Linwei Sang 桑林卫

• 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.

I am working on leveraging the machine learning and optimization techniques to solve the power system problems.

Energy System Topics

1. Explore and Eploit of Energy System Flexbility 

2. Power System Operation and Control 

3. Energy Management through the Low-carbon Lens

Machine learning and Optimization Techniques

1. Semi-end-to-end Models

2. Optimization with Constraint Learning

3. Learn-to-Optimize Methods

4. Structured Neural Networks, e.g., ICNN, monotone neural networks

5. Multi-armed Bandits Methods

6. Safe and stable reinforcement learning algorithms

[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.