Linwei Sang 桑林卫
Phd. Candidate in Tsinghua University
E-mail: sanglw21@mails.tsinghua.edu.cn
I recieved the B.S and M.S degree from Southeast University, Nangjing, China with honors in 2018, 2021 respectively. I’m current pursing my Ph.d degree advised by Prof. Yinliang Xu in Tsinghua University, which belongs to EMS group head by Prof. Hongbin Sun.
I have been supporting FC Bayern Munich since 2011.
Recent News
I start my journey in University of California, Berkeley advised by Prof. Oren in May 4th, 2023.
Our paper “Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach,” was accepted to IEEE Transactions on Sustain. Energy, May, 2023.
Our paper “Conservative Sparse Neural Network Embedded Frequency-Constrained Unit Commitment With Distributed Energy Resources” was accepted to IEEE Trans. on Sustain. Energy, Apr. 2023.
Research Interest
I am working on leveraging the machine learning and optimization techniques to solve the power system problems.
Energy System Topics
Explore and Eploit of Energy System Flexbility
Power System Operation and Control
Energy Management through the Low-carbon Lens
Machine learning and Optimization Techniques
Semi-end-to-end Models
Optimization with Constraint Learning
Learn-to-Optimize Methods
Structured Neural Networks, e.g., ICNN, monotone neural networks
Multi-armed Bandits Methods
Safe and stable reinforcement learning algorithms
Publication
[J10] C. Huang, Q. Hu, L. Sang, et al. “A Review of Wildfire Mitigation Plans in Power Systems: Datasets, Model, and Industry Practice,” IEEE Transactions on Energy Markets, Policy and Regulation, Early Access, 2023.
[J9] L. Sang, Y. Xu, and H. Sun, “Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach,” IEEE Transactions on Sustainable Energy, Early Access, 2023.
[J8] L. Sang, Y. Xu, Z. Yi, H. Long, and H. Sun, “Conservative Sparse Neural Network Embedded Frequency Constrained Unit Commitment With Distributed Energy Resources,” IEEE Transactions on Sustainable Energy, Early Access, 2023.
[J7] Y. Wang, Z. Yan, L. Sang, et al. “Acceleration Framework and Solution Algorithm for Distribution System Restoration based on End-to-End Optimization Strategy,” IEEE Transactions on Power Systems, Early Access, 2023.
[J6] L. Sang, Y. Xu, and H. Sun, “Ensemble Provably Robust Learn-to-optimize Approach for Security-Constrained Unit Commitment, ” IEEE Transactions on Power Systems, Early Access, 2022.
[J5] L. Sang, Y. Xu, H. Long, and W. Wu, “Safety-aware Semi-end-to-end Coordinated Decision Model for Voltage Regulation in Active Distribution Network, ” IEEE Transactions on Smart Grid, vol. 14, no. 3, pp. 1814-1826, May 2023.
[J4] L. Sang, Y. Xu, H. Long, Q. Hu, and H. Sun, “Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach,” IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 2822-2832, July 2022.
[J3] L. Sang, Q. Hu, Y. Xu, and Z. Wu, “Privacy-preserving Hybrid Cloud Framework for Real-time TCL-based Demand Response,” IEEE Transactions on Cloud Computing, , vol. 11, no. 2, pp. 1182-1193, 1 April-June 2023.
[J2] Z. Yi, Y. Xu, H. Wang, and L. Sang, “Coordinated Operation Strategy for a Virtual Power Plant With Multiple DER Aggregators,” IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2445-2458, Oct. 2021
[J1] H. Long, L. Sang, Z. Wu, and W. Gu, “Image-Based Abnormal Data Detection and Cleaning Algorithm via Wind Power Curve,” IEEE Transactions on Sustainable Energy, vol. 11, no. 2, pp. 938-946, Apr. 2020.
[C2] L. Sang, Y. Xu, W. Chan, and Z. Wei. “Carbon-aware Integrated Energy System Operation with Demand Response”, Accepted by CIECC, 2022, Oral speech, Best Paper Award.
[C1] L. Sang, Q. Hu, Y. Zhao, R. Han, Z. Wu, and X. Dou, “A Scenario-adaptive Online Learning Algorithm for Demand Response,” 2020 IEEE Power Energy & Society General Meeting (PESGM), 2020, pp. 1-5.