Optimization in modern power systems

• Describe the fundamental principles of convex optimization and linear programming
• Formulate and explain the dual problem and KKT optimality conditions of a linear and convex optimization problem
• Recognize the structure of various decision-making problems for power systems operation and planning, in terms of input data, decisions, objective and constraints, and formulate them as mathematical optimization problems
• Describe and identify the suitability of different solution algorithms for large-scale optimization problems, and apply them to solve efficiently specific decision-making problems in power systems
• Describe and compare the different methods of optimization under uncertainty in terms of input data, problem structure (objective, constraints, decision variables), and computational complexity
• Solve, analyze and interpret the solutions of decision-making problems in power systems, such as the Economic Dispatch, Unit Commitment, and Optimal Power Flow problems
• Organize, plan, and carry out work collaboratively in a group
• Analyze, structure and present results to a broad audience
• Provide clear and constructive peer feedback

This course focuses on how to take optimal decisions that deal with both the economic and the technical aspects of power systems operation. We learn how to identify the structure of specific decision-making problems for power systems operation and planning, in terms of input data, decisions, objective and constraints, formulate them as mathematical optimization problems, and solve them efficiently.

The applications studied in this course include the Economic Dispatch, Unit Commitment, and Optimal Power Flow problems, which aim at finding optimal device settings and energy flows in the power system.

On the theoretical side, this course will provide a toolbox of optimization techniques including convex and linear optimization, LP and Lagrange duality, single- and multi-stage stochastic programming, and decomposition algorithms for large-scale optimization problems.

46700/46705/02402/42112/42101, or equivalent. Solid programming skills in Python or Julia or alike are expected, since since programming is an essential part of the course assignments. It is recommended that students are familiar with the fundamentals of electric power systems and principles.

a) Course materials (e.g. short videos, self-assessment quizzes) to prepare before lectures;
b) Lectures and in-class activities in connection with the lectures (e.g. individual and group exercises);
c) Peer-to-peer meetings and feedback-time on individual and group assignments (formative assessment)

Minimum: 10.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.