叶宇剑 (Yujian Ye)

Southeast University邮箱:yeyujian@seu.edu.cn

H指数:28

被引数:3,053

个人简介

"叶宇剑，国家级人才，新时代青年先锋奖获得者，东南大学青年首席教授、博士生导师、****青年学者（电气工程学院首个），北京中关村学院（国家人工智能学院）兼聘博士生导师（全国首批），深圳河套学院（国家人工智能学院）兼聘教授（全国首批），伦敦帝国理工学院荣誉讲师、校长奖学金（全额）博士。担任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，第一届南京市青年科技工作者协会副秘书长、能源环境专委会秘书长，HW-东南大学宏微观一体化仿真创新实验室副主任，伦敦帝国理工学院南京校友会会长、华东校友会理事。IEEE、中国计算机学会、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、中国公路学会、亚太人工智能学会高级会员，中英人工智能学会高级研究员。\n现主持国家自然科学基金项目4项、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（优秀结题并获腾讯公司滚动资助）、国网/南网总部科技项目、五大发电集团科技项目、HW、腾讯等校企合作横向课题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。\n入选斯坦福全球前2%顶尖科学家榜单，作为第一/通讯作者在中国电机工程学报等领域中文顶刊发表论文10余篇；在电气工程Nature子刊Nature Review Electrical Engineering、IEEE旗舰期刊Proceedings of the IEEE、IEEE P&E Magazine、IEEE TPWRS、TSG、TII、TCYB、APEN、ADAPEN等中科院一区Top期刊上发表高水平论文40余篇，累积影响因子破500，4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20余项、国际（美国）专利2项。\n担任IEEE Trans. Smart Grid（东南大学首个）、IEEE Power Engineering Letters、IEEE Trans. Industrial Informatics、Applied Energy、Protection and Control of Modern Power Systems等多个中科院一区Top期刊副主编；IET Smart Grid主题编辑（Subject Editor）；新型电力系统、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。\n近3年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，华为挑战难题“价值火花奖”，日内瓦国际发明展金奖、银奖各1项（排1），金砖国际发明展银奖1项（排1）、江苏省自动化学会科学技术一等奖（排2）；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（连续3年）等荣誉。\n带领本科生/研究生获得2025年全国人工智能应用场景创新挑战赛全国总决赛特等奖与一等奖（指导老师排1）、2024年“挑战杯”全国大学生课外学术科技作品竞赛“揭榜挂帅”专项挑战赛特等奖、2025年中国国际大学生创新大赛主赛道全国金奖、2025年“挑战杯”全国大学生课外学术科技作品竞赛“人工智能+”应用赛特等奖、2025年“挑战杯”全国大学生课外学术科技作品竞赛“揭榜挂帅”擂台赛一等奖、、2025年工信部新域新质创新大赛特等奖。\n代表性论著\nY. Ye; Modelling and Analysing the Market Integration of Flexible Demand and Storage Resources; Nanjing: Southeast University Press & Springer, Aug. 2022.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．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．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．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．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．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．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．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.Y. Ye, C. Zhang, et. al, “Coordinated and Generalizable Planning and Operation of PV-Storage-Charging Facilities in Coupled Power and Transportation Nexus,”IEEE Transactions on Smart Grid, early access.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.X. Chen,Y. Ye (通讯作者), et. al, “Knowledge Transferred DRL-Based Adversary for Cyberattac"