葉宇劍

葉宇劍

葉宇劍，博士，國家青年高層次人才、東南大學青年首席教授，博士生導師，IEEE Senior Member，多年來一直從事電力市場、智慧型電網及能源系統領域最佳化和智慧型決策等關鍵問題的研究，主要研究方向包括電力市場建模與分析，人工智慧在電力及能源系統的套用，能源網際網路中的建模、最佳化與控制等國際前沿熱點課題。主持國家自然科學基金（青年）、江蘇省自然科學基金（青年）、2021年江蘇省“雙創博士”世界名校類人才項目和2022年江蘇省科技副總項目各一項。

基本介紹

  • 中文名：葉宇劍
  • 國籍：中國
  • 出生日期：1988年8月1日
  • 職業：教師
人物簡介,主持參與的項目,專著,會議論文,專利,教學,所獲榮譽,

人物簡介

葉宇劍，國家青年高層次人才，東南大學青年首席教授、教授、博士生導師；倫敦帝國理工學院榮譽講師、全額獎學金博士；IEEE、中國電機工程學會高級會員、中國電工技術學會、中國人工智慧學會高級會員。現主持國家青年高層次人才項目、國家自然科學基金青年項目、江蘇省自然科學基金青年項目、CCF-騰訊犀牛鳥基金項目、國網總部科技項目等10餘項，作為研究骨幹參與國家自然科學基金國際合作與交流項目1項。
作為第一/通訊作者，在Proc. IEEE、IEEE TPWRS、IEEE TSG、Appl. Energy、IEEE IoT J.等國際電力、能源、物聯網、自動化等領域頂級期刊上發表中科院一區Top SCI 論文20篇，累積影響因子超過220，2篇入選ESI高被引，發表2篇CCF A類人工智慧會議論文，授權國家發明專利5項。擔任IEEE TSG、IEEE TII、IEEE TIA、IEEE Syst. J 等多個期刊副編輯，APEN、中國電力青年編委， 擔任中國人工智慧學會智慧型自適應協同最佳化控制專業委員會委員、中國自動化學會能源網際網路專業委員會、智慧型分散式能源專委會。
自攻讀博士學位以來，共發表60餘篇學術論文以及1部英文專著、作為通訊作者在IEEE旗艦期刊Proceedings of the IEEE上發表1篇論文、作為第一或通訊作者在中國電機工程學報、IEEE Trans. Power Systems、IEEE Trans. Smart Grid等國內外高水平學術期刊上發表共26篇論文（其中中科院一區Top SCI 論文15篇），h因子17，累積影響因子超過160，第一作者 IEEE Trans. 期刊論文單篇最高引用破百;共發表21篇EI 國際會議論文，其中1篇CCF A類會議IJCAI2020論文（被評選為人工智慧技術在能源領域的開創性套用），1篇論文獲IEEE PESGM 2017會議最佳論文獎；以第一發明人申請國家發明專利10項。部分研究成果收錄於《電力系統經濟性原理》2019年修訂版的數個章節。
受邀擔任10餘個國內外學術組織會員和理事, 包括IEEE 高級會員（Senior Member）、中國電機工程學會（CSEE）會員、英國工程技術學會（IET）會員、IEEE PES電力系統運行、規劃與經濟技術委員會（中國）- 電力市場技術分委會理事、IEEE PES智慧型電網與新技術委員會（中國） - 智慧型電網與人工智慧分委會理事等。長期擔任英國工程和自然科學研究委員會（EPSRC）基金項目評審專家;
擔任IEEE Transactions on Smart Grid、IEEE Transactions on Industry Applications等期刊編委; Applied Energy等期刊青年編委；中國電機工程學報等期刊專題編委。擔任20餘個國內外頂級學術期刊審稿人，2021年獲電力系統自動化期刊優秀審稿專家、2020年獲IEEE Trans. Power Systems期刊傑出審稿專家、2017年獲International Journal of Electrical Power and Energy Systems期刊傑出審稿專家。

主持參與的項目

  1. 國家自然科學基金（青年）項目，可交易能源市場環境下配用電系統多異構主體分層自治協同最佳化方法，2023.01至2025.12，主持；
  2. 江蘇省自然科學基金（青年）項目，2022.07至2025.06，主持；
  3. 2022年“江蘇省科技副總”項目，主持；
  4. 2021年江蘇省“雙創博士”項目，主持；
  5. 東南大學“複雜工程系統測量與控制”教育部重點實驗室2022年度開放課題，主持
  6. 國家電網江蘇省電力有限公司業務研究項目，基於區域試點先行的分散式綠電現貨運營機制研究，主持；
  7. 國家電網浙江省電力有限公司項目，電量統計知識圖譜構建與人工智慧套用技術研究,主持；
  8. 國家電網浙江省電力有限公司項目，基於統計學理論的電力知識圖譜構建技術研究,主持；
  9. 南方電網深圳供電局有限公司項目，智慧型電網調度全景AI指揮平台關聯技術研究與套用，參與；
  10. 英國工程和自然科學研究委員會，英韓聯合項目, EP/N03466X/1，Peer-to-peer energy trading and sharing - 3M (Multi-times, Multi-scales, Multi-qualities), 2016-09至2020-02, 98萬英鎊，結題，參與；
  11. 歐洲地區發展基金會，英國首個本地能量市場項目，Cornwall local energy market，2017.09至2020.12，1900萬英鎊，結題，參與；
  12. Innovate UK，104249，E-FLEX - Real-world Energy Flexibility through Electric Vehicle Energy Trading，2018.09至2022.02，37萬英鎊，結題，參與；
  13. 英國工程和自然科學研究委員會，中英聯合項目，EP/T021780/1，Technology Transformation to Support Flexible and Resilient Local Energy Systems，2020-07至2023-03，81萬英鎊，在研，參與；
  14. 歐盟“水平線2020計畫”，歐盟委員會項目，773505，EU-Sysflex (Pan-European system with an efficient coordinated use of flexibilities for the integration of a large share of RES)，2017.11至2021.11，2000萬歐元，結題，參與；
  15. 歐盟“水平線2020計畫”，歐盟委員會項目，864276，TradeRES – Tools for the Design and Modelling of New Markets and Negotiation Mechanisms for a 100% Renewable European Power System，400萬英鎊，2020.02至2023.02，在研，參與；
  16. 歐盟“水平線2020計畫”，歐盟委員會項目，824386，MERLON – Integrated Modular Energy Systems and Local Flexibility Trading for Neural Energy Islands，730萬英鎊，2019.01至2022.04，已結題，參與；
  17. 英國工程和自然科學研究委員會，面上項目，EP/R045518/1，Integrated Development of Low-Carbon Energy Systems (IDLES): A Whole-System Paradigm for Creating a National Strategy，2018-11至2023-10，705萬英鎊，在研，參與；
  18. 英國工程和自然科學研究委員會，中英聯合項目，EP/L014386/1，Business, economics, planning and policy for energy storage in low-carbon futures, 2014-09至2017-09，100萬英鎊，結題，參與；
  19. 英國工程和自然科學研究委員會，中英聯合項目，EP/L001039/1，Grid Economics, Planning and Business Models for Smart Electric Mobility，2013-12至2016-12，100萬英鎊，結題，參與；
  20. 英國工程和自然科學研究委員會，面上項目，EP/V012053/1，The Active Building Centre Research Programme (ABC RP)，2020-04至2022-09，932萬英鎊，在研，參加。
  21. Economic and Social Research Council，面上項目，ES/T000112/1，Socio-Techno-Economic Pathways for Sustainable Urban Energy Development，2019-05至2022-04，30萬英鎊，在研，參與；
  22. 英國工程和自然科學研究委員會，面上項目，EP/R030235/1，Resilient Electricity Networks for a productive Grid Architecture (RENGA)，2018-05至2022-04，98萬英鎊，在研，參加；
  23. 英國工程和自然科學研究委員會，面上項目，EP/L019469/1，SUPERGEN Energy Storage Hub，391萬英鎊，2014-06至2019-12，已結題，參與；
  24. 英國工程和自然科學研究委員會，面上項目，EP/K039326/1，Whole Systems Energy Modelling Consortium (WholeSEM)，2013-07至2018-01，461萬英鎊，已結題，參與。

專著

  1. Y. Ye; Modelling and Analysing the Market Integration of Flexible Demand and Storage Resources; Nanjing: Southeast University Press & Springer, Aug. 2022.（英文專著）
期刊論文(第一或通訊作者論文累積影響因子超過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 (通訊作者), et. Al, “Transition to digitalised paradigms for security control and decentralised electricity market,” Proceedings of the IEEE, early access.
  3. 葉宇劍，袁泉，劉文雯，等．基於參數共享機制多智慧型體深度強化學習的社區能量管理協同最佳化[J/OL]．中國電機工程學報: 1-14. DOI:10.13334/j.0258-8013.pcsee.212707.
  4. Q. Yuan，Y. Ye (通訊作者), 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 (通訊作者), 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 (通訊作者), 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. 葉宇劍, 袁泉, 湯奕，等.抑制柔性負荷過回響的微網分散式調控參數最佳化[J]．中國電機工程學報，2022，42(05)：1748-1760．
  9. 葉宇劍，王卉宇，湯奕，等．基於深度強化學習的居民實時自治最優能量管理策略[J]．電力系統自動化，2022，46(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.（單篇引用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 (通訊作者), 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 (通訊作者), 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 (通訊作者), 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 (通訊作者), 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 (通訊作者), 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 (通訊作者), 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 (通訊作者), 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 (通訊作者), 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. 臧漢洲,葉宇劍 (通訊作者) 等.基於內點策略最佳化的受約束電動汽車充放電策略[J/OL].電網技術: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 (通訊作者), 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. 馮雙,崔昊,陳佳寧,葉宇劍,湯奕,雷家興.基於自編碼器信號壓縮與LSTM的寬頻振盪擾動源定位方法[J/OL].電力系統自動化:1-12.
  37. 崔昊,馮雙,陳佳寧,葉宇劍,湯奕,雷家興.基於自編碼器與長短期記憶網路的寬頻振盪廣域定位方法[J].電力系統自動化,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.

會議論文

  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類、世界人工智慧頂會, 2020年接收率為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. (會議最佳論文獎)
  3. Y. Ye, D. Qiu, et. al, “A Deep Q Network Approach for Optimizing Offering Strategies in Electricity Markets,” Proc. 2 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. 2International 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.
  20. 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.
  21. 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.

專利

  1. 葉宇劍; 袁泉; 湯奕 ; 一種抑制負荷過回響的分散式調控參數最佳化方法, 2021-2-17, 中國, ZL202110219581.8 （授權）
  2. 葉宇劍; 袁泉; 湯奕 ; 一種面向含大規模產消者社區的可擴展能量管理協同方法, 2021-11-05, 中國, 202111302186.2 （受理，實質審查中）
  3. 葉宇劍; 王卉宇; 湯奕 ; 一種基於近端策略最佳化的用戶實時自治能量管理最佳化方法, 2021-7-27, 中國, 202110848508.1（受理，實質審查中）
  4. 葉宇劍; 湯奕; 胡健雄; 吳忠; 陳沛凌 ; 一種基於圖論的電量圖資料庫構建及搜尋方法, 2021-12-11, 中國, 202111510601.3 （受理，實質審查中）
  5. 葉宇劍; 王卉宇; 湯奕; 一種電力市場環境下考慮儲能影響的可再生能源規劃方法, 2022-2-16, 中國, 202210140149.4 （受理，實質審查中）
  6. 葉宇劍; 王卉宇; 湯奕; 一種電力市場環境下考慮網路容量和綠證交易的可再生能源併網規劃方法, 2022-5-16, 中國, 202210160849.6 （受理）
  7. 葉宇劍; 湯奕; 胡健雄; 吳忠; 陳沛凌 ; 一種基於圖卷積神經網路的電量視角推薦方法及系統, 2021-12-08, 中國, 202111491511.4 （受理，實質審查中）
  8. 袁泉; 葉宇劍; 湯奕 ; 圖卷積和深度置信網路的電動汽車負荷最佳化方法, 2021-12-18, 中國, 202111556287.2（受理）
  9. 陳沛凌; 葉宇劍; 胡健雄; 王洪儒; 殷勇高; 湯奕; 韓嘯; 智慧樓宇用電分析的動態建模方法、系統、設備和介質, 2021-12-20,中國, 202111566675.9（受理，實質審查中）
  10. 臧漢洲; 葉宇劍; 湯奕; 錢俊良; 周吉; 一種基於內點策略最佳化的電動汽車充放電策略最佳化方法；2022-07-19，中國，202210848364.x（受理）
  11. 袁泉; 王琦；湯奕; 葉宇劍; 一種電動汽車充電決策的權重量化方法, 2021-12-18, 中國, 202011346880.X（受理，實質審查中）

教學

2021春研究生課程：智慧型電網（全英文），2021秋本科生課程：智慧型電網新技術（研討）
2022春研究生課程：智慧型電網（全英文），2022春研究生課程：最佳化理論新技術（全英文）, 2022秋本科生課程：智慧型電網新技術（全英文）（研討）

所獲榮譽

2023年11月，入選《2023福布斯中國·青年海歸菁英100人評選》名單。

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