<フォーカストセッション C / Focused Session C ><日時/Date>

2024 年 1 月 30 日 (火曜日)

16:00-18:15(日本時間）

Tuesday, January 30th, 2024 16:00-18:15 （JST）<場所/Vanue>

オンライン (Online)<言語/Language>

英語 (English)＜司会/Moderator＞

九州大学 エネルギー研究教育機構 准教授 本山 宗主
Assoc. Prof. Munekazu Motoyama, Q-PIT, Kyushu University＜ファシリテーター/Facilitator＞ 九州大学 エネルギー研究教育機構 准教授 セリャンチン ロマン
Assoc. Prof. Roman Selyanchyn, Q-PIT, Kyushu University

九州大学 エネルギー研究教育機構 准教授 劉 城準
Assoc. Prof. Sungjun Yoo, Q-PIT, Kyushu University

時間 / Time

プログラム・講演者 / Program & Speaker
スウェーデン王立工科大学（KTH）との合同セッション
Joint Symposium between KTH and Kyushu University

テーマ/Theme:「Digital Revolution for a Sustainable Future」<講演者/Speaker>●グエン ディン ホア 准教授（九州大学カーボンニュートラル・エネルギー国際研究所，マス・フォア・インダストリ研究所）
Assoc. Prof. Dinh Hoa Nguyen, I2CNER/IMI, Kyushu University「Robust Peer-to-Peer Energy Systems Against Cyber-Attacks」●György Dán 教授 (Division of Network and Systems Engineering, KTH)
Prof. György Dán, Division of Network and Systems Engineering, KTH

16:00-18:15「AI for Energy Security: A Blessing or a Threat」●脇 隼人 准教授（九州大学 マス・フォア・インダストリ研究所）
Assoc. Prof. Hayato Waki, IMI, Kyushu University「Analysis of Alternating Projection Method for the Nontransverse Intersection ofConvex Sets」●Qianwen Xu 助教 (Division of Electric Power and Energy Systems, KTH)
Asst. Prof. Qianwen Xu, Division of Electric Power and Energy Systems, KTH「Data-Driven Modelling and Coordination of Sustainable Power Systems withSmart Converters」●廣瀨 慧 教授（九州大学 マス・フォア・インダストリ研究所）
Prof. Kei Hirose, IMI, Kyushu University「Modern Statistical Theories for Energy Demand Forecasting」●Shiva Sander Tavallaey 教授 (Division of Engineering Mechanics, KTH)
Prof. Shiva Sander Tavallaey, Division of Engineering Mechanics, KTH「The Dual Transition: Electrification & Digitalization - an ABB perspective」(14 to 16 min talk, 3 to 5 min Q&A per person)※本プログラム記載事項は現時点での情報であり、今後変更となる可能性がございます。

Kyushu University Energy Week 2024

Joint Symposium between KTH and Kyushu University“Digital Revolution for a Sustainable Future”
Common terms such as AI, machine learning, and big data now hold immense potential. The field of data
science, powered by AI, is emerging and can revolutionize society by using AI to analyze large datasets.
This technological advancement enables efficient energy generation, storage, and environmentallyresponsible consumption. In transportation, we are on the brink of a shift towards centrally managed,
wirelessly connected vehicles and widespread car-sharing programs. This symposium will explore advanced
research in digitalization within the mathematical and information sciences. Topics covered include
electricity demand forecasting, AI-driven smart grid enhancement, industrial implications of electrification
and automation, cybersecurity, and information mathematics to improve the reliability of MIMO technology.

Speaker
Title

Robust Peer-to-Peer Energy Systems Against Cyber-Attacks

Dr. Nguyen Dinh Hoa
Associate Professor
International Institute for Carbon-Neutral Energy Research (I2CNER), Kyushu University, Japan
Institute of Mathematics for Industry (IMI), Kyushu University, Japan
Abstract

Peer-to-peer (P2P) energy systems are being considered as an evolutionary means to transform conventional energy
systems into cleaner, more sustainable and smarter ones, where both conventional generators and consumers as well as
prosumers equipped with renewable and distributed energy resources can participate. One of the key features in such
P2P energy systems is the capability of direct energy trading between participants, without any central entity andindependent of geographical locations. This is enabled by a bidirectional and secured information exchange network
through which the energy trading price and amounts are negotiated. This talk presents basic properties of the optimal
solution of a social welfare maximization problem in such P2P energy systems and some algorithms for countermeasuresagainst cyber-attacks on inter-participant communications.
Title

AI for Energy Security: A Blessing or a Threat

Dr. György DánProfessor
DIVISION OF NETWORK AND SYSTEMS ENGINEERING, KTH Royal Institute of Technology, Sweden
Abstract

Machine learning an AI have found numerous applications in the area of energy systems recently, from building energy
managements systems, through controlling the cooling of data centers to state estimation and solving optimal power flowin power systems. While there is no one-size-fits all ML solution to these problems, well designed ML solutions often
outperform algorithmic approaches under computing time constraints. This raises the question whether they are equallyeffective in computing attacks against these systems. In this talk we discuss recent results that show that this is indeed
the case, and through the examples of automatic generation control and secondary control of microgrids we highlight thepressing need for assessing the adversarial robustness of optimization and control schemes in power systems.
Title

Analysis of Alternating Projection Method for the Nontransverse
Intersection of Convex Sets
Dr. Hayato Waki
Associate Professor
Institute of Mathematics for Industry (IMI), Kyushu University, Japan
Abstract

Optimization is fundamental in science, engineering, decision-making, machine learning, and AI. Several algorithms havebeen proposed for one class of optimization problems. Therefore, understanding the theoretical properties of eachalgorithm is essential for algorithm selection. The well-known alternating projection method and related algorithms forconvergence speed evaluation will be presented in this talk. These algorithms find a point in the intersection of two given
convex sets. If the intersection of these sets does not contain any interior, i.e., these sets do not intersect transversally,
it is known that the convergence rate can be sublinear. However, a more rigorous estimation still needs to be made clear.
In this talk, we would like to present results on the convergence rate of these algorithms found in our joint work with
Hiroyuki Ochiai and Yoshiyuki Sekiguchi.

Kyushu University Energy Week 2024

SpeakerTitle

Data-Driven Modelling and Coordination of Sustainable Power Systems
with Smart Converters
Dr. Qianwen Xu
Assistant Professor
Division of Electric Power and Energy Systems, KTH Royal Institute of Technology, Sweden
AbstractMoving towards climate neutral society, future power grids will have a high share of renewable energy. As power
converters serve as the interface of renewable energy sources into the grid, future power systems are power converterdominated grids. However, grid transition brings new challenges. First, the grid has high volatility and uncertainty.
Second, there are stability issues due to the interactions of grid-connected converters. Moreover, with large-scale
integration of power conver ters, to achieve optimal coordination under renewable fluctuations requires a highcommunication and computation burden. To address these challenges, this talk will present novel data-driven modellingand coordination strategies for sustainable power systems with smart converters.
Title

Modern Statistical Theories for Energy Demand Forecasting

Dr. Kei Hirose
Professor
Institute of Mathematics for Industry (IMI), Kyushu University, Japan
Abstract

Modern statistical theories, such as high-dimensional statistics and double descent, have recently attracted the attentionof mathematicians and statisticians. From a mathematical viewpoint, these theories are quite complex, and theirapplication is challenging. However, such techniques are valuable for energy demand forecasting. In this talk, I will
demonstrate how the modern statistical theories can be applied to energy data analysis.
Title

The Dual Transition: Electrification & Digitalization - an ABB perspective
Dr. Shiva Sander Tavallaey
Senior Principal Scientist
Applied Analytics, AI Lead, ABB, SwedenAdjunct Professor
Division of Engineering Mechanics at the Marcus Wallenberg Laboratory for Sound and Vibration, KTH
Royal Institute of Technology, Sweden
Abstract

The first wave of digital transformation, centered on industrial IT, significantly impacted technical solutions and industrial
automation development. Despite initial infrastructure limitations, the hype drove industrial automation toward
standardizing communication protocols, emphasizing IT and OT importance in factories. By the late 90s, major
automation suppliers provided already different types of asset management systems; rather advanced CMMS with
automatic workorder generation, etc. The main business drivers then were to increase productivity, quality and efficiency.
The digitalization and maturity of industrial IoT, sensor technology, access to webcams, market data, cheap computational
power, and sophisticated and novel algorithms are today providing the opportunity to take the next step in the evolution ofindustrial automation, but this time the key drivers of the last decades, although still relevant, have been left behind in thepriority list! Sustainability and resource management are today the main drivers of industrial transformation, with energy
being by far the most important and critical resource. Achieving the zero-emission vision, which can roughly be describedas“electrify everything”, requires a competent system and solution package that can handle not only the new types ofloads but also the various flexibility requirements - often in real time - that this complex problem requires. In this shorttalk, I will try to highlight how ABB, as a supplier of products and system solutions, contributes to a sustainable andresource-efficient industry.