光華講壇——社會(huì)名流與企業(yè)家論壇第6691期
主題：Frequency Control Ancillary Services and Bidding in the National Electricity Market (國(guó)家電力市場(chǎng)中的頻率控制輔助服務(wù)和競(jìng)標(biāo))
主講人：澳大利亞新南威爾士大學(xué) 莫華東教授
主持人：西南財(cái)經(jīng)大學(xué) 管理科學(xué)與工程學(xué)院副院長(zhǎng) 肖輝
時(shí)間：12月19日10:00
地點(diǎn)：柳林校區(qū)通博樓D301會(huì)議室
主辦單位：管理科學(xué)與工程學(xué)院
主講人簡(jiǎn)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.
莫華東博士（IEEE SMC早期職業(yè)獎(jiǎng)獲得者2024）于2019年加入澳大利亞新南威爾士大學(xué)擔(dān)任講師，并于2021年晉升為高級(jí)講師。他目前是系統(tǒng)與計(jì)算學(xué)院系統(tǒng)工程學(xué)科的協(xié)調(diào)員。他曾在蘇黎世瑞士聯(lián)邦理工學(xué)院擔(dān)任博士后研究員。他于2012年獲得中國(guó)科學(xué)技術(shù)大學(xué)自動(dòng)化學(xué)士學(xué)位，并于2016年獲得香港城市大學(xué)工業(yè)工程與工程管理博士學(xué)位。多年來(lái)致力于利用基于學(xué)習(xí)的算法提高復(fù)雜系統(tǒng)的彈性、性能和安全性的研究，主要集中在電力和能源系統(tǒng)、網(wǎng)絡(luò)物理系統(tǒng)和制造系統(tǒng)籌新興領(lǐng)域，利用數(shù)據(jù)收集和分析能力揭示系統(tǒng)在不確定性下的演化模式，發(fā)表了60多篇SCI和會(huì)議論文，并出版了一本專(zhuān)著。莫博士指導(dǎo)了4名博士研究生，目前在讀的博士生超過(guò)15名，并擔(dān)任了大約10個(gè)相關(guān)SCI期刊和國(guó)際會(huì)議的編委會(huì)成員。莫博士目前是IEEE高級(jí)會(huì)員和IEEE SMC ACT分會(huì)主席。
內(nèi)容簡(jiǎn)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.
在本次講座中，我將首先介紹電力市場(chǎng)的背景，特別是頻率控制輔助服務(wù)（FCAS）的市場(chǎng)規(guī)則和投標(biāo)模式。然后，我將介紹雙層模型，該模型包括a）調(diào)整電池儲(chǔ)能系統(tǒng)的投標(biāo)策略，以最大限度地提高FCAS收入，以及b）調(diào)整國(guó)家電力市場(chǎng)（NEM）調(diào)度引擎的購(gòu)買(mǎi)容量，以最大程度地降低FCAS的總成本。我采用了強(qiáng)化學(xué)習(xí)的方法來(lái)求解當(dāng)前的雙層模型。我還將介紹兩種正在建設(shè)中的加速計(jì)算的新方法——量子強(qiáng)化學(xué)習(xí)和固定時(shí)間梯度動(dòng)力學(xué)。
