Yang Li

Yang Li, Ph.D. Professor School of Electrical Engineering, Northeast Electric Power University (NEEPU)

Contact

Room 525, Power and Energy Building, Northeast Electric Power University, Jilin, China
Tel: (86) 159-4796-6691
Email: liyang@neepu.edu.cn
Chinese Homepage: http:ee.neepu.edu.cninfo1204/3846.htm

Research Interests

My research interests include

• Wind and solar dispatch

• Distributed generation planning and control

• Uncertainty modeling and analysis

• Energy storage/EV integration

• Optimization of integrated energy systems

• Integrated demand response

• AI-driven power system analysis

• Federated Learning

Find me on: Google Scholar Profile, ORCiD, ResearchGate, and Publons.

News & Updates

• 10/2023

1. Dr. Li ranked among the “World's Top 2% Scientists” (Stanford University) for both Single Year and Whole Career.

2. Paper accepted in IEEE Transactions on Control Systems Technology: “A Demand-Supply Cooperative Responding Strategy in Power System with High Renewable Energy Penetration”

3. Paper accepted in Renewable and Sustainable Energy Reviews: “Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance”

4. Paper accepted in IET Generation, Transmission & Distribution: “A novel method of restoration path optimization for the AC-DC bulk power grid after a major blackout”. (Coming soon on arXiv)

5. Patent granted by China National Intellectual Property Administration: “Optimal scheduling method of community-integrated energy systems considering integrated demand response under uncertain environments.”

• 9/2023

1. “Detection of false data injection attacks in smart grid: A secure federated deep learning approach” recognized as “Popular Documents” in IEEE Transactions on Smart Grid.

2. “Coordinating flexible demand response and renewable uncertainties for scheduling of community integrated energy systems with an electric vehicle charging station: A bi-level approach” recognized as “Popular Documents” in IEEE Transactions on Sustainable Energy.

3. Paper accepted in IEEE Transactions on Sustainable Energy: “Collaborative response of data center coupled with hydrogen storage system for renewable energy absorption.”

4. Paper accepted in Electric Power Construction: “Low-carbon optimal dispatch of integrated energy system considering demand response under the tiered carbon trading mechanism.”

• 8/2023

1. “Optimal scheduling of isolated microgrids using automated reinforcement learning-based multi-period forecasting” recognized as “Popular Documents” in IEEE Transactions on Sustainable Energy.

2. Paper accepted in IEEE Transactions on Instrumentation & Measurement: “PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning.”

3. Paper accepted in Electric Power Systems Research: “Model predictive control strategy in waked wind farms for optimal fatigue loads.”

4. Editorial accepted in Energy Reports: “Guest Editorial: Special Issue on Innovative Methods and Techniques for Power and Energy Systems with High Penetration of Distributed Energy Resources.” (Coming soon in publication)

5. Paper accepted in Electric Power Construction: “Deep reinforcement learning-driven cross-community energy interaction optimal scheduling.”

• 7/2023

1. Paper accepted in High Voltage Engineering: “Dual-layer model for capacity optimization of hybrid energy storage system to reduce thermal power frequency modulation loss.” (Coming soon in publication)

2. Paper accepted in Electric Power Automation Equipment: “Short-term power load forecasting method based on CNN-SAEDN-Res.”

3. Paper accepted in Acta energiae solaris sinica: “Secondary frequency control of islanded microgrid considering wind and solar stochastics.”

• 6/2023

1. Paper accepted in IEEE Transactions on Power Electronics: “Output voltage response improvement and ripple reduction control for input-parallel output-parallel high-power DC supply.”

2. Paper accepted in IEEE Transactions on Network Science and Engineering: “Renewable energy absorption oriented many-objective probabilistic optimal power flow.”

3. Paper accepted in Energy: “Collaborative optimization of multi-microgrids system with shared energy storage based on multi-agent stochastic game and reinforcement learning.”

4. Editorial accepted in Frontiers Energy Research: “Editorial: Rising stars in energy research: 2022.”

• 4/2023

1. Dr. Li recognized in the 2022 Clean Energy Reviewer Awards by Clean Energy journal.

2. Paper accepted in International Journal of Bio-Inspired Computation: “Image encryption for offshore wind power based on 2D-LCLM and Zhou Yi Eight Trigrams.”

3. Paper accepted in Energies: “Multi-microgrid collaborative optimization scheduling using an improved multi-agent soft actor-critic algorithm.”

• 3/2023

1. Dr. Li named on the Highly Cited Chinese Researchers list of 2022 by Elsevier.

2. Paper published in .Applied Energy.: “Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach.”

• 2/2023

1. Paper accepted in Engineering Applications of Artificial Intelligence: “Demand response method considering multiple types of flexible loads in industrial parks.”

2. Paper accepted in Scientific Reports: “A new method for axis adjustment of the hydro-generator unit using machine learning.”

• 1/2023

1. Paper accepted in Applied Energy: “Data-Driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response.””

2. Paper published in IEEE Transactions on Neural Networks and Learning Systems: “Federated multiagent deep reinforcement learning approach via physics-informed reward for multimicrogrid energy management.”

3. Paper published in IEEE Transactions on Intelligent Vehicles: “Probabilistic charging power forecast of EVCS: Reinforcement learning assisted deep learning approach.”

4. Paper published in Applied Energy: “Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach.”