Yujian Ye

Professor

Southeast University

Education Background

Educational Background (in reverse chronological order)：

  • 2013–2017: Imperial College London, Electrical and Electronic Engineering, Ph.D. (Full-time), President’s Scholarship (Full Funding)
  • 2011–2012: Imperial College London, Control Systems, M.Sc. (Full-time), Award for Outstanding Academic Contribution
  • 2009–2011: Northumbria University, Electrical and Electronic Engineering, B.Eng. (Full-time), IET Award for Outstanding Academic Achievement
  • 2007–2009: Nanjing Normal University, Electrical and Electronic Engineering, B.Eng. (Full-time)

Work Experience (in reverse chronological order)：

  • 2025–Present: Shenzhen Loop Area Institute, Jointly Appointed Professor and Ph.D. Supervisor
  • 2024–Present: Beijing Zhongguancun Academy, Jointly Appointed Professor and Ph.D. Supervisor
  • 2023–Present: Southeast University, Young Chair Professor and Ph.D. Supervisor
  • 2022–Present: Imperial College London, Honorary Lecturer
  • 2021–2022: Southeast University, Associate Professor and Ph.D. Supervisor
  • 2019–2020: Fetch.ai (UK), Machine Learning Scientist
  • 2017–2019: Imperial College Consulting, Independent Consulting Advisor
  • 2016–2020: Imperial College London, Research Fellow
Research Field
Reinforcement Learning, Swarm Intelligence, Intelligent Decision-Making, Evolutionary Computation, and Game Theory，Intelligent Scientific Computing for Next-Generation Power Systems, AI4Energy, AI-enabled Electrified Transportation, and Power–Computing Co
Email
yujianye@slai.edu.cn
Biography

Yujian Ye, Ph.D., is a recipient of China’s National High-Level Talent Program and the New Era Young Pioneer Award, an IET Fellow, Young Chair Professor at Southeast University, and a Huawei Zijin Young Scholar. He serves as a jointly appointed Professor and Ph.D. supervisor at the National Academy of Artificial Intelligence (Shenzhen Loop Area Institute and Beijing Zhongguancun Academy). He received his Ph.D. from Imperial College London (ICL) with the President’s Scholarship and is also an Honorary Lecturer at ICL. His research focuses on intelligent scientific computing for next-generation power systems. He has led seven national-level research projects, including those funded by the National Natural Science Foundation of China, the National Key R&D Program, and innovation joint projects of central state-owned enterprises. He has published over 40 papers in top-tier journals such as Nature sub-journals, Proceedings of the IEEE, and IEEE Transactions, with a cumulative impact factor exceeding 500. He currently serves as an Associate Editor for leading CAS Q1 journals, including IEEE Transactions on Smart Grid (TSG), IEEE Power Engineering Letters (PESL), IEEE Transactions on Industrial Informatics (TII), and Applied Energy (APEN). Over the past three years, he has received multiple honors, including the China Electric Power Outstanding Young Talent Award, the Wu Wenjun Artificial Intelligence Outstanding Young Scholar Award, and the Huawei Spark Award for Grand Challenge Problems. Under his supervision, students have won numerous prestigious awards, such as the Grand Prize (Special Award) in the Challenge Cup “Unveiling the List” Competition, the Grand Prize (Special Award) in the Challenge Cup “AI+” Application Competition, the National Gold Award in the China International College Students’ Innovation Competition (Main Track) and the Grand Prize (Special Award) in the National Finals of the AI Application Scenario Innovation Challenge. Research collaborations in the field of artificial intelligence and power & energy systems, as well as applications from outstanding students, are warmly welcomed. The research group can provide joint training opportunities at world-class universities, including ICL.

Academic Publications

代表性论文（近5-10年）：

  1. 叶宇剑，吴奕之，胡健雄，等．城市电力-交通耦合系统的联合推演与协同优化：研究综述、挑战与展望[J]．*中国电机工程学报*，2025，45(11)：4144-4163．
  2. 叶宇剑，吴奕之，胡健雄，等．市场环境下智能配用电系统分层协同优化运行：研究挑战、进展与展望[J]．中国电机工程学报，2024，44(06)：2078-2097．
  3. 叶宇剑，袁泉，刘文雯，等．基于参数共享机制多智能体深度强化学习的社区能量管理协同优化[J]．中国电机工程学报，2022，42(21)：7682-7695．
  4. 叶宇剑，袁泉, 汤奕，等. 抑制柔性负荷过响应的微网分散式调控参数优化[J]．中国电机工程学报，2022，42(05)：1748-1760．
  5. 吴奕之，*叶宇剑 (通讯作者)*，胡健雄，等．弥合配电系统恢复调度仿真现实间隙的两阶段数据机理融合优化架[J/OL] ．中国电机工程学报．
  6. 黄麒霖，*叶宇剑 (通讯作者)*，王睿，等．面向社会福利最大化的城市电动汽车充电设施自适应动态规划策略[J/OL] ．*中国电机工程学报*．
  7. 叶宇剑，王卉宇，汤奕，等．基于深度强化学习的居民实时自治最优能量管理策略[J]．电力系统自动化，2022，46(01)：110-119．
  8. 叶宇剑，王卉宇，刘曦木，等．电-碳耦合市场环境下可再生能源投资规划优化方法[J]．电力系统自动化，2023，47(23)：92-104.
  9. Y. Ye, X. Guo, et. al, “Advancing Privacy-Preserving Wind Generation Forecasts with Selective Spatial-Temporal Dependencies Extraction, Encryption and Sharing,” IEEE Transactions on Smart Grid, vol. 16, no. 4, pp. 3070-3084, July 2025.
  10. Y. Ye, Y. Tang, et. al, “Multi-agent Deep Reinforcement Learning for Coordinated Energy Trading and Ancillary Services Provision in Local Electricity Markets,” IEEE Transactions on Smart Grid, vol. 14, no. 2, pp. 1541-1554, Mar. 2023.
  11. Y. Ye, H. Wang, et. al, “Safe Deep Reinforcement Learning for Microgrid Energy Management in Distribution Networks with Leveraged Spatial-Temporal Perception,” IEEE Transactions on Smart Grid, vol. 14, no. 5, pp. 3759-3775, Sep. 2023.
  12. Y. Ye, H. Wang, et. al, “Identifying Generalizable Equilibrium Pricing Strategies for Charging Service Providers in Coupled Power and Transportation Networks,” Advances in Applied Energy, vol. 12, p. 100151, Sep. 2023.
  13. Y. Ye, Y. Tang, et. al, “A Scalable Privacy-Preserving Multi-agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading,” IEEE Transactions on Smart Grid, vol. 12, no. 6, pp. 5185-5200, Nov. 2021.
  14. Y. Ye, D. Qiu, et. al, “Deep Reinforcement Learning for Strategic Bidding in Electricity Markets,” IEEE Transactions on Smart Gird, vol. 11, no. 2, pp. 1343-1355, Mar. 2020.
  15. Y. Ye, D. Qiu, et. al, “Model-Free Real-Time Autonomous Control for a Residential Multi-Energy System Using Deep Reinforcement Learning,” IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3068-3082, Jul. 2021.
  16. Y. Ye, D. Papadaskalopoulos, et. al, “Incorporating Non-Convex Operating Characteristics into Bi-Level Optimization Electricity Market Models,” IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 163-176, Jan. 2020.
  17. Y. Ye, D. Papadaskalopoulos, et. al, “Investigating the ability of demand shifting to mitigate electricity producers’ market power”, IEEE Transactions on Power Systems, vol. 33, no. 4, pp. 3800-3811, Jul. 2018.
  18. Y. Ye, D. Papadaskalopoulos, et. al, “Factoring flexible demand non-convexities in electricity markets,” IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 2090-2099, Jul. 2015.
  19. Y. Ye, Y. Wu, J. Hu, et. al, “Physics-Guided Safe Policy Learning with Enhanced Perception for Real-Time Dynamic Security Constrained Optimal Power Flow”, Journal of Modern Power Systems and Clean Energy, vol. 13, no. 6, pp. 1507-1519, Sep 2025.
  20. Y. Ye, D. Ma et. al, “Harvesting Spatial-Temporal Load Migration Flexibility of Data Centers: A Chance-Constrained Bi-Level Optimization Model with Endogenously Formed Risk-Reflective Locational Prices,” Applied Energy, vol. 402, Part B, p. 126971, Jan. 2026.
  21. Z. Zhu, S. Bu, KW. Chan, F. Li, Y. Ye (通讯作者), et. al, “Designing the future electricity spot market with high renewables via reliable simulations,” Nature Review Electrical Engineering, vol. 2, pp. 320-337, 2025.
  22. F. Bellizio, W. Xu, D. Qiu, Y. Ye (通讯作者), et. al, “Transition to digitalised paradigms for security control and decentralised electricity market,” Proceedings of the IEEE, vol. 111, no. 7, pp. 744-761, July 2023.
  23. X. Liu, *Y. Ye (通讯作者)*, et. al, “Towards System-Wide Satisfaction of Emission and Security Constraints for Decentralized Coordination of Carbon-Aware Virtual Power Plants in Distribution Network,” *IEEE Transactions on Smart Grid*, early access.
  24. X. Chen, *Y. Ye (通讯作者)*, et. al, “Knowledge Transferred DRL-Based Adversary for Cyberattacks on Active Distribution Network Volt-Var Control Agents: When and How,” *IEEE Transactions on Cybernetics*, early access.
  25. W.-J. Lee, Y. Ye (通讯作者), et. al, “Special Section on Integrated Operation, Planning, and Business Paradigm for Coupled Energy, Transportation, and Information Networks,” IEEE Transactions on Smart Grid, vol. 16, no. 1, pp. 455-462, Jan. 2025.
  26. Q. Ma, Y. Ye (通讯作者), et. al, “Carbon Cap Based Multi-Energy Sharing among Heterogeneous Microgrids Using Multi-Agent Safe Reinforcement Learning Method with Credit Assignment and Sequential Update,” Applied Energy, vol. 393, p. 126018, Sep. 2025.
  27. H. Liu, Y. Ye (通讯作者), et. al, “Spatiotemporal Coordination of Electric Vehicle Traffic and Energy Flows in Coupled Power-Transportation Networks with Multiple Energy Replenishment and Vehicle-to-Grid Strategies”, Applied Energy, vol. 396, p. 126291, Oct. 2025.
  28. W. Lv, Y. Ye (通讯作者), et. al, “Sustainable Electrified Seaports: A Coordinated Energy and Logistics Scheduling Approach for Future Maritime Hubs”, Applied Energy, vol. 401, Part A, p. 126645, Dec. 2025.
  29. X. Liu, Y. Ye (通讯作者), et. al, “Network-Constrained P2P Trading: A Safety-Aware Decentralized Multi-Agent Reinforcement Learning Approach,” IEEE Transactions on Smart Grid, vol. 16, no. 5, pp. 5573-5588, Nov. 2025.
  30. Q. Ma, Z. Liu, Y. Ye (通讯作者), et. al, “Carbon-Aware Peer-to-Peer Energy Trading in An Unbalanced Distribution Network via A Nash Equilibrium Discovery Deep Reinforcement Learning Approach,” IEEE Transactions on Smart Grid, vol. 16, no. 4, pp. 3070-3084, July 2025.
  31. X. Liu, Y. Ye (通讯作者), et. al, “Multi-Stage Day-Ahead and Intra-Day Resource Scheduling and Market Bidding Strategy for Integrated PV-ESS-EV Station under Multiple Uncertainties,” International Journal of Electrical Power and Energy Systems, early access.
  32. T. Cui, Y. Ye (通讯作者), et. al, “Toward Profitable Energy Futures Trading Strategies Using Reinforcement Learning Incorporating Disagreement and Connectedness Methods Enabled by Large Language Models,” Energy and AI, vol. 21, p. 100562, Sep. 2025.
  33. X. Guo, Y. Ye (通讯作者), et. al, “Leveraging Extranet Computation Security for Collaborative Wind Generation Forecasting via Secure Multiparty Computation,” CSEE Journal of Power and Energy Systems, early access.
  34. Y. Wu, Y. Ye (通讯作者), et. al, “Chance Constrained MDP Formulation and Bayesian Advantage Policy Optimization for Stochastic Dynamic Optimal Power Flow”, IEEE Transactions on Power Systems, vol. 39, no. 5, pp. 6788-6791, Sep. 2024.
  35. J. Hu, Y. Ye (通讯作者), et. al, “Rethinking Safe Policy Learning for Complex Constraints Satisfaction: A Glimpse in Real-Time Security Constrained Economic Dispatch Integrating Energy Storage Units”, IEEE Transactions on Power Systems, vol. 40, no. 1, pp. 1091-1104, Jan. 2025.
  36. J. Hu, Y. Ye (通讯作者), et. al, “Towards Risk-Aware Real-Time Security Constrained Economic Dispatch: A Tailored Deep Reinforcement Learning Approach”, IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 3972-3986, Mar. 2024.
  37. H. Cui, Y. Ye (通讯作者), et. al, “Online Preventive Control for Transmission Overload Relief Using Safe Reinforcement Learning with Enhanced Spatial-Temporal Awareness,” IEEE Transactions on Power Systems, vol. 39, no. 1, pp. 517-532, Dec. 2023.
  38. H. Wang, Y. Ye (通讯作者), et. al, “An Efficient LP-based Approach for Spatial-Temporal Coordination of Electric Vehicles in Electricity-Transportation Nexus,” IEEE Transactions on Power Systems, vol. 38, no. 3, pp. 2914-2925, May 2023.
  39. J. Li, Y. Ye (通讯作者), et. al, “Distributed Consensus-Based Coordination of Flexible Demand and Energy Storage Resources,” IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 3053-3069, Jul. 2021.
  40. J. Li, Y. Ye (通讯作者), et. al, “Computationally Efficient Pricing and Benefit Distribution Mechanisms for Incentivizing Stable Peer-to-Peer Energy Trading,” IEEE Internet of Things Journal, vol. 8, no. 2, pp. 734-749, Jan. 2021.
  41. Q. Yuan, Y. Ye (通讯作者), et al, “A Novel Deep-Learning based Surrogate Modeling of Stochastic Electric Vehicle Traffic User Equilibrium in Low-Carbon Electricity-Transportation Nexus,” Applied Energy, vol. 315, p. 118961, Jun. 2022.
  42. Q. Yuan, Y. Ye (通讯作者), et al, “Low Carbon Electric Vehicle Charging Coordination in Coupled Transportation and Power Networks,” IEEE Transactions on Industry Applications, vol. 59, no. 2, pp. 2162-2172, Mar./Apr. 2023.
  43. P. Chen. Y. Ye (通讯作者), et al, “Holistic Coordination of Transactive Energy and Carbon Emission Right Trading for Heterogenous Networked Multi-Energy Microgrids: A Fully Distributed Adaptive Consensus ADMM Approach,” *Sustainable Energy Technologies and Assessments*, vol. 64, p. 103729, Apr. 2024.
  44. Y. Zhang, W. Qian, Y. Ye (通讯作者), et al, “A novel non-intrusive load monitoring method based on ResNet-seq2seq networks for energy disaggregation of distributed energy resources integrated with residential houses,” Applied Energy, vol. 349, p. 121703, Aug. 2023.
  45. X. Zhang, Z. Dong, F. Huangfu, Y. Ye (通讯作者), et al, “Strategic dispatch of electric buses for resilience enhancement of urban energy systems,” Applied Energy, vol. 361, p. 122897, May 2024.
  46. Y. Wu, J. Feng, X. Chen, *Y. Ye (通讯作者)*, et al, “Enhancing Power Grid Resilience Through Weather-Aware Security Constraints: A Deep Reinforcement Learning Approach with Hybrid CNN-GRU Architecture,” *Applied Energy*, early access.

著作/编著：

  1. Y. Ye; Modelling and Analysing the Market Integration of Flexible Demand and Storage Resources; Nanjing: Southeast University Press & Springer, Aug. 2022