光华讲坛——社会闻东说念主与企业家论坛第6691期澡堂 偷拍

主题：Frequency Control Ancillary Services and Bidding in the National Electricity Market (国度电力市集中的频率限度补助做事和竞标)

主讲东说念主：澳大利亚新南威尔士大学 莫华东素质

主握东说念主：西南财经大学 搞定科学与工程学院副院长 肖辉

时刻：12月19日10:00

处所：柳林校区通博楼D301会议室

主理单元：搞定科学与工程学院

主讲东说念主简介：

Dr. Huadong Mo (IEEE SMC Early Career Award Receptor 2024) joined the University of New South Wales in Australia as a lecturer in 2019 and was promoted to senior lecturer in 2021. He is currently the coordinator of the Systems Engineering Discipline under the School of Systems and Computing. He was previously a postdoctoral fellow at the Swiss Federal Institute of Technology Zurich. Dr. Mo obtained a bachelor’s degree in Automation from the University of Science and Technology of China in 2012 and a Ph.D. in Industrial Engineering and Engineering Management from the City University of Hong Kong in 2016.

Dr. Mo has been engaged in research on enhancing better resilience， performance， and security of complex systems with learning-based algorithms， which primarily lay in the emerging field of power and energy systems， cyber-physical systems， and manufacturing systems， leveraging the capacity to collect and analyze data to reveal patterns of system evolution against uncertainties for many years， publishing over 60 SCI and conference papers， as well as one monograph.

Dr. Mo has supervised four doctoral students to graduate， with over 15 doctoral students currently enrolled and serves as a member of the editorial board in approximately 10 relevant SCI journals and international conferences. Dr. Mo is currently the IEEE Senior Member and Chair of the IEEE SMC ACT Chapter.

莫华东博士（IEEE SMC早期功绩奖获取者2024）于2019年加入澳大利亚新南威尔士大学担任讲师，并于2021年晋升为高档讲师。他当今是系统与计较学院系统工程学科的互助员。他曾在苏黎世瑞士联邦理工学院担任博士后商议员。他于2012年获取中国科学本领大学自动化学士学位，并于2016年获取香港城市大学工业工程与工程搞定博士学位。多年来奋力于讹诈基于学习的算法普及复杂系统的弹性、性能和安全性的商议，主要纠合在电力和能源系统、聚集物理系统和制造系统筹新兴范畴，讹诈数据采集和分析才略揭示系统在不祥情味下的演化款式，发表了60多篇SCI理会论说文，并出书了一册专著。莫博士指令了4名博士商议生，当今在读的博士生逾越15名，并担任了简短10个有关SCI期刊和国外会议的编委会成员。莫博士当今是IEEE高档会员和IEEE SMC ACT分会主席。

实质简介：

In this talk， I will first introduce the electricity market background， specifically the frequency control ancillary services (FCAS) market rules and bidding model. Then， I will present the bilevel model， which consists of a) adjusting the bidding strategy for the battery energy storage system to maximize FCAS revenue and b) adjusting the purchased capacity for the National Electricity Market (NEM) Dispatch Engine to minimize overall FCAS cost. I employed the reinforcement learning method to solve the current bilevel model. I will also present two new approaches under construction to accelerate the computation – quantum reinforcement learning and fixed-time gradient dynamics.

在本次讲座中澡堂 偷拍，我将最初先容电力市集的布景，很是是频率限度补助做事（FCAS）的市集次序和投标款式。然后，我将先容双层模子，该模子包括a）颐养电板储能系统的投标计谋，以最大限制地普及FCAS收入，以及b）颐养国度电力市集（NEM）颐养引擎的购买容量，以最猛经过地裁减FCAS的总本钱。我承袭了强化学习的步调来求解现时的双层模子。我还将先容两种正在确立中的加快计较的新步调——量子强化学习和固定时刻梯度能源学。

• 国度
• 澡堂 偷拍
• 做事
• 竞标
• Frequency