Experience

 
 
 
 
 
January 2021 – Present
Beijing, China

Principal Research Scientist

SGRI (Previously known as GEIRI)

Institute of Computing and Applications

Previously as Technical Leader

Working on the following topics:

  • AI and optimization applications
  • Advanced power system computing
 
 
 
 
 
January 2019 – October 2019
Palo Alto, CA, USA

Visiting Scholar

Stanford University

Jointly with Precourt Institute for Energy and Department of Management Science and Engineering

Hosted by Prof. Yinyu Ye

 
 
 
 
 
June 2017 – December 2020
San Jose, CA, USA

Senior Research Engineer

GEIRI North America

AI & System Analytics Group

Previously as Postdoctoral Researcher and Power System Research Engineer

Working on the following topics:

  • Machine learning and PMU based load modeling
  • AI-based oscillation detection and control
  • Commercial hybrid AC/DC microgrid planning and operation
 
 
 
 
 
June 2015 – September 2015
Lemont, IL, USA

Research Aide

Argonne National Laboratory

Energy Systems Division

Worked with Dr. Audun Botterud, Dr. Zhi Zhou and Dr. Cong Liu on renewable integration with stochastic unit commitment

 
 
 
 
 
June 2014 – September 2014
Cambridge, MA, USA

Research Internship

Mitsubishi Electric Research Laboratories

Data Analytics Group

Worked with Dr. Hongbo Sun on distribution system reconfiguration with energy storage

 
 
 
 
 
September 2011 – June 2017
Seattle, WA, USA

Research Assistant

Department of Electrical Engineering, University of Washington

Renewable Energy Analysis Lab (REAL LAb)

Worked with Prof. Daniel Kirschen on the following topics:

  • Energy storage operation and planning in electricity markets
  • Renewable integration with stochastic unit commitment
  • Scenario generation and reduction
  • Energy forecasting

Updates

More Posts

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Recent Publications

More Publications

. A Review of Deep Reinforcement Learning Applications in Power System Parameter Estimation. IEEE PowerCon, 2021.

PDF

. Tensor-Based Parameter Reduction of Dynamic Load Models with Variable Frequency Drive. IEEE Transactions on Power Systems, 2021.

PDF Project

. Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network. MPCE, 2021.

PDF Project

. Real-time Energy Disaggregation at Substations with Behind-the-Meter Solar Generation. IEEE Transactions on Power Systems, 2020.

PDF Project

. Imitation and Transfer Q-learning-Based Parameter Identification for Composite Load Modeling. IEEE Transactions on Smart Grid, 2020.

PDF Project

. Mathematical representation of the WECC composite load model. MPCE, 2020.

Preprint PDF Project

. Evaluating Load Models and Their Impacts on Power Transfer Limits. IEEE ICCA, 2020.

Preprint Project

. Data-Driven Low Frequency Oscillation Mode Identification and Preventive Control Strategy Based on Gradient Descent. Electric Power System Research, 2020.

PDF Project

. Using Transfer Learning to Distinguish between Natural and Forced Oscillations. IEEE PESGM, 2020.

Project

. Self-organizing Probability Neural Network Based Intelligent Non-Intrusive Load Monitoring with Applications to Low-cost Residential Measuring Devices. Transactions of the Institute of Measurement and Control, 2020.

PDF Project

Patents

[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.

Contact

  • Beijing, China