Feasibility Layer Assistance for Sub-hourly Unit Commitment,” (Under Review)
4. W. Lin, J. Hua, H. Jian, J. Xue, J. Wu, C. Wang and Z. Lin, “High-dimension tie-line security regions for renewable accommodations,” Energy, vol 270, May 2023, 126887.
5. J. Wu, P. B. Luh, Y. Chen, B. Yan, and M. A. Bragin, “A Decomposition and Coordination Approach for Large Sub-hourly Unit Commitment,” 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Aug. 2020.