余佩佩
星空体育在线网页版 讲师
研究方向
需求响应，综合能源系统，虚拟电厂，海风制氢，人工智能
联系方式
地址：上海市杨浦区长阳路2588号
邮箱：peipeiyu@shiep.edu.cn
个人简介
2016年和2019年分别于浙江大学获得理学学士与硕士学位，硕士毕业后在阿里巴巴从事数据分析师，2020-2024年跨专业攻读澳门大学工学博士学位（Electronic and Computer Engineering）。期间，曾于2023年赴清华大学电机系进行为期半年的学术交流与访问。2024年8月于上海电力大学任教。
近五年，发表学术论文22篇，包括高水平SCI源刊论文11篇，其中以第一或通讯作者发表包括IEEE Transactions on Power Systems、IEEE Transactions on Smart Grid及Applied Energy等在内的本领域SCI(中科院一区)Top期刊论文5篇，ESI高被引论文1篇。获2021可持续电力与能源国际会议iSPEC最佳论文奖、2022中国南方电网电力调度AI应用大赛唯一的一等奖、创新奖。目前，主持上海市浦江人才计划项目1项、上海市晨光计划1项、上海市教委人工智能专项计划1项。
科研指导理念
最理想的师生关系，一定不是借着导师所谓的权力，让学生沦为工具人，害怕甚至不愿发表自己的观点。而应当是，导师有幸能参与学生的硕士生涯，通过适当科研训练，帮助其在专注力的培养、解决问题的能力、行为处事的习惯、以及自我意志的表达上，成长为更好的自己。同时，期望学生度过快乐而又充实的三年时光。至于成果产出，我相信是认知提升的自然结果，无需刻意强求。
教育背景
2020-2024 澳门大学，Electronic and Computer Engineering，博士
2023 清华大学，电机系，访问学者
2016-2019 浙江大学，计算数学，硕士
2013-2016 浙江大学，数学与应用数学，学士
代表性期刊论著
[1] P. Yu, H. Zhang, Y. Song, H. Hui and C. Huang, “Frequency Regulation Capacity Offering of District Cooling System: An Intrinsic-motivated Reinforcement Learning Method,” IEEE Transactions on Smart Grid, vol. 14, no. 4, pp. 2762-2773, July. 2023.
[2] P. Yu, H. Zhang and Y. Song, “Equivalent System Model of District Cooling System in Frequency Domain to Provide Primary Frequency Regulation,” CSEE Journal of Power and Energy Systems, Early Access, Jun. 2023.
[3] P. Yu, H. Zhang, Y. Song, H. Hui and G. Chen, “District Cooling System Control for Providing Operating Reserve Based on Safe Deep Reinforcement Learning,” IEEE Transactions on Power Systems, vol. 39, pp. 40-52, Jan. 2024. (Highly Cited Paper)
[4] P. Yu, H. Zhang and Y. Song, “Adaptive Tie-Line Power Smoothing of District Cooling System With Renewable Generation Based on Risk-aware Reinforcement Learning,” IEEE Transactions on Power Systems, vol. 39, pp. 6819-6832, Nov. 2024.
[5] P. Yu, H. Zhang and Y. Song, “District Cooling System Control for Providing Regulation Services based on Safe Reinforcement Learning with Barrier Functions,” Applied Energy, vol. 347, pp. 121396, Oct. 2023.
[6] P. Yu, H. Zhang, Z. Hu and Y. Song, “Voltage Control With District Cooling Systems: Compensator-based Scenario-classified Reinforcement Learning,” Applied Energy, Early Access, Sep. 2024.
[7] P. Yu, H. Hui, H. Zhang, C. Huang and Y. Song, “Frequency Regulation Capacity Offering of District Cooling System Based on Reinforcement Learning,” 2022 IEEE Power & Energy Society General Meeting (PESGM), Denver, America, Jul. 2022.
[8] H. Hui1, P. Yu 1, H. Zhang, N.Dai, W. Jiang and Y. Song, “Regulation capacity evaluation of large-scale residential air conditioners for improving flexibility of urban power systems,” International Journal of Electrical Power & Energy Systems, vol. 257, pp. 108269, Nov. 2022.
[9] 宋永华, 余佩佩*, 张洪财. 实时电价机制下基于复合两端采样强化学习的区域供冷系统需求响应运行控制[J]. 中国科学: 技术科学, 2023, 53: 1699– 1712.
[10] Z. Wang, P. Yu and H. Zhang, “Privacy-Preserving Regulation Capacity Evaluation for HVAC Systems in Heterogeneous Buildings based on Federated Learning and Transfer Learning,” IEEE Transactions on Smart Grid, vol. 14, no. 5, pp. 3535-3549, Dec, 2022.
[11] H. Hui, P. Yu, H. Zhang, N.Dai, Wei Jiang and Yonghua Song, “Regulation Capacity Evaluation of Large-scale Heterogeneous Residential Air Conditioning Loads,” 2021 IEEE Sustainable Power and Energy Conference (iSPEC), pp. 2505-2510, Hainan, China, Dec, 2021. (Best Paper Award)
[12] H. Hui, P. Siano, Y. Ding, P. Yu, Y. Song, H. Zhang and N. Dai, “A Transactive Energy Framework for Inverter-Based HVAC Loads in a Real-Time Local Electricity Market Considering Distributed Energy Resources,” IEEE Transactions on Industrial Informatics, vol. 18, no. 12, pp. 8409 - 8421, Feb. 2022.