Invited Speaker
Prof. Shunli Wang
RSA Life Fellow, IET Fellow
Sichuan University, China；Inner Mongolia University of Technology, China
Bio: Shunli Wang is a Professor and Doctoral Supervisor, Academic Dean at Inner Mongolia University of Technology, Executive Vice President at Smart Energy Storage Institute, Haina Distinguished Professor at Sichuan University, RSA Life Fellow, IET Fellow, IEEE Senior Member, PCIM Asia Committee Member, IEEE PES Committee Member, Top 2% Worldwide Scientist, Global Highly Cited Researcher. Focusing on the major national strategic needs of new energy and energy storage systems, the research of green and low-carbon energy storage is conducted in smart grids, undertaken 56 projects such as the National Natural Science Foundation and National Key Research & Development, with a Research Interest Score value of 14995, and 258 articles published on SCI-indexed famous journals with 53 articles in the First Area / TOP journals in Chinese Academy of Sciences, 42 high-cited ones, 23 international and domestic invention patents, 20 software Copyrights and standard formulation and 9 books have been published by first-class international and domestic publishing houses. 10 awards at or above the provincial or ministerial level have been achieved, including 3 international gold medals. 17 chairmanships of international conferences have been participated in, with 5 editorial boards of international periodicals. The core technical achievements have reached an international advanced level and have been reported by the People's Daily.
王顺利，教授，博导，内蒙古工业大学电力学院学术院长，智慧储能研究院常务副院长，四川大学海纳特聘教授，英国皇家学会工艺院终身院士，英国工程与技术学会会士（IET Fellow），IEEE Senior Member，PCIM Asia委员，IEEE PES委员，全球前2%顶尖科学家。紧密围绕新能源与储能系统国家重大战略需求，聚焦开展智能电网绿色低碳储能研究，承担国家自然科学基金、国家重点研发等项目56项，RIS值为14995，和Robert Gordon University、Aalborg University联合，发表SCI核心库检索文章258篇，其中中科院一区/TOP期刊文章53篇，高被引/热点文章42篇，授权国际/国内发明专利23项、软件著作权20项，在国际和国内一级出版社出版著作9部，获得省部级以上奖励10项，其中国际金奖3项，担任国际会议主席17次，国际和国内中文核心期刊编委5项，核心技术成果鉴定达到国际先进水平，获得《人民日报》报道。
Speech Topic: Situation Awareness Technology for New Electric Energy Storage Systems and Its Application in Energy Storage Power Stations
Abstract: Energy storage plays an important role in the integration of clean energy into the grid, which holds profound strategic significance for ensuring national energy security, promoting the optimization and upgrading of industrial structures, and facilitating the green and low-carbon transformation of industries. As a key regulating unit for the coordinated operation of "source-grid-load-storage", the situational awareness accuracy and robustness of large-scale energy storage directly determine the level of safe and stable operation of the power grid and the effectiveness of regulation and control strategies. For multi-cluster, multi-level large-scale energy storage systems of gigawatt scale, conducting research on composite modeling of mechanical-electrical-material characteristics and constructing a comprehensive evaluation and optimization strategy for safe states are crucial to breaking through the technical bottlenecks of large-scale energy storage management and real-time modeling, and enhancing the safety, economy, and practicality of energy storage systems. This report details novel power energy storage modeling methods to achieve reliable monitoring of core state parameters. It constructs a multi-feature composite optimization model to accurately characterize the internal characteristics of batteries, overcoming the challenges of dynamic responses to electrical parameters and nonlinear analysis of electrical characteristics. New algorithms for multi-dimensional information fusion and online estimation are proposed, breaking through the bottleneck of collaborative estimation of state parameters across multiple time scales. It achieves innovation in real-time monitoring and early warning of core state parameters, significantly enhancing the intelligence and reliability of safety monitoring for large-scale energy storage systems, and realizing the industrial application of large-scale energy storage in new power systems.