叶宇剑

发布者:宋阳发布时间:2022-09-30浏览次数:6245

叶宇剑

职称:  上岗副研究员、博士生导师

研究方向: 电力市场的建模与分析、人工智能在电力及能源领域的应用、能源互联网的建模、优化与控制

Email: yeyujian@seu.edu.cn

办公电话: 13851918258


个人简介:

叶宇剑,博士,副研究员,博士生导师,IEEE Senior Member,多年来一直从事电力市场、智能电网及能源系统领域优化和智能决策等关键问题的研究,主要研究方向包括电力市场建模与分析人工智能在电力及能源系统的应用,能源互联网中的建模、优化与控制等国际前沿热点课题。主持国家自然科学基金(青年)、江苏省自然科学基金(青年)、2021年江苏省“双创博士”世界名校类人才项目和2022年江苏省科技副总项目各一项。


作为帝国理工学院科研团队的技术骨干,参与了多个由欧盟委员会、英国工程和自然科学研究委员会、欧洲地区发展基金会资助的多个科研项目,项目投资额超过4000万英镑,包括欧盟“地平线2020计划”下规模最大的能源项目EU-SysFlex2000万欧元)、100%新能源系统市场机制设计项目TradeRES400万英镑)、本地能源系统灵活性服务交易项目MERLON730万欧元)、多个英国工程和自然科学研究委员会(EPSRC)面上项目、中英/英韩/英印项目;欧洲区域发展基金资助的英国首个本地电力市场设计与试点项目Cornwall Local Energy Market1900万英镑)、中英自然科学基金重大合作项目(NSFC-RCUK规模化储能在未来低碳电力系统中的经济、规划、政策和商业模式研究(100万英镑)等。


2021.1至今

东南大学

副研究员

2022.3至今

伦敦帝国理工学院

荣誉讲师

2022.05-2024.04

南京天创电自技术有限公司

技术委员会专家

2021.03-2022.02

伦敦帝国理工学院

访问学者

2019.06-2020.08

英国Fetch.ai人工智能公司

机器学习科学家

2016.12-2020.12

伦敦帝国理工学院

博士后研究员

2017.05-2019.06

帝国理工咨询Imperial Consultant

独立咨询顾问

2012.12-2017.03

伦敦帝国理工学院

博士研究生

2011.09-2012.11

伦敦帝国理工学院

硕士研究生


自攻读博士学位以来,共发表60余篇学术论文以及1部英文专著、作为通讯作者IEEE旗舰期刊Proceedings of the IEEE上发表1篇论文、作为第一或通讯作者在中国电机工程学报、IEEE Trans. Power SystemsIEEE Trans. Smart Grid国内外高水平学术期刊上发表共26篇论文(其中中科院一区Top SCI 论文15),h因子17累积影响因子超过160,第一作者 IEEE Trans. 期刊论文单篇最高引用破百;共发表21EI 国际会议论文,其中1CCF A类会议IJCAI2020论文(被评选为人工智能技术在能源领域的开创性应用),1篇论文获IEEE PESGM 2017会议最佳论文奖;以第一发明人申请国家发明专利10项。部分研究成果收录于《电力系统经济性原理》2019年修订版的数个章节。


受邀担任10余个国内外学术组织会员和理事, 包括IEEE 高级会员(Senior Member、中国电机工程学会(CSEE)会员、英国工程技术学会(IET)会员、IEEE PES电力系统运行、规划与经济技术委员会(中国)- 电力市场技术分委会理事、IEEE PES智能电网与新技术委员会(中国) - 智能电网与人工智能分委会理事等。长期担任英国工程和自然科学研究委员会(EPSRC基金项目评审专家;


担任IEEE Transactions on Smart GridIEEE Transactions on Industry Applications等期刊编委; Applied Energy等期刊青年编委;中国电机工程学报等期刊专题编委。担任20余个国内外顶级学术期刊审稿人,2021年获电力系统自动化期刊优秀审稿专家2020年获IEEE Trans. Power Systems期刊杰出审稿专家、2017年获International Journal of Electrical Power and Energy Systems期刊杰出审稿专家


主持/参与的项目:

  1. 国家自然科学基金(青年)项目,可交易能源市场环境下配用电系统多异构主体分层自治协同优化方法,2023.012025.12,主持;

  2. 江苏省自然科学基金(青年)项目,2022.072025.06,主持;

  3.  2022年“江苏省科技副总”项目,主持;

  4.  2021年江苏省“双创博士”项目,主持;

  5. 东南大学“复杂工程系统测量与控制”教育部重点实验室2022年度开放课题,主持

  6. 国家电网江苏省电力有限公司业务研究项目,基于区域试点先行的分布式绿电现货运营机制研究,主持;

  7. 国家电网浙江省电力有限公司项目,电量统计知识图谱构建与人工智能应用技术研究,主持;

  8. 国家电网浙江省电力有限公司项目,基于统计学理论的电力知识图谱构建技术研究,主持;

  9. 南方电网深圳供电局有限公司项目,智能电网调度全景AI指挥平台关联技术研究与应用,参与;

  10. 英国工程和自然科学研究委员会,英韩联合项目, EP/N03466X/1Peer-to-peer energy trading and sharing - 3M (Multi-times, Multi-scales, Multi-qualities), 2016-092020-02, 98万英镑,结题,参与;

  11. 欧洲地区发展基金会,英国首个本地能量市场项目,Cornwall local energy market2017.092020.121900万英镑,结题,参与;

  12.  Innovate UK104249E-FLEX - Real-world Energy Flexibility through Electric Vehicle Energy Trading2018.092022.0237万英镑,结题,参与;

  13. 英国工程和自然科学研究委员会,中英联合项目,EP/T021780/1Technology Transformation to Support Flexible and Resilient Local Energy Systems2020-072023-0381万英镑,在研,参与;

  14. 欧盟“水平线2020计划”,欧盟委员会项目,773505EU-Sysflex (Pan-European system with an efficient coordinated use of flexibilities for the integration of a large share of RES)2017.112021.112000万欧元,结题,参与;

  15. 欧盟“水平线2020计划”,欧盟委员会项目,864276TradeRES – Tools for the Design and Modelling of New Markets and Negotiation Mechanisms for a 100% Renewable European Power System400万英镑,2020.022023.02,在研,参与;

  16. 欧盟“水平线2020计划”,欧盟委员会项目,824386MERLON – Integrated Modular Energy Systems and Local Flexibility Trading for Neural Energy Islands730万英镑,2019.012022.04,已结题,参与;

  17. 英国工程和自然科学研究委员会,面上项目,EP/R045518/1Integrated Development of Low-Carbon Energy Systems (IDLES): A Whole-System Paradigm for Creating a National Strategy2018-112023-10705万英镑,在研,参与;

  18. 英国工程和自然科学研究委员会,中英联合项目,EP/L014386/1Business, economics, planning and policy for energy storage in low-carbon futures, 2014-092017-09100万英镑,结题,参与;

  19. 英国工程和自然科学研究委员会,中英联合项目,EP/L001039/1Grid Economics, Planning and Business Models for Smart Electric Mobility2013-122016-12100万英镑,结题,参与;

  20. 英国工程和自然科学研究委员会,面上项目,EP/V012053/1The Active Building Centre Research Programme (ABC RP)2020-042022-09932万英镑,在研,参加。

  21.  Economic and Social Research Council,面上项目,ES/T000112/1Socio-Techno-Economic Pathways for Sustainable Urban Energy Development2019-052022-0430万英镑,在研,参与;

  22. 英国工程和自然科学研究委员会,面上项目,EP/R030235/1Resilient Electricity Networks for a productive Grid Architecture (RENGA)2018-052022-0498万英镑,在研,参加;

  23. 英国工程和自然科学研究委员会,面上项目,EP/L019469/1SUPERGEN Energy Storage Hub391万英镑,2014-062019-12,已结题,参与;

  24. 英国工程和自然科学研究委员会,面上项目,EP/K039326/1Whole Systems Energy Modelling Consortium (WholeSEM)2013-072018-01461万英镑,已结题,参与。



专著:

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



期刊论文(第一或通讯作者论文累积影响因子超过160)

  1. 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, early access.

  2. 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.

  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/OL]中国电机工程学报: 1-14. DOI:10.13334/j.0258-8013.pcsee.212707.

  4. Q. YuanY. 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, early access

  5. 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, early access.

  6. H. Cui, Q. Wang, Y. Ye (通讯作者), et. al, “A Combinational Transfer Learning Framework for Online Transient Stability Prediction,” Sustainable Energy, Grids and Networks, vol. 30, p. 100674, Jun. 2022.

  7. 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.

  8. 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202242(05)1748-1760

  9. 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202246(01)110-119

  10. 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. 2020.

  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.(单篇引用120

  12. 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.

  13. 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.

  14. 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, July. 2015.

  15. Y. Ye, H. Wang, et. al, “Market-based Hosting Capacity Maximization of Renewable Generation in Power Grids with Energy Storage Integration,” Frontiers in Energy Research, vol. 10, p. 933295, Aug. 2022.

  16. 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, early access

  17. 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.

  18. 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.

  19. D. Qiu, Y. Ye (通讯作者), et. al, “Scalable Coordinated Management of Peer-to-Peer Energy Trading: A Multi-Cluster Deep Reinforcement Learning Approach,” Applied Energy, vol. 292, p. 116940, Apr. 2021.

  20. D. Qiu, Y.Ye (通讯作者), et. al, “A Deep Reinforcement Learning Method for Pricing Electric Vehicles with Discrete Charging Levels,” IEEE Transactions on Industry Applications, vol. 56, no. 5, pp. 5901-5912, Sept.-Oct. 2020.

  21. J. Li, Y.Ye (通讯作者), et. al, “Stabilizing Peer-to-Peer Energy Trading in Prosumer Coalition Through Computational Efficient Pricing,” Electric Power Systems Research, vol. 189, p. 106764, Dec. 2020.

  22. D. Qiu, Y. Ye (通讯作者), et. al, “Exploring the Effects of Local Energy Markets on Electricity Retailers and Customers,” Electric Power Systems Research, vol. 189, p. 106761, Dec. 2020.

  23. H. Cui, Y. Ye (通讯作者), et. al, “Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning,” Frontiers in Energy Research, vol. 10, p. 933011, Jul. 2022.

  24. Y. Ye, D. Qiu, et. al, "Multi-period and Multi-spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning Approach," IEEE Access, vol. 7, pp. 130515-130529, Sep. 2019.

  25. 臧汉洲,叶宇剑 (通讯作者) .基于内点策略优化的受约束电动汽车充放电策略[J/OL].电网技术:1-12.

  26. Y. Ye, D. Papadaskalopoulos, et. al, "Investigating the Impacts of Price-Taking and Price-Making Energy Storage in Electricity Markets through an equilibrium programming model", IET Generation, Transmission and Distribution, vol. 3, no. 2, pp. 305-315, Jan. 2019.

  27. Y. Ye, D. Qiu, et.al, "Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning", Energies, vol. 14, no. 3, pp. 531, Jan. 2021.

  28. D. Qiu, D. Papadaskalopoulos, Y. Ye (通讯作者), et. al, “Investigating the Effects of Demand Flexibility on Electricity Retailers’ Business through a Tri-Level Optimization Model,” IET Generation, Transmission and Distribution, vol. 14, no. 9, pp. 1739-1750, May 2020.

  29. J. Hu, Q. Wang, Y. Ye, et. al, “Toward Online Power System Model Identification: A Deep Reinforcement Learning Approach,”IEEE Transactions on Power Systems, early access.

  30. Z. Liu, Q. Wang, Y. Ye, et. al, “A GAN Based Data Injection Attack Method on Data-Driven Strategies in Power Systems,”IEEE Transactions on Smart Gird, vol. 13, no. 4, pp. 3203-3213, Jul. 2022.

  31. W. Sun, Q. Wang, Y. Ye, et. al, “Unified Modelling of Gas and Thermal Inertia for Integrated Energy System and its Application to Multitype Reserve Procurement”, Applied Energy, vol. 305, p. 117963, Jan. 2022.

  32. H. Wang, Q. Wang, Y. Ye, et. al, “Spatial load migration in a power system: concept, potential and prospects,”International Journal of Electrical Power and Energy Systems, vol. 140, p. 107926, Sep. 2022.

  33. F. Shuang, J. Chen, Y. Ye, et.al, “A two-stage deep transfer learning for localisation of forced oscillations disturbance source”, International Journal of Electrical Power and Energy Systems, vol. 135, p. 107577, Feb. 2022.

  34. T. Oderinwale, D. Papadaskalopoulos, Y. Ye, et. al, “Investigating the Impact of Flexible Demand on Market-Based Generation Investment Planning,”International Journal of Electrical Power and Energy Systems, vol. 119, p. 105881, Jul. 2020.

  35. X. Han, C. Zhang, Y. Tang, Y. Ye “Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building,”Journal of Modern Power Systems and Clean Energy, vol. 10, no. 2, pp. 482-491, Mar. 2022.

  36. 冯双,崔昊,陈佳宁,叶宇剑,汤奕,雷家兴.基于自编码器信号压缩与LSTM的宽频振荡扰动源定位方法[J/OL].电力系统自动化:1-12.

  37. 崔昊,冯双,陈佳宁,叶宇剑,汤奕,雷家兴.基于自编码器与长短期记忆网络的宽频振荡广域定位方法[J].电力系统自动化,2022,46(12):194-201.

  38. M. Sun, Y. Wang, F. Teng, Y. Ye, et. al, “Clustering-Based Residential Baseline Estimation: A Probabilistic Perspective,” IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6014-6028, Nov. 2019.

  39. G. Strbac, D. Pudjianto, M. Aunedi, D. Papadaskalopoulos, P. Djapic, Y. Ye, et. al, "Cost-Effective Decarbonization in a Decentralized Market: The Benefits of Using Flexible Technologies and Resources," IEEE Power and Energy Magazine, vol. 17, no. 2, pp. 25-36, Feb. 2019.


会议论文:

  1. Y. Ye, D. Qiu, et. al, “Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach,” Proc. 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan, 11-17 July. 2020. (CCF A类、世界人工智能顶会, 2020年接收率为12.6%)

  2.  D. Papadaskalopoulos, Y. Ye, et. al, "Exploring the Role of Demand Shifting in Oligopolistic Electricity Markets", Proc. 2017 IEEE Power & Energy Society General Meeting (GM), Chicago, IL, USA, 16-20 July 2017. (会议最佳论文奖)

  3. Y. Ye, D. Qiu, et. al, “A Deep Q Network Approach for Optimizing Offering Strategies in Electricity Markets,” Proc. 2nd International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, Sep. 9-11, 2019.

  4. Y. Ye, D. Papadaskalopoulos, et. al, "Strategic capacity withholding by energy storage in electricity markets", Proc. PowerTech Conference 2017, Manchester, UK, Jun. 2017.

  5. Y. Ye, D. Papadaskalopoulos, et. al, "An MPEC approach for Analysing the Impact of Energy Storage in Imperfect Electricity Markets", Proc. 13th International Conference on the European Energy Market (EEM), Porto, Portugal, June 6-9, 2016.

  6. Y. Ye, D. Papadaskalopoulos, et. al, "Pricing Flexible Demand Non-convexities in Electricity Markets", Proc. 18th Power Systems Computation Conference (PSCC), Wroclaw, Poland, Aug. 18-22, 2014.

  7. P. Chen, Y. Ye, J. Hu, et. al, “Dynamic Modeling of Smart Buildings Energy Consumption: A Cyber-Physical Fusion Approach”, Proc. 2021IEEE Sustainable Power and Energy Conference (iSPEC), Nanjing, China, Nov. 25-27, 2021.

  8. H. Wang, Y. Ye, Y. Tang, “Towards Market-Based Integration of Renewable Generation in Power Grids”, Proc. 2021IEEE Sustainable Power and Energy Conference (iSPEC), Nanjing, China, Nov. 25-27, 2021.

  9. Q. Yuan, Y. Ye, Y. Tang, X. Liu and Q. Tian, “Optimal Load Scheduling in Coupled Power and Transportation Networks”, Proc. 2021 IEEE IAS Industrial & Commerical Power System Asia, Chengdu, China, July. 18-21, 2021.

  10. J. Li, Y.Ye, et. al, “Stabilizing Peer-to-Peer Energy Trading in Prosumer Coalition Through Computational Efficient Pricing,” Proc. 21st Power Systems Computation Conference (PSCC), Porto, Portugal, Jun. 29 - Jul. 3, 2020.

  11. D. Qiu, Y. Ye, et. al, “Exploring the Effects of Local Energy Markets on Electricity Retailers and Customers,” Proc. 21st Power Systems Computation Conference (PSCC), Porto, Portugal, Jun. 29 - Jul. 3, 2020.

  12. J. Li, Y. Ye, et. al, “Incentivizing Peer-to-Peer Energy Sharing Using a Core Tâtonnement Algorithm,” Proc. 2020 IEEE Power & Energy Society General Meeting (GM), Montreal, Canada, 2-6 Aug. 2020.

  13. Q. Yuan, Y. Ye, X. Liu, et. al, "Optimal Load Scheduling in Coupled Power and Transportation Networks," submitted to 2021 IEEE Power & Energy Society General Meeting (GM), under review.

  14. J. Li, Y. Ye, et. al, “Consensus-Based Coordination of Time-Shiftable Flexible Demand,” Proc. 2ndInternational Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, Sep. 9-11, 2019.

  15. T. Oderinwale, Y. Ye, et. al, “Impact of Energy Storage on Market-Based Generation Investment Planning,” Proc. PowerTech Conference 2019, Milano, Italy, Jun. 23-27, 2019.

  16. D. Papadaskalopoulos and Y. Ye, “Investigating the role of flexible demand and energy storage in the deregulated electricity market,” Proc. 4th Hellenic Association for Energy Economics (HAEE) Annual Symposium, “Energy Transition IV: SE Europe and beyond”, Athens, Greece, May 6-8, 2019.

  17. D. Papadaskalopoulos, Y. Ye, et. al, “A Bi-Level Optimization Modeling Framework for Investigating the Role of Flexible Demand in Deregulated Electricity Systems,” Proc. 19th International Conference on Environment and Electrical Engineering (19th IEEE EEEIC), Genoa, Italy, Jun. 11-14, 2019.

  18.  D.Qiu, Y. Ye, et. al, “Advanced Bi-level Optimization and Reinforcement Learning Approaches for Modelling Deregulated Electricity Markets,” Proc. 2020 INFORMS Annual Meeting, Washington DC, USA, Nov. 11-14, 2020.

  19.  G.Takis-Defteraios,D. Papadaskalopoulos, Y. Ye, et. al, “Role of Flexible Demand in Supporting Market-Based Integration of Renewable Generation,” Proc. PowerTech Conference 2019, Milano, Italy, Jun. 23-27, 2019.


  1. D. Qiu, D. Papadaskalopoulos, Y. Ye, et. al, "Investigating the Impact of Demand Flexibility on Electricity Retailers", Proc. 20th Power Systems Computation Conference (PSCC), Dublin, Ireland, Jun. 11-15, 2018.

  2. T. Oderinwale,D. Papadaskalopoulos, Y. Ye, et. al, "Incorporating Demand Flexibility in Strategic Generation Investment Planning", Proc.15th International Conference on the European Energy Market (EEM), Lodz, Poland, Jun. 27-29, 2018.



专利:

  1. 叶宇剑; 袁泉; 汤奕 ; 一种抑制负荷过响应的分散式调控参数优化方法, 2021-2-17, 中国, ZL202110219581.8 授权

  2. 叶宇剑; 袁泉; 汤奕 ; 一种面向含大规模产消者社区的可扩展能量管理协同方法, 2021-11-05, 中国, 202111302186.2 (受理,实质审查中)

  3. 叶宇剑; 王卉宇; 汤奕 ; 一种基于近端策略优化的用户实时自治能量管理优化方法, 2021-7-27, 中国, 202110848508.1(受理,实质审查中)

  4. 叶宇剑; 汤奕; 胡健雄; 吴忠; 陈沛凌 ; 一种基于图论的电量图数据库构建及搜索方法, 2021-12-11, 中国, 202111510601.3 (受理,实质审查中)

  5. 叶宇剑; 王卉宇; 汤奕; 一种电力市场环境下考虑储能影响的可再生能源规划方法, 2022-2-16, 中国, 202210140149.4 (受理,实质审查中)

  6. 叶宇剑; 王卉宇; 汤奕; 一种电力市场环境下考虑网络容量和绿证交易的可再生能源并网规划方法, 2022-5-16, 中国, 202210160849.6 (受理)

  7. 叶宇剑; 汤奕; 胡健雄; 吴忠; 陈沛凌 ; 一种基于图卷积神经网络的电量视角推荐方法及系统, 2021-12-08, 中国, 202111491511.4 (受理,实质审查中)

  8. 袁泉; 叶宇剑; 汤奕 ; 图卷积和深度置信网络的电动汽车负荷优化方法, 2021-12-18, 中国, 202111556287.2(受理)

  9. 陈沛凌; 叶宇剑; 胡健雄; 王洪儒; 殷勇高; 汤奕; 韩啸; 智慧楼宇用电分析的动态建模方法、系统、设备和介质, 2021-12-20,中国, 202111566675.9(受理,实质审查中)

  10. 臧汉洲; 叶宇剑; 汤奕; 钱俊良; 周吉; 一种基于内点策略优化的电动汽车充放电策略优化方法;2022-07-19,中国,202210848364.x(受理)

  11. 袁泉; 王琦;汤奕; 叶宇剑; 一种电动汽车充电决策的权重量化方法, 2021-12-18, 中国, 202011346880.X(受理,实质审查中)


教学

2021春研究生课程:智能电网(全英文),2021秋本科生课程:智能电网新技术(研讨)

2022春研究生课程:智能电网(全英文),2022春研究生课程:优化理论新技术(全英文), 2022秋本科生课程:智能电网新技术(全英文)(研讨)