应自动化学院邀请，俄罗斯科学院Denis Sidorov教授和德国TU多特蒙德大学Ulf Häger研究员等一行5人受邀参加2019年6月20日自动化学院智慧能源系统控制研究所召开的专题“Mathematical Modeling and Control Strategies of Renewable Energy Sources”研讨会，并作报告。
报告时间：2019年6月20号14:00-18:30
报告地点：中南大学民主楼210
报告安排如下：
俄罗斯科学院Denis Sidorov教授，题目：Methods of analysis of differential-algebraic and integral models: stability, branching and blow-up
TU多特蒙德大学Ulf Häger研究员，题目：Curative congestion management in transmission grids
俄罗斯科学院Daniil Panasetsky副教授，题目：Power flow calculation program for hybrid AC/DC networks，
TU多特蒙德大学Jonas Maasmann博士，题目：Methods, concepts and behavior of grid supporting charging infrastructure
俄罗斯科学院Aleksei Zhukov博士，题目：Mathematical methods of wind generation and power demand forecasting for modern power systems
报告摘要：
1.Nonlinear differential-algebraic equations and integral equations are in the core of time domain simulation in various fields. This talk focuses on the nonlinear dynamic models analysis formulated as the systems of differential-algebraic and integral equations. The sufficient conditions of the global classical solution's existence are formulated on both cases. The problem of numerical solution is addressed. Application of the integral dynamical models to load leveling problem is considered.
2.When applying curative congestion management, the N-0 case is set in such a way that transmission lines in the system are used to higher capacity than with classic N-1 security. To allow for curative N-1 security it is ensured that a curative measure is available for every possible N-1 case that would lead to a line overload. If such an N-1 case occurs, the curative measure is executed immediately and automatically in order to eliminate the line overload in the network.any countries are currently planning to build Power2Gas (P2G)-plants in order to couple the electricity and gas sectors and to feed excess quantities of RES into the gas grid instead of curtailing them. If the P2G systems are suitably placed in the electricity grid and can be regulated by the transmission system operator, they can be used for congestion management purposes.n the context of this presentation, the different possibilities for curative congestion management will be presented and a practical example will be given. The automation requirements of this concept will be discussed.
3.Multi-terminal hybrid AC/DC power systems have found a wide application in different areas, such as wind and solar energy, micro grids, shipboard power system, etc. The power flow calculation for hybrid EPS is the foun-dation for solving any planning and control tasks. The report presents the program for steady-state analysis of hybrid AC/DC networks. Typically, two types of power flow methods for hybrid AC/DC systems are considered: the unified methods, which solve AC and DC power flow simultaneously, and the sequential methods, which solve AC and DC networks separately in an iteration manner using an interaction procedure. The advantage of the sequential method is that it does not need to change the conventional AC power flow routines and there is an opportunity to use open source ready-made software packages, such as Matpower. However, the sequential approach may lead to convergence problems, and a worse convergence rate in comparison with the unified methods. The program presented in the report implements the unified method, it allows to perform calculations for networks of arbitrary configuration. Particular attention was paid to the speed, as well as the possibility of using software code for third-party tasks (dynamics, optimization, etc.).
4.In the future, electric vehicles will make a contribution to reducing green-house gas emissions. The challenge is to charge electric vehicles with climate neutral energy from the power grid. In this presentation, the relevant basics are specified first, whereupon the research approach of the Virtual Direct Link for different application cases is defined. Subsequently, a concept for the application case Remote-Self- Consumption is defined and analysed. Based on this example application, a simulation shows the local and global effects of the Virtual-Direct-Link on the power grids and derives the dynamic boundary conditions. Finally, the technical feasibility of the deduced concept is demonstrated through a laboratory experiment and a field test using a prototype system.
5.Renewable energy sources have an increasingly important role in modern electric power systems. Increasing penetration of renewable energy sources make necessary to take its variable nature into account. On the other hand, electricity market rules and social processes lead to complica-tion of electric power demand. Due to these factors problem of generation and consumption balance maintaining have become difficult. In this case stable operation of the power system highly de-pends on forecasting ac-curacy. In order to achieve high accuracy, an intelligent forecasting method based on decision tree ensemble methods is proposed to obtain the more accurate forecast in comparison with traditional approaches. Proposed models use not only a retrospective data but multiple input variables with different nature. Approbation of the developed approach was carried out on the real data. Comparison with the popular machine learning approaches like SVR and ANN are provided.
报告人简介：
1.Denis Sidorov is an Leading Researcher of Operations Research Laboratory of Energy Systems Institute of Russian Academy of Sciences. He has completed his PhD in 1999 and Habilitation (DSc) in 2014. He is a Distinguished Guest Professor of Hunan University, Changsha, China and Visiting Research Professor of QUB, UK. He has held Vision Engineer Lead position at ASTI Holdings Pte Ltd, Singapore (2005-2008), Research Fellow ay Trinity College Dublin, Ireland (2001-2002) and CNRS, Compiegne, France (2003-2004). He is IEEE Senior Research Fellow, Expert of the Russian Science Foundation and the Russian Foundation for Basic Research. Reviewer of Mathematical Reviews and Zentralblatt fur Mathematik. Dr Sidorov is IEEE Chapter Chair of IEEE PES Russia. He serves as Member of the Editorial Boards of the journals: Renewable and Sustainable Energy Reviews (Elsevier, Q1); International Journal of Artificial Intelligence (Scopus Q2). He gained Publons Award as one of the top 1 per cent of peer reviewers in Engineering in 2018 and Publons Award "Sentinels of Science" in Mathematics in 2016. His main research interests include machine learning, power engineering, image processing, numerical methods and integral equations. He is Professor of Russian Academy of Sciences.
2.Ulf Häger studied electrical engineering at TU Dortmund University and received his PhD degree in 2012 at the ie³ at TU Dortmund University. From 2009 until 2012 he was responsible for the coordination of the FP7 project ICOEUR with 20 partners from Europe and Russia. This project has developed new and innovative methods for monitoring, control and protection of large scale transmission grids. From 2011 until 2016 he was head of the group for transmission and distribution grids of ie³. Since 2016 he is responsible for the overall coordination of research projects at ie³. His research is focused on the grid integration of new distributed generation devices, loads and storages. He has a strong focus on the interactions between distribution and transmission grids considering flexibility elements, such as FACTS or HVDC devices and PST, in network planning and design.
3. Daniil Panasetsky was born in Bodajbo, Russia in 1983. He received the B.Sc. and Ph.D. degrees in 2006 and 2014, respectively, from Irkutsk State Technical University (ISTU) and Melentiev Energy Systems Institute (MESI), Irkutsk, Russia. Since 2009 he is a senior researcher in MESI. Since 2014 he is an associated professor of electrical engineering in ISTU. His research interests include power system stability, emergency control, ac/dc converters and application of artificial intelligence to power systems.
4.Jonas Maasmann graduated his studies at the TU Dortmund University in electrical engineering and received his phd also at TU Dortmund University. From there he in working as a research associate at ie³ and project manager in the cooperation projects TIE-IN, ZAESAR and SyncFueL with his research focus Electric Vehicle integration in Smart Buildings under considering distribution grid. As the head of the research group Smart Grid Technology Lab and Electric Mobility he coordinates the research in the SGTLab. Together with the University of Queensland, Brisbane, Australia he worked on a research project “Integration of Electric Vehicle in low performed grids” and he is member of the standardization committees DKE/AK 901.0.1 “NeLDE”
5. Aleksei Zhukov was born in Irkutsk, Russia, in 1991. He gained the MSc degree in physics in 2013 from Irkutsk State University, Russia. In 2014-2016, he was an engineer at Energy Systems Institute SB RAS. Since 2017, He is a research fellow in Institute of Solar-Terrestrial Physics SB RAS. His research interests include: machine learning algorithms and methods and its application in power systems and time series forecasting. A.V. Zhukov is the author and co-author of more than 30 scientific papers.
