After successful completion of the course, students are able to...

• classify optimization problems and select appropriate solution methods.
• interpret linear and mixed-integer-linear optimization problems geometrically, implement them in MATLAB, and solve them using appropriate solution procedures.
• formulate Unit Commitment problems with given linear models of aggregates and demand curves as optimization problems.
• linearize nonlinear operational behavior and implement it using SOS1, SOS2 and convex approximation.
• formulate and solve typical configuration and dimensioning problems in energy systems (e.g.: storage integration) as mixed-integer-linear optimization problems.
• solve multiobjective optimization problems with two objective functions using the weighted sum method and represent Pareto fronts.

The (energy-) efficiency of industrial companies can often be significantly increased by precisely arranging the design and operation of all components. Due to the complexity and dynamics of industrial processes, this potential can only be exploited with computer-aided optimization. In this course, students learn to formulate mathematical optimization problems and to solve them using appropriate methods. The focus lies on mixed-integer-linear optimization (MILP) and suitable linearization methods to approximate nonlinear plant behavior appropriately.

• Each lesson consists of a lecture part with subsequent exercise part.
• Independent study of the examples, as well as submission and discussion of the results.

• 60 % - Active participation (preparation and presentation of coding assignments)
• 40 % - Written lecture examination