陈 涛

发布者:宋阳发布时间:2020-01-02浏览次数:6371

陈 涛

职称:  讲师、硕士生导师

研究方向: 电力需求侧管理,电力市场,人工智能技术应用

Email:  taoc@seu.edu.cn




个人简介:

陈涛,安徽铜陵人,民革党员,博士/博士后,东南大学电气工程学院讲师、美国密歇根大学博士学位,弗吉利亚理工大学博士后。2012年至2015年期间在芬兰坦佩雷电力研究中心及芬兰国立研发中心取得工程硕士学位并工作实习,参与欧盟智慧电力能源市场(Smart Electricity & Energy Market)、北欧电力公司(Elenia Oy)数据分析等项目,2015年至2018年博士期间参与研究多项美国自然科学基金、福特汽车公司、底特律电力公司(DTE)科研项目,2017年期间在美国加州国家电网全球能源互联网美国研究院(GEIRI North America)工作实习,2018年至2019年博士后期间协助IEEE Life FellowIEEE PES主席 Saifur Rahman 教授指导多名博士、硕士研究生,参与工作及申请了美国国家自然科学基金(NSF)、美国能源部(DOE)、以及国际知名企业(ABBComEDTritium)的多项课题。

      其本人长期从事电力市场、电力需求侧管理、人工智能技术应用的研究工作,共发表各类SCI\EI\核心论文50余篇,获IEEE ISGT-ASIA会议2019年度最佳论文奖、IEEE iSPEC会议2021年度最佳论文奖、IEEE Transactions on Smart Grid期刊2020年度最佳审稿人奖。曾受邀担任 APPEECEI2ISGT-AsiaiSPEC等各类国际会议Session Chair,现担任《电力需求侧管理》杂志编委会委员、IEEE PES(中国)智能电网柔性资源互动分委会常务理事。现主持国家自然科学基金(青年)、江苏省自然科学基金(青年)、澳门大学智慧城市物联网国家重点实验室开放课题、国家电网科技项目等课题共10项,参与国家重点研究计划、国家自然科学基金(面上)、英国繁荣基金、国网/南网科技项目等课题20余项。



论著:       

期刊论文

  1. T. Chen, C. Gao, and Y. Song, “Optimal control strategy for solid oxide fuel cellbased hybrid energy system using deep reinforcement learning”, IET Renewable Power Generation, vol.16, no.5, pp.912-921, 2022.

  2. T. Chen, M. Song, H. Hui, and H. Long, “Battery electrode mass loading prognostics and data-driven analysis for lithium-ion battery-based energy storage system”, Frontiers in Energy Research, 543, 2021.

  3. T. Chen, C. Gao, H. Hui and H. Long, “A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis”, Transactions of the Institute of Measurement and Control, November 2021.

  4. T. Chen, Q. Cui, C. Gao, and et. al., “Optimal Demand Response Strategy of Commercial Building-based Virtual Power Plant using Reinforcement Learning”, IET Generation, Transmission & Distribution, v.15, no.16, pp.2309-2318, August 2021.

  5. T. Chen, M. Pipattanasomporn, I. Rahman, Z. Jing, and S. Rahman, “MATPLAN: A Probability-based Planning Tool for Cost-effective Integration of Renewable Energy into the Electricity Grid”, Renewable Energy, vol.156, pp.1089-1099, August 2020.

  6. T. Chen, and W. Su, "Indirect Customer-to-Customer Energy Trading with Reinforcement Learning", IEEE Trans. on Smart Grid, vol.10, no. 4, pp.4338-4348, 2019.

  7. T. Chen, B. Zhang, H. Pourbabak, A. K. Fard, and W. Su, “Optimal Routing and Charging of an Electric Vehicle Fleet for High-Efficient Dynamic Transit Systems”, IEEE Trans. on Smart Grid, vol.9, no.4, pp.3563-3572, July 2018.

  8. T. Chen, and W. Su, "Local Energy Trading Behavior Modeling with Deep Reinforcement Learning", IEEE Access, vol.6, no.1, pp.62806-62814, Dec. 2018.

  9. T. Chen, Q. Alsafasfeh, H. Pourbabak, and W. Su, "The Next-generation Retail Electricity Market with Customers and Prosumers - A Bibliographical Survey", Energies, vol.11, no.1, 2017.   

  10. T. Chen, H. Pourbabak, Z. Liang, and W. Su, "An Integrated eVoucher Mechanism for Flexible Loads in Real-Time Retail Electricity Market", IEEE Access, vol. 5, pp. 2101 – 2110, 2017.

  11. M. Niu, C. Gao, and T. Chen. “Energy Pricing Mechanism Considering Energy Converter Devices as Components of Pan-Energy Transmission System”, IEEE Transactions on Smart Grid, vol.13, no.2, pp.1061-1074, 2022.

  12. Y. Xu, K. Mert, L. Mili, J. Valinejad, T. Chen, and X. Chen, "An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty", IEEE Transactions on Smart Grid, vol.12, no.3, pp. 2696 – 2707,2021.

  13. Y. Yao, C. Gao, K. Lai, T. Chen, and J. Yang, “An incentive-compatible distributed integrated energy market mechanism design with adaptive robust approach”, Applied Energy, vol.282, 2021.

  14. X. Zhang, M. Pipattanasomporn, T. Chen, and S. Rahman, "An IoT-based Thermal Model Learning Framework for Smart Buildings", IEEE Internet of Things Journal, 2019, vol.7, no.1, pp.518 – 527, 2019.

  15. H. Hui, Y. Ding, T. Chen, S. Rahman, and Y. Song, “Dynamic and Stability Analysis of the Power System With the Control Loop of Inverter Air Conditioners”, IEEE Transactions on Industrial Electronics, vol.68, no.3, pp. 2725 – 2736, 2021.

  16. M. A. Mohamed, T. Chen, W. Su, and T. Jin, Proactive Resilience of Power Systems Against Natural Disasters: A Literature Review. IEEE Access, vol.7, pp.163778-163795, 2019.

  17. H. Pourbabak, J. Luo, T. Chen, and W. Su, "A Novel Consensus-based Distributed Algorithm for Economic Dispatch Based on Local Estimation of Power Mismatch", IEEE Trans. on Smart Grid, vol.9, no.6, pp.5930-5942, November 2018.

  18. H. Pourbabak, A. Ajao, T. Chen, and W. Su, "Fully Distributed AC Power Flow (D-ACPF) Algorithm for Distribution Systems”,IET Smart Grid, vol. 2, no. 2, pp. 155-162, 2018.

  19. F. Chang, T. Chen, W. Su, and Q.H. Alsafasfeh, "Charging Control of an Electric Storage Battery Based on Reinforcement Learning and Long Short-term Memory Networks", Computers and Electrical Engineering, vol.85, 106670, July 2020.

  20. S. Xu, X. Chen, J. Xie, S. Rahman, J. Wang, H. Hui and T. Chen, “Agent-based modeling and simulation for the electricity market with residential demand response” CSEE Journal of Power and Energy Systems, vol.7, no.2, pp.368 – 380, May 2020.

  21. K. Lai, T. Chen, and B. Natarajan, “Optimal scheduling of electric vehicles car-sharing service with multi-temporal and multi-task operation”, Energy, vol.204, 2020.

  22. Li, Dongsen, Ciwei Gao, T. Chen, Xiaoxuan Guo, and Shuai Han. "Planning strategies of power-to-gas based on cooperative game and symbiosis cooperation." Applied Energy 288 (2021): 116639.

  23. Y. Yao, C. Gao, T. Chen, J. Yang, and S. Chen, “Distributed electric energy trading model and strategy analysis based on prospect theory”, International Journal of Electrical Power & Energy Systems, vol. 131, 2021.

  24. Y. Wang, K. Tang, K. Lai, T. Chen and X. Wu, “Optimal planning of charging stations for an electric vehicle fleet in car‐sharing business”, International Transactions on Electrical Energy Systems, e13098, 2021.

  25. Liu, K., Peng, Q., Li, K., & T. Chen, “Data-Based Interpretable Modeling for Property Forecasting and Sensitivity Analysis of Li-ion Battery Electrode”, Automotive Innovation, 1-13, 2022.

  26. Li, T., Gao, C., T. Chen, Jiang, Y., & Feng, Y. “Medium and long-term electricity market trading strategy considering renewable portfolio standard in the transitional period of electricity market reform in Jiangsu, China”, Energy Economics, 105860, 2022.

  27. Yan, X., Gao, C., Song, M., T. Chen, Ding, J., Guo, M., ... & Abbes, D. An IGDT-based Day-ahead Co-optimization of Energy and Reserve in a VPP Considering Multiple Uncertainties. IEEE Transactions on Industry Applications, 2022.

  28. 宋艺航,王刚,蔡浩,高赐威,陈涛.售电公司购售电决策研究综述[J].电力需求侧管理,2020,22(06):85-89.

  29. 黄国日,宋艺航,蔡浩,高赐威,陈涛.考虑可转移负荷的售电公司负荷组合优化[J].电力建设,2020,41(11):126-134.

  30. 黄豫,邵冲,郝洁,柴明哲,高赐威,陈涛.能源互联网环境下的多能需求响应技术[J].电力需求侧管理,2020,22(05):2-6+18.

  31. 胡秦然,丁昊晖,陈心宜,陈涛,丁一原,李扬.美国加州2020年轮流停电事故分析及其对中国电网的启示[J].电力系统自动化,2020,44(24):11-18.

  32. 葛毅,陈佳铭,朱永康,史静,李冰洁,胡秦然,陈涛.考虑广义需求侧资源的江苏“十四五”电源规划[J].电力需求侧管理,2021,23(2):04-09.

  33. 蔡浩,黄博,高赐威,陈涛.考虑用户用电效用的售电公司交易联合优化策略[J].电力需求侧管理,2021,23(06):31-36.

  34. 柴明哲,高赐威,陈涛,胡楠,管永丽.江苏省工业用户配置储能的经济性研究[J].电力需求侧管理,2021,23(03):47-51.

  35. 宋雨桐,陈涛,高赐威,宋梦,胡秦然.基于深度强化学习技术的光伏-固体氧化物燃料电池混合能源系统多场景控制[J].中国电机工程学报,2022,DOI:10.13334/j.0258-8013.

  36. 管馨,陈涛,高赐威.适应风电参与电力市场的需求侧储能负荷运行优化研究[J].综合智慧能源,2022,44(02):35-41.

  37. 高赐威,王崴,陈涛.基于可逆固体氧化物电池的电氢一体化能源站容量规划[J/OL].中国电机工程学报:1-15[2022-03-28].

  38. 严兴煜,高赐威,陈涛,丁建勇.数字孪生虚拟电厂系统框架设计及其实践展望[J/OL].中国电机工程学报:1-17[2022-03-28].


代表性科研项目:

主持项目:

  • 国家自然科学基金(青年)项目,2022.1 - 2024.1224万元(主持)

  • 江苏省自然科学基金(青年)项目,2021.7 - 2024.720万元(主持)

  • 澳门大学智慧城市物联网国家重点实验室开放课题项目,2022.3 – 2023.1210万澳元(主持)

  • 江苏省智能电网技术与装备重点实验室(一般)项目,2022.1 – 2023.128万元(主持)

  • 南京留学人员科技创新(择优)项目,2021.7 - 2022.73万元(主持)

  • 国家电网公司科技项目,“大规模可调节负荷资源评估、规划运行、优化调节关键技术研究及示范应用”,2021.1 - 2022.12202万元(主持)

  • 国家电网公司科技项目,“基于源网荷储互动的清洁能源市场化水平消纳体系研究”,2020.1 - 2021.1284万元(主持)

  • 国家电网公司科技项目,“现货市场环境下的电网规划方法与仿真验证评价体系研究理论研究”,2020.1 - 2021.1238万元(主持)

参与项目:

  • 国家重点研究计划项目,“综合能源高效协同运行关键技术及应用示范”,2020.12 - 2023.11250万元(参与)

  • 国家自然科学基金(面上)项目,“连续时间优化理论在异构负荷聚合建模与优化调度中的应用”, 2022.1-2025.1258万元(参与)

  • 国家自然科学基金(面上)项目,“跨境综合能源系统分布式协同运行及联合仿真技术研究”, 2022.1-2025.1258万元(参与)

  • 国家自然科学基金专项,2022 IEEE第五届国际电气与能源大会(CIEEC 2022),2021.10 - 2022.510万元(参与)

  • 中英繁荣基金项目CELCEP,“Efficiency Analysis and Policy Recommendation of Jiangsu Power Market Design and Operation2020.6 – 2021.1125万英镑(参与)

  • 国家电网科技项目,“国网安徽电力在现代能源体系转型升级中的体制机制研究”,2020.8 – 2021.8115万元(参与)

  • 国家电网科技项目,“客户侧柔性资源互动运营与交易支撑技术研究”,2020.12 – 2022.12130万元(参与)

  • 国家电网科技项目,“面向信息通信基础设施的虚拟电厂构建与运行关键技术及示范应用”,2020.7 – 2023.7120万元(参与)

  • 美国能源部(DOE)项目,"A Probability-based Model for Cost-effective Integration of Renewables into the Electricity Grid", 2017-201984万美金(参与)

  • 美国能源部(DOE)项目,"Enabling Secure and Resilient XFC: A Software/Hardware-Security Co-Design Approach", 2019-2020220万美金(参与)

  • 美国自然科学基金(NSF)项目,"I-Corps: Distributed Energy Management Systems for Grid Integration of Distributed Energy Storage Devices", 2015-201660万美金,(参与)

  • 密歇根大学-上海交通大学联合(UM-SJTU)项目,"Adaption and Coordination Technology of Large-Scale EV Charging and Variable Renewable Energy Based on Big Data and Electricity Network Reliability Analysis", UM-SJTU, 2016-201925万美金(参与)

  • 芬兰欧盟Smart Energy and Electricity Market项目,2013.1 – 2015.12120万欧元(参与)



教学:

发电厂电气部分 32学时 (本科生课程、春学期)

人工智能在电力系统领域应用  32学时(研究生课程、秋学期/春学期)

电力系统综合设计(研讨) 16学时(协助外教授课、暑期学校)

文献检索与学术写作 32学时(本科生课程、暑期学校)



其他:

业余爱好文史、哲学研究,掌握初级芬兰语,中级德语。与学生为友,互切互磋,共学共进,招co-worker、不招labor,崇尚自由而无用的灵魂,寄望学生体验科学工程理论之趣味、宇宙人生之玄妙,能力有限、故盼诸君青出于蓝而胜于蓝。