Objectives and competences
Objectives:
To present fundamental concepts and tools of software in energy, including modeling of energy processes, numerical methods, simulations, process digitization, and the role of mathematical models.
To enable practical application of acquired knowledge, particularly in the development and use of mathematical models, and their implementation in simulation environments.
To provide students with insights into various tools and technologies in the field of energy, such as Matlab/Simulink/Simscape, Python, SCADA systems, and others. The goal is for them to understand how these tools contribute to improving the understanding, management, and optimization of energy systems.
To foster the development of analytical skills and the ability to solve practical problems in the energy industry, especially through an understanding of numerical methods and their application in modeling and simulations.
Competences:
Modeling of Energy Processes: Development of mathematical models to describe energy systems.
Numerical Methods: Application of numerical methods to solve engineering problems in energy.
Simulations and Software: Use of tools such as Matlab/Simulink/Simscape for modeling and analysis of energy systems.
Practical Model Implementation: Development and implementation of models to solve practical energy problems.
Python Programming: Basic programming knowledge in Python for data analysis and solving energy challenges.
Understanding of SCADA Systems: Understanding the use of SCADA systems for monitoring energy processes.
Analytical and Problem-Solving Skills: The ability to analyze and solve complex energy problems.
Adaptation to Technological Advances: The ability to adapt and use the latest tools and approaches in energy.
Communication: The ability to communicate clearly with teams and experts in the energy industry.
Teamwork: The ability to collaborate in teams to address energy challenges.
Critical Thinking: Developing critical thinking and making informed decisions in energy
Content (Syllabus outline)
Introduction to the subject:
Explanation of concepts such as modeling of energy processes, numerical methods, simulation tools, and process digitization.
The role of mathematical models in the analysis of energy systems and examples of software applications in the energy sector.
Fundamentals of Modeling Energy Systems:
Reasons for using modeling in energy and basic modeling concepts.
Types of models and practical applications.
Application of modeling in the energy industry and model validation.
Modeling Electrical, Electromagnetic, Mechanical, and Thermal Systems:
Modeling various energy systems, including electrical, mechanical, and thermal systems.
Practical use of models in the design of energy devices.
Numerical Methods:
The importance of numerical methods in solving engineering problems.
Basics and use of numerical algorithms, including solving differential equations.
Simulations and Simulation Software in Energy:
An overview of simulation tools, their advantages, and further development.
Trends in the use of simulations in the energy sector.
Using the Simulation Tool Matlab/Simulink:
Using Simulink for modeling and analyzing energy systems.
Using Matlab/Simscape Software:
Advantages of using Simscape for more complex simulations in the energy sector.
Introduction to the Python Programming Language:
Basics of Python, including syntax and control statements.
Using Python for various tasks.
Using Python in Matlab:
Running Python code in MATLAB and exchanging data between the two environments.
Visualizing the results of Python code in MATLAB.
SCADA in Energy Systems:
The importance of SCADA systems for monitoring and managing energy systems.
Components of the SCADA system, data acquisition and analysis, and security.
Learning and teaching methods
Lectures (frontal form of teaching without student involvement, frontal form of teaching with student involvement).
Working with examples (frontal form of teaching with student involvement).
Presentation of visual, video, and animation materials (frontal form of teaching with student involvement).
Laboratory exercises (application of acquired knowledge in experimental work in the laboratory with basic real elements and their connections).
Homework (independent solving of basic electrical engineering problems).
Preparation of reports from laboratory exercises (independent analysis of the conducted experimental work in the laboratory in connection with the acquired knowledge within lectures).
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
Students recognize the basic concepts of energy systems. They develop the ability to create mathematical models to describe the behavior of energy systems.
Knowledge of fundamental numerical methods for solving engineering challenges in energy.
They can use tools such as Matlab/Simulink/Simscape for modeling and analysis of energy systems.
Python Programming: Basic knowledge for data analysis and solving energy problems.
Apply the fundamentals of SCADA systems for monitoring energy processes.
Analytical skills: They develop the ability to analyze and solve energy challenges.
Practical model implementation: The ability to develop and use mathematical models to solve specific energy problems.
Intended learning outcomes - transferable/key skills and other attributes
Transferable/key competences and other abilities:
They include analytical thinking, critical evaluation, problem-solving, computer literacy, communication skills, and teamwork. They also develop the ability to learn and adapt, data literacy, and sustainability thinking. Additionally, they encourage innovation, acquire knowledge about the energy industry, develop ethical awareness, and responsibility towards the environment and society.
Readings
M. Moškon, Osnove programiranja v jeziku Python za neračcunalničarje, 2020, Založba UL FRI
S. Strmčnik in drugi, Celostni pristop k računalniškemu vodenju procesov, 1998, UL Fakulteta za elektrotehniko
J. R. Hackworth, F. D. Hackworth: Programmable Logic Controllers: Programming Methods and Applications. Pearson, 2004,
https://www.mathworks.com/help/pdf_doc/matlab/using_ml.pdf
Additional information on implementation and assessment Method (written or oral exam, coursework, project):
- report
- theoretical exam
