叶宇剑

个人简介：

叶宇剑，国家高层次人才（青年），东南大学青年首席教授、博士生导师、紫金青年学者（电气工程学院首个），北京中关村学院博士生导师（东南大学首批），伦敦帝国理工学院荣誉讲师、校长奖学金（全额）博士，英国皇家特许工程师（Charted Engineer，CEng），IEEE系统、人与控制论协会（IEEE SMC）南京分会主席，XX-东南大学宏微观一体化仿真创新实验室副主任，伦敦帝国理工学院华东校友会理事（分管智能电网方向）。IEEE、中国电机工程学会、中国电工技术学会、中国人工智能学会、中国自动化学会、中国计算机学会、亚太人工智能学会高级会员。

现主持国家自然科学基金项目3项（XX、面上、青年）、江苏省自然科学基金青年项目1项、CCF-腾讯犀牛鸟基金项目1项、国网总部科技项目、省公司科技项目等10余项，参与国家自然科学基金国际（地区）合作与交流项目1项。曾作为伦敦帝国理工学院骨干，参与欧盟委员会“地平线2020”框架项目等国际项目10余项，总投资额逾 4000 万英镑。近年来获中国电力优秀青年科技人才奖、IEEE PES中国专业分会联合会优秀青年工程师奖、中国发明协会创业奖创新奖一等奖（排名第1），吴文俊人工智能优秀青年奖，中国能源研究会优秀青年能源科技工作者奖，XX挑战难题“价值火花奖”，东南大学青年五四奖章、优秀班主任标兵、优秀本科生导师、优秀本科学优生导师等荣誉。

近5年作为第一/通讯作者在Proceedings of the IEEE、IEEE P&E Magazine、IEEE Transactions等国际权威期刊上发表中科院一区Top SCI 论文25余篇，累积影响因子超过280，4篇入选ESI高被引，发表2篇CCF A类人工智能会议论文；论文入选中信所中国精品科技期刊顶尖学术论文（F5000）、中国科协科技期刊双语传播工程、IEEE PESGM最佳会议论文、中国工程院工程科技学术研讨会优秀论文，授权国家发明专利11项、国际（美国）专利2项。担任IEEE Trans. Smart Grid、IEEE Power Engineering Letters、IEEE Trans. Industrial Informatics、Applied Energy、IEEE Trans. Industry Applications等多个期刊编委；IET Smart Grid“Emerging Smart Grid Technologies”主题编辑；电力系统保护与控制、中国电力等青年编委；中国电机工程学报、IEEE Trans. Smart Grid等专题编委。担任中国人工智能学会智能自适应协同优化控制专业委员会委员、中国自动化学会能源互联网专业委员会、智能分布式能源专委会委员。

代表性论著：

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

2. 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/OL]．中国电机工程学报，1-20．http://kns.cnki.net/kcms/detail/ 11.2107.TM.20241223.1243.014.html.

3. 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．

4. 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．

5. 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．

6. 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．

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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, early access.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

27.  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.

28. 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.