叶宇剑 - 东南大学 - 电气工程学院

个人简介

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

研究领域

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

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

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. 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. 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. 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, early access． 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. 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. 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. 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．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． 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. 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） 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, July. 2015. 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. 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． 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. 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. 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. 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.