Short description:
Energy Systems is an interdisciplinary educational track at Skoltech with focus on Power Systems Control and Optimization, Smart Grids, Energy Conversion, Power Electronics and Energy Markets. The broad educational program builds upon core and target area courses taught by faculty members of Center for Energy Science and Technology and elective courses delivered by members of other CREIs including Skoltech Center for Artificial Intelligence Technology (Skoltech AI) and Center for Digital Engineering. Following major trends in energy sector the program also delivers an interdisciplinary vision of energy systems as coupled electric/heat/gas infrastructures that is unique for the universities in Russia and beyond. Other core aspects of the program are industry immersion and the entrepreneurship and innovation component. Skoltech offers international environment and the opportunities for students to visit foreign universities and international conferences.
The education program consists of core and elective courses with recommendations for two tracks – Energy systems and Energy transition and ESG as detailed below as detailed below:
Core courses
Course instructor: Prof. Elena Gryazina Description: The aim of this is to recap the basic topics that you are expected to get at bachelor level. If you do not feel confident with basic matrix manipulations, integration and differentiation – you should definitely take this course; fluency in these is a must for an educated engineer. At the first day of studies you’ll take the preliminary exam to make final decision regarding taking this course.
Course instructor: Prof. Dmitry Titov Course instructor: Prof. Elena Gryazina, Sergey Parsegov, Oleg Khamisov Description: This course covers power systems analysis & operations, including fundamentals (balanced three-phase power) steady-state analysis (power flow), state estimation, operation (optimal power flow), security (contingency analysis and security constrained optimal power flow), power system dynamics (frequency and voltage control) and challenges and trend of future power systems. After successfully completing this course, the student will be capable of analyzing the technical and economic operation of an electric energy system. Course instructor: Prof. Federico Ibanez Course instructor: Prof. Elena Gryazina Description: This research seminar is the general meeting for faculty, researchers and master and PhD students of Energy Systems programs. The seminar takes place every week during Terms 2(6)-3(7)-4(8). Master students must attend the seminar at least for one academic year but welcome to attend during two years. PhD students are welcome to attend the seminar during all years of studies but can gain no more than 6 credits in total. The seminar consists of faculty lectures, invited lectures of top scientists in their research field as well as students’ reports on their own or examined papers. To PASS the course and gain 3 credits per academic year the student must fulfill all three requirements: 1. Attendance: > 2/3 of seminars. 2. Presentation. Depending on the status: 3. Active participation in the discussion. Being able to pose a question for every presentaion. The core of the self-study activity will be preparation to the talk that is comparable to project implementation (a significant part of many regular courses). The students are expected to assign at the beginning of Term 2/6 and may drop the seminar till the beginning of Term 3/7 while credits are provided in Term 4/8. Recommended electives in power systems and power electronics Course instructor: Marina Dolmatova Description: This course explores the complex landscape of power system economics, electricity markets and the regulatory frameworks that govern them. After covering the fundamentals of microeconomics and auction theory, the main types of electricity markets and regulations are discussed. The course covers all stages of market procedures from capacity markets, trough unit commitment to real-life market performance, touching Economic dispatch and Optimal Power Flow with Locational Marginal Pricing. The course addresses current trends in electricity markets and reviews the challenges associated with technological advancements and evolution of market participants’ roles. The course also explores the aspects of Russian electricity market design and illustrates general performance issues using it as a case study. Course instructors: Irina Gayda Description: The course is designed to help students understand key strategic challenges that are facing energy companies and influencing their investment and risk management decisions. Students will discuss technological sanctions, re-orientation toward new export markets, climate change and climate regulation, technological innovation. During the course, students will assess impact of climate change on their company/ country, play a climate negotiation game, develop recommendation for strategy update of an energy company. Course instructor: Federico Martin Ibanez Description: Power systems around the world are undergoing a period of unprecedented change. A typical 20th Century power system was characterized by unidirectional flow of power from a limited number of large controllable power stations to a highly predictable demand. There was no energy storage so that at any time generation had to be equal to demand and the infrastructure utilization rates were low (about 55% for generation, 30% for transmission and even lower for distribution). Generally planning and controlling such a system was relatively straightforward as it was based around principles of deterministic hierarchical control, usually based on (N-1) reliability criterion. On the other hand the emerging 21st Century power system is characterized by bidirectional flows between a very large number of uncontrollable and stochastic generators (usually, but not always, renewable ones such as wind or solar) and stochastic and often poorly-predictable demand. Demand ceases to be predictable as it consists of consumers equipped with smart meters and wind/solar generators hence possibly becoming net generators – so-called prosumers. Increased penetration of energy storage, both stationary and mobile due to a take-up of electric vehicles, offers buffering possibilities in dispatch (generation does not have to be equal to demand at any time). Controlling such a power system is the main research challenge in power systems and it is made possible by latest advances in ICT (Information and Control Technology), communication networks, Internet, GPS, sensors, etc. However it requires new tools and methodologies, the Smartgrid course will give the basis of this new grid scenario. The course will present the details of solar generation, wind generation, energy storage systems and how to put all of them together in a controllable grid. The course is mainly focused on the interfase between the source of energy and the grid, or in other words the power electronic converters. Recommended electives in data science and computational engineering Course instructor: Prof. Elena Gryazina Description: The aim of this is to recap the basic topics that you are expected to get at bachelor level. If you do not feel confident with basic matrix manipulations, integration and differentiation – you should definitely take this course; fluency in these is a must for an educated engineer. At the first day of studies you’ll take the preliminary exam to make final decision regarding taking this course.
Students will learn how to analyze strategies in the field of digitalization of power grid companies and provide an expert assessment of the chosen strategy, taking into account the technological maturity of the company, as well as other internal and external factors.
Description: The course provides an overview of the latest achievements in power electronics. The main purpose of the course is to analyze different circuit topologies, to understand how they work and which are their benefits and limitations. The course starts with reviewing the basics in electric circuit theory, and then, it introduces different kind of semiconductor devices such as diodes, thyristors and transistors. After this, power electronics circuits are presented: rectifiers, DC-DC converters and inverters. The course gives the tools to analyze any kind of power converters, and provides different examples related with microgrids and energy storage applications. It has three parts: lectures, home tasks and experimental activities in the lab. By the end of the course, the students should be able to analyze a power converter, to simulate it and to understand the possible applications.
a. For the 1st year masters: deep understanding of the given paper including reproduction of some numerical simulations
b. For the 2nd year masters: literature review on the research topic including personal novel results (extended pre-defense format)
c. For PhD: personal research results. PhD students are also recommended to prepare a quiz on the topic.
Course instructors: Prof. Mikhail Belyayev, Prof. Maxim Panov Description: The course gives an introduction to the main topics of modern data analysis such as classification, regression, clustering, dimensionality reduction, reinforcement and sequence learning, scalable algorithms. Each topic is accompanied by a survey of key machine learning algorithms solving the problem and is illustrated with a set of real-world examples. The primary objective of the course is giving a broad overview of major machine learning techniques. Particular attention is paid to the modern data analysis libraries which allow solving efficiently the problems mentioned above. Course instructor: Prof. Andrey Somov Description: In the last decade the Internet of Things (IoT) paradigm has slowly but steadily and increasingly permeated what researchers and engineers study and build. The term “Internet of Things” doesn’t have a single definition and people today often use it to interchangeably refer to Wireless Sensor Network (WSN), Machine-to-Machine (M2M), Web of Things (WoT) and other concepts. The focus of this course is to learn about these technologies that will be extending the Internet as we know it and use it today, to interconnect not only people and computers but also sensors and associated objects. The course will be divided into two strongly coupled parts. The first part of the course covers the IoT ‘pillar’ technologies, i.e. embedded systems, wireless sensor networks, semantic technologies, and theory behind them while the second part will have a special focus on IoT development, i.e. IoT apps, open platforms, sensors and actuators, software/middleware. Apart from covering the theory behind the IoT and “how to connect things to the Internet”, the course will therefore also engage the students to demonstrate the feasibility of simple IoT real applications and will challenge them to improve their applications through the use of cognitive technologies and cloud computing.
Course instructor: Prof. Elena Gryazina Description: The course is devoted to optimization methods and optimization problems design with a special attention to those motivated by data science, engineering and industrial applications. The course starts with a brief reminder of the foundations of convex analysis. Then we discuss zero, first and second order methods with a special focus on their efficient implementation. We distinguish between various problem classes, discuss suitable methods for every class. The problem formulation and its proper reformulation is the critical issue for an optimizer. We’ll learn about optimization models and convex relaxations. Special attention will be addressed to Linear Matrix Inequalities (LMI) that arise in optimization problem formulations. One of the home assignments is devoted to understanding the constraints of performance for different software packages. At the last part of the course we move further to advanced first order optimization methods such as proximal mirror descent and extra gradient methods and discuss how to utilize problem structure (e.g. sparsity and separability) to speed up the methods. Within engineering and practical sections, we show how to use the methods above to crack convex and non-convex problems arises in engineering, energy systems, machine learning and related fields.
Course instructor: Prof.Evgeny Burnaev Description: The course is a general introduction to machine learning (ML) and its applications. It covers fundamental topics in ML and describes the most important algorithmic basis and tools. It also provides important aspects of the algorithms’ applications. The course starts with an overview of canonical ML applications and problems, learning scenarios, etc. Next, we discuss in-depth fundamental ML algorithms for classification, regression, clustering, etc., their properties, and practical applications. The last part of the course is devoted to advanced ML topics such as Gaussian processes, neural networks. Within practical sections, we show how to use the ML methods and tune their hyper-parameters. Home assignments include the application of existing algorithms to solve data analysis problems. The students are assumed to be familiar with basic concepts in linear algebra, probability, real analysis, optimization, and python programming.On completion of the course students are expected to: Recommended electives in natural sciences Course instructor: Prof. Henni Ouerdane Description: Classical thermodynamics is useful to describe equilibrium states, while non-equilibrium states and irreversibility characterize real physical processes. If one is interested in actual processes at work during energy conversion, a classical thermodynamic description of equilibrium states is insufficient as it yields very incomplete information on the processes. Irreversible thermodynamics accounts for the rates of physical processes, and provides relationships between “measurable quantities” such as transport coefficients. This graduate course, which constitutes the natural continuation of the course Energy Systems Physics & Engineering, provides the students with basic knowledge of out-of-equilibrium and finite-time thermodynamics, which describe irreversible processes that routinely take place in physical systems and permits a fine understanding of the processes ensuring energy conversion. Thermoelectric generators serve as the main example to illustrate in a simple fashion the out-of-equilibrium formalism, and other systems such as, e.g., solar cells are studied. Essential notions which are taught include: Onsager’s approach to linear nonequilibrium thermodynamics; coupled transport theory; Boltzmann equation; thermal conductivity; electrical conductivity; electrochemical potential in solid-state systems; force-flux formalism and its application to thermoelectric systems; device optimization modelling accounting for dissipative coupling to heat reservoirs; solar energy conversion. The course is organized around the learning of essential concepts and an awareness development of current energy technologies. It is based both on “teaching with lecture” and “teaching with discussions” methods. In addition to home assignments and project, students will solve problems during tutorials and discuss their solutions. Course instructor: Prof. Henni Ouerdane Description: Classical equilibrium thermodynamics is a theory of principles, which provides a framework to study means to produce motive power and useful heat, crucial for our everyday life. It is a pillar of any serious physics and engineering curriculum. This graduate course provides the students from possibly diverse backgrounds with the theoretical concepts that underlie the physics of energy conversion at the heart of heat engines operation, including chemical processes, and the specific knowledge of energy technologies in use nowadays. Covering some of the main real-world technologies for the generation of electric/mechanic, heating and cooling power, students will learn to critically analyze and assess these technologies to improve their performance and imagine innovative and commercially viable solutions to energy problems, accounting for costs and environmental aspects like pollutants formation and their abatement. Essential notions which are taught include: energy conversion; heat transfer; work; first and second principles; working fluids and thermoelastic coefficients; thermodynamic cycles; motors and refrigerators. The course is organized around the learning of essential concepts and an awareness development of current energy technologies. While it is based both on “teaching with lecture” and “teaching with discussions” methods, report writing and oral presentations are a key element of the learning experience. In addition to home assignments and projects, students will solve problems during tutorials and discuss their solutions. Recommended electives for track “Energy transition and ESG” Course instructor: Alexey Cheremisin Description: The course is an introduction to Petroleum Engineering and gives an overview of Petroleum Engineering and its various components and their internal connection. The course will address the story of oil from its origin to the end user. The objective is to provide an overview of the fundamental operations in exploration, drilling, production, processing, transportation, and refining of oil and gas. Course instructor: Vladimir Istomin Description:Natural gases characterization of the gas and gas-condensate fields and overview of technological complications (flow assurance) at different stages of field development. Gas hydrates: physical and chemical properties, two- and three-phase equilibria, quadrupole points, phase diagrams of gas and the gas-condensate systems including water, ice and hydrates; gas hydrate control, thermodynamic (methanol, MEG, electrolytes) and low-dosage (kinetic and anti-agglomerates) inhibitors. Hydrate control at gas wells, gas gathering systems and the main technological processes of natural gas treatment in field conditions: dehydration of lean gases (absorption and adsorption); low-temperature processes of gas treatment at gas-condensate fields. Course instructor: Prof. Dmitry Dylov Description:In the computational era of everything, imaging has not become an exception. Computational algorithms allow both to extract valuable information from a scene and to improve the very sensor that forms the image. Today, computational and image processing enhancements became integrable parts of any digital imager, be it a miniature smartphone camera or a complex space telescope. This crash course is designed as a prerequisite for those students who would like to venture into the field of Computer Vision. We will cover foundational mathematical equations that are involved in the image formation and in the geometric projection principles. The concept of Point Spread Function that distorts the object will be explained on particular examples and will be experimented with for the tasks of image reconstruction and denoising. Image processing will be covered with an emphasis on the Python libraries to be used in the rest of the imaging-related courses on the DS/IST tracks (openCV and others). A basic DSLR photo camera will be considered as a model for understanding Fourier Imaging and Filtering methods in a laboratory exercise. Hands-on tutorials on how to select a camera and a lens for your machine vision application will be provided. The theory of color and stereo light-field cameras will be covered using the models of commonplace Bayern RGB sensors; as well as state-of-art spectral and multi-lens imagers. The course will consist of three theoretical lectures riffled by three graded in-class laboratory coding sessions on the subjects covered in the theoretical lectures. 100% attendance is mandatory. There will be a single in-class exam during the evaluation week and no homework.
Other core courses and activities Master thesis Skoltech professors offering Master projects in Energy Systems are: Assistant Prof. Elena Gryazina Assistant Prof. Federico Martin Ibanez Assistant Prof. Henni Ouerdane Associate Professor of the Practice Dmitry Titov Associate Professor of the Practice Marina Dolmatova Additional information can be found here: The program is managed by:
model selection, model complexity among others;
– Have an understanding of the strengths and weaknesses of many popular ML
approaches;
– Appreciate the basic underlying mathematical relationships within and across
ML algorithms and the paradigms of supervised and unsupervised learning.
– Be able to design and implement various machine learning algorithms in a range of
real-world applications.
As additional topics it is planned to consider Permafrost Engineering and Flow Assurance, which are actual for Russian Oil&Gas Industry. Within the framework of the course it is planned to invite speakers from industry.