Yishen Wang (王轶申) joined State Grid Smart Grid Research Institute Co. Ltd. (SGRI, previously known as GEIRI) since 2021, currently as Principal Research Scientist. His research interests include machine learning and optimization applications in low-carbon power system operation and economics.
Dr. Wang serves as the Associate Editor of IEEE Systems Journal and IET Renewable Power Generation. He was a Guest Editor of Journal of Modern Power Systems and Clean Energy. He is IEEE Senior Member, CIGRE Member, CSEE Member and CES Senior Member.
PhD in Electrical Engineering, 2017
University of Washington
MS in Electrical Engineering, 2013
University of Washington
BEng in Electrical Engineering, 2011
Tsinghua University
Institute of Computing and Applications
Previously as Technical Leader
Working on the following topics:
Jointly with Precourt Institute for Energy and Department of Management Science and Engineering
Hosted by Prof. Yinyu Ye
AI & System Analytics Group
Previously as Postdoctoral Researcher and Power System Research Engineer
Working on the following topics:
Energy Systems Division
Worked with Dr. Audun Botterud, Dr. Zhi Zhou and Dr. Cong Liu on renewable integration with stochastic unit commitment
Data Analytics Group
Worked with Dr. Hongbo Sun on distribution system reconfiguration with energy storage
Renewable Energy Analysis Lab (REAL LAb)
Worked with Prof. Daniel Kirschen on the following topics:
[Granted: 4]
P1. H. Sun, Y. Wang, “Dynamic and Adaptive Configurable Power Distribution System”, US Patent 9,876.356, issued 2018.
P2. Z. Yi, Y. Wang, B. Huang, D. Shi, Z. Wang, “Model Predictive Controller for Autonomous Hybrid Microgrids”, US Patent 10,651,654, issued 2020.
P3. Z. Yu, Y. Wang, H. Li, C. Fu, Z. Wang, and D. Shi, “Bayesian Estimation Based Parameter Estimation for Composite Load Model,” US Patent 11,181,873, issed 2021.
P4. Y. Wang, H. Li, D. Shi, Q. Zhang, C. Xu, Z. Wang, “Submodular Load Clustering with Robust Principal Component Analysis,” US Patent 11,264,799, issued 2022.
[Allowed/Pending: 7]
P5. Y. Wang, Q. Chang, D. Shi, Z. Wang, “A Probabilistic Two-stage Load Forecasting Method using Point Forecast and Quantile Regression Neural Network,” US Patent 62,741,408, 2018.
P6. X. Wang, Y. Wang, D. Shi, Y. Lin, S. Wang, and Z. Wang, “Two-stage WECC Composite Load Modeling with A Double Deep Q-Learning Networks Approach,” US Patent 62,887,167, 2019.
P7. Y. Lin, Y. Wang, D. Shi, X. Wang, S. Wang, and Z. Wang, “Global Sensitivity Analysis in Load Modeling via Low-rank Tensor,” US Patent 62,888,967, 2019.
P8. Y. Wang, Q. Chang, X. Zhao, D. Shi, and Z. Wang, “Probabilistic Load Forecasting via Point Forecast Feature Integration,” US Patent 16,391,992, 2019.
P9. Z. Yu, Y. Wang, D. Shi, and Z. Wang, “Machine Learning in Oscillation Classification and Forced Oscillation Source Locating,” US Patent 62,891,756, 2020.
P10. Y. Wang, X. Wang, D. Shi, H. Li, C. Xu, Y. Lin, S. Wang, Z. Wang, “Systems and Methods of Composite Load Modeling for Electric Power Systems”, US Patent 16,994,512, 2020.
P11. Z. Yu, Y. Wang, X. Lu, C. Xu, D. Shi, “Power System Low-Frequency Oscillation Mechanism Identification with CNN and Transfer Learning”, US Patent 17,065,627, 2020.