叶宇剑

发布者：宋阳发布时间：2022-09-30浏览次数：15697

叶宇剑

职称：国家青年高层次人才、东南大学青年首席教授、博士生导师、东南大学紫金青年学者

研究方向:

  • 低碳城市能源-交通-信息互联网的建模、控制与分析

  • 人工智能在电力及能源领域的应用

  • 电力市场建模与分析

Email:yeyujian@seu.edu.cn

每年招收博士生2-3名，硕士生2-3名，博士后1名，

欢迎有志之士加入朝气蓬勃的团队！

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，东南大学紫金青年学者；伦敦帝国理工学院荣誉讲师、全额奖学金博士。IEEE、中国电机工程学会高级会员、中国电工技术学会、中国人工智能学会、亚太人工智能学会高级会员。现主持国家高层次人才（青年）项目、国家自然科学基金青年项目、江苏省自然科学基金青年项目、CCF-腾讯犀牛鸟基金项目、国网总部科技项目等10余项，作为研究骨干参与国家自然科学基金国际（地区）合作与交流项目1项。


作为第一/通讯作者在Proc. IEEE、IEEE TPWRS、IEEE TSG、Appl. Energy、IEEE IoT J.等国际电力、能源、物联网、自动化、人工智能等领域顶级期刊上发表中科院一区Top SCI论文20篇，累积影响因子超过220，3篇入选ESI高被引，发表2篇CCF A类人工智能会议论文，授权国家发明专利5项。担任IEEE TSG、IEEE TII、APEN、IEEE TIA等多个期刊副编辑，IET Smart Grid“Emerging Smart Grid Technologies”主题编辑，中国电机工程学报、IEEE TSG等特邀专题编委。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。


近3年代表作：

  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．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/OL]．中国电机工程学报，2023．

  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. 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．

  6. 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/OL]．电力系统自动化，2023．

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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, early access.

  15. 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, early access.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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．

  22. 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.


Baidu
map