Research interests

Work on fusing learning and optimization to facilitate the sustainable, safe, and carbon-aware energy systems from the comprehensive perspectives of decision-focused prediction, constraint learning, and online optimization.

Prediction: Integrating Optimization into Learning

Related Papers:

• 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, Early Access, 2022. Arxiv
• 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. Arxiv

Scheduling: Integrating Learning into Optimization

Related Papers:

• 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. Arxiv
• L. Sang, Y. Xu, Z. Yi, L. Yang, 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. Arxiv

Online Optimization: Integrating Learning and Optimization

Related Papers:

• L. Sang, Y. Xu, W. Wu, and H. Long, “Online Voltage Regulation of Active Distribution Networks: A Deep Neural Encoding-Decoding Approach, ” IEEE Transactions on Power Systems, Early Access, 2023.
• 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.