Power System Analysis 2

The course provides a methodological foundation for the advanced analysis of electric power systems in steady-state operation under both normal and contingency conditions. It covers analytical and numerical methods for assessing power system behaviour, with emphasis on computer-aided analysis of large-scale power systems. Students develop in-house computational tools using Python as part of the learning process. Central topics include power flow, advanced power flow and optimal power flow studies, beginning with formulation and solution of the problems and progressing to interpretation of results and practical application. The course is mathematically intensive and focuses on mathematical modelling and analysis rather than detailed physical phenomena.

Additional state-of-the-art topics relevant to current research and practice will also be covered.

Knowledge:

After completing this course, the student will be able to:

Construct per-phase impedance diagrams of a balanced power system and solve them using per unit systems.

Algorithmically build network matrices (Bus Admittance and Bus Impedance Matrices) for large-scale power systems, including the impact of both nominal and voltage regulating transformers.

Analyse the steady state conditions of a power system using load flow solution

• Formulate the load flow problem and study the various algorithmic approaches of solving the load flow problem
• Identify suitable assumptions that could be made to design and execute a faster Load Flow Solution
• Illustrate their applicability to real-world systems.
• Evaluate how voltage control can be achieved at the various buses in a power system using load flow solution as the basis
• Assess the comparative advantages and disadvantages of the various load flow solution methods
• Conduct contingency analysis of power systems using suitable methods

Formulate the Optimal Power Flow (OPF) Problem and solve the OPF Problem using suitable optimisation algorithms. Investigate the potential applications of OPF in power system planning and operation.

Carry out a state-of-the-art review of challenges and developments in power system planning and operational, including power system reliability.

Apply Symmetrical Component Theory for the analysis of unbalanced systems. Establish Bus Impedance Matrix Methods for algorithmic short circuit studies.

Skills

After completing the course, the student is able to:

• analyse large-scale power systems using advanced computational methods and algorithms
• select appropriate modelling choices based on system characteristics and study objectives
• justify modelling assumptions and choices when studying real-world power systems
• interpret analysis results from the various studies and critically reflect on their implications
• develop simulation tools for load flow and optimal power flow studies using Python
• assess challenges related to the design and operation of reliable power systems
• select and apply relevant strategies for solving open-ended problems in power system analysis
• supplement their learning through independent and targeted literature study

General competence

After completing the course, the candidate is able to:

• collaborate effectively in professional and academic contexts
• communicate technical results and reflections to peers, professionals and non-specialists through discussions, reports and presentations
• provide constructive feedback to peers
• handle uncertainty in problem descriptions and decision-making processes
• take sustainability perspectives into account in power system studies
• contribute to innovation and development processes within the field
• make critical and responsible use of AI tools in academic and professional work

The course will be pre-dominantly based on group activities.

Pre-recorded lectures, live lectures, guided problem-solving, problem-solving in groups, and project work (with presentation) are the various types of learning activities for the course. The course is given in English. Assignment/Project tasks will also be based on the usage of ready-made simulation tools and self-created software tools using Python