- Skoltech:
- About
- Admissions
- Education
- Research
- Innovation
- Faculty
- Industry
- Student Life
- Colloquia

Adjunct Professor

**Center for Energy Systems**

Prior to coming to Skoltech, Michael worked at the Los Alamos National Laboratory as a technical staff member in the Theoretical Division. Before that, he spent three years at Princeton University as a R. H. Dicke Fellow in the Department of Physics. He has been a visiting scholar at five universities, is a fellow of the American Physical Society and has published more than 100 papers.

Michael co-chaired the IEEE SmartGridComm symposium in 2012 and has co-organized more than 20 workshops and conferences. He is an editor of the Journal of Statistical Mechanics and is a referee for more than 15 scientific journals. He has a patent for methods and optical fibers that decrease pulse degradation resulting from random chromatic dispersion.

Michael’s areas of interest include applied and theoretical problems in power systems, and infrastructure networks with a focus on power grids. He also researches statistical and mathematical physics applied to hydrodynamics, optics, information theory, computer science and communication. He is currently leading “Physics of Algorithms” and “Optimization and Control Theory for Smart Grids” projects at the Los Alamos National Laboratory.

He holds bachelor’s and master’s of science degrees in physics from Novosibirsk State University and a Ph.D. in physics from the Weizmann Institute of Science.

- applied and theoretical problems in power systems
- infrastructure networks with a focus on power grids
- statistical and mathematical physics applied to hydrodynamics, optics, information theory, computer science and communication

- K. Turitsyn, P. Sulc, S. Backhaus, and M. Chertkov, Options for Control of Reactive Power by Distributed Photovoltaic Generators, Proceedings of IEEE, Proceedings of the IEEE 99 (6), 1063-1073 (2011).
- M. Chertkov, F. Pan, and M. Stepanov, Predicting Failures in Power Grids: The Case of Static Overloads, IEEE Transactions on Smart Grids 2, 150 (2010).
- M. Chertkov and V. Chernyak, Loop series for discrete statistical models on graphs, JSTAT/2006/P06009.
- M. Chertkov and M.G. Stepanov, An Efficient Pseudo-Codeword-Search Algorithm for Linear Programming Decoding of LDPC Codes, IEEE Transactions on Information Theory 54, 1514 (2008).
- M. Chertkov, Phenomenology of Rayleigh-Taylor Turbulence, Phys. Rev. Lett. 91, 115001 (2003).
- V. Chernyak, M. Chertkov, I. Gabitov, I. Kolokolov, and V. Lebedev, PMD induced fluctuations of Bit-Error-Rate in optical fiber systems, Journal of Lightware Technology 22, 1155 (2004).
- A. Pumir, B. Shraiman, and M. Chertkov, The Lagrangian view of energy transfer in turbulent flow, Euro. Phys. Lett.56, 379 (2001).
- M. Chertkov, Instanton for random advection, chao-dyn/9606011, Phys. Rev. E 55, 2722 (1997)
- M. Chertkov, G. Falkovich, I. Kolokolov and V. Lebedev, Normal and anomalous scaling of the fourth-order correlation function of a randomly advected passive scalar, Phys. Rev. E 52, 4924 (1995).
- M. Chertkov, G. Falkovich, I. Kolokolov and V. Lebedev, Statistics of a passive scalar advected by a large-scale 2D velocity .eld: analytic solution, Phys.Rev.E 51, 5609 (1995).

- J.R. Oppenheimer Fellowship at Los Alamos National Laboratory (1999)
- Prize of the Feinberg Graduate School (1996)
- R.H. Dicke Fellowship at Princeton (1996)
- Prize of the Charles Clore Israel Foundation (1995)

Number of ECTS credits: 6

Course Classification: Science, Technology, and Engineering

Course Classification: Science, Technology, and Engineering

Course description:

The course shall be considered as a “soft” and self-contained introduction to modern “applied probability” covering theory and application of stochastic models. Emphasis is placed on intuitive explanations of the theoretical concepts, such as random walks, law of large numbers, Markov processes, reversibility, sampling, etc., supplemented by practical/computational implementations of basic algorithms. In the second part of the course, the focus will shift from general concepts and algorithms per se to their applications in science and engineering with examples, aiming to illustrate the models and make the methods of solution clear, from physics, chemistry, machine learning, control and operations research discussed.Prerequisites:

Solid preparation in practical math (ability to solve problems in linear algebra, calculus, and differential equations) will be required from anybody taking this course. Basic “user” knowledge of a high-level scientific programming language (matlab, python, julia or mathematica) will be needed for completing homework assignments.

The course shall be considered as a “soft” and self-contained introduction to modern “applied probability” covering theory and application of stochastic models. Emphasis is placed on intuitive explanations of the theoretical concepts, such as random walks, law of large numbers, Markov processes, reversibility, sampling, etc., supplemented by practical/computational implementations of basic algorithms. In the second part of the course, the focus will shift from general concepts and algorithms per se to their applications in science and engineering with examples, aiming to illustrate the models and make the methods of solution clear, from physics, chemistry, machine learning, control and operations research discussed.Prerequisites:

Solid preparation in practical math (ability to solve problems in linear algebra, calculus, and differential equations) will be required from anybody taking this course. Basic “user” knowledge of a high-level scientific programming language (matlab, python, julia or mathematica) will be needed for completing homework assignments.

Description

The course will provide a graduate level overview of modern power systems, with a major emphasis on the system aspects of power production, transmission, storage and delivery. All stages of the power energy technical chain will be thoroughly reviewed from a comprehensive engineering prospective but also from the standpoint of fundamental physics principles/equations. A special emphasis will be given to improving students’ ability to extract well-formulated physics and mathematics problems from power engineering reality.

The course will rely on strong undergraduate math/physics background of the students, however no background in power systems will be assumed or required. In this course we will advance gradually through major principles of the power system design (with some history re-course), discussion of major elements of the power systems (generators, power lines, transformers), analyze state estimation, optimization, control and design practice, and will conclude with discussions of modern engineering, physics and mathematics challenges associated with the emergent smart/intelligent/resilient grid technologies.

The course will rely on strong undergraduate math/physics background of the students, however no background in power systems will be assumed or required. In this course we will advance gradually through major principles of the power system design (with some history re-course), discussion of major elements of the power systems (generators, power lines, transformers), analyze state estimation, optimization, control and design practice, and will conclude with discussions of modern engineering, physics and mathematics challenges associated with the emergent smart/intelligent/resilient grid technologies.

Upon completion of this course the student will be able to:

- Explain the general picture of the spatio-temporal scales and magnitude of energy production, transfer, storage and consumption in power systems at both transmission and distribution levels and related energy infrastructures.
- Explain basic operation principles and physics behind individual components and power system as the whole.
- Formulate optimization, control and planning problems in power systems.
- Select and apply the appropriate mathematical tools and algorithms to solve optimization, control and planning problems.
- Discuss major elements of the power systems (generators, power lines, transformers).
- Analyze state estimation, optimization, control and design practice.
- Discuss modern engineering, physics and mathematics challenges associated with the emergent smart/intelligent/ resilient grid technologies.

ФИО: Чертков Михаил Викторович

Занимаемая должность (должности): Профессор

Преподаваемые дисциплины: Введение в энергетические системы

Ученая степень: PhD, 1996 г., физика, Институт Вейцмана, Израиль; кандидат физико-математических наук, 1990, Новосибирский государственный университет

Ученое звание (при наличии): нет

Наименование направления подготовки и/или специальности: Физика

Данные о повышении квалификации и/или профессиональной переподготовке (при наличии): нет

Общий стаж работы: 19 лет

Стаж работы по специальности: 19 лет