Energy and Power System Modeling Course

by Celia Galley Courses Energy


On October 2–5, 2017, Dr. Maindl Consulting - in cooperation with power system optimization modeling in GAMS expert and book author Dr. Alireza Soroudi of Dublin, Ireland - offered their first “Energy and power system modeling in GAMS” course. The four-day workshop venue was located right in the heart of the city of Vienna, Austria where two days filled with fundamental GAMS modeling and programming were followed by two interactive days entirely devoted to energy and power systems.

In the general GAMS modeling module, the course used a modeling example about a small vintage car and truck business to introduce the concepts of linear and mixed-integer linear optimization. The same model was also used later to illustrate how to use GAMS procedural language features to implement a complete enumeration approach. Another - bigger - application dealt with optimizing the schedule of a pumped storage hydropower plant. This more extensive code made use of all GAMS language objects taught in the course, like sets, scalars and parameters, variables, equations, and models. We explored together how the GAMS output files aid in debugging a model and how to use GAMS for interpreting optimization results and for advanced analytics by doing sensitivity analysis. On the interface side, we experimented with input and output of flat files and with integrating a model with Excel(R) via the GAMS Data Exchange (GDX and GDXXRW).

 

The second module of the course was devoted to energy and power system optimization. Building on the GAMS basics taught in the first module, we started from a simple static economic dispatch model which - in a way maximizing participant interaction - was developed into a dynamic economic dispatch model. At the same pace we jointly developed GAMS models for unit commitment, DC/AC optimal power flow, involved energy storage systems, and had a thorough look at modeling and solving allocation problems. Once formulated, all components were put together by formulating full-blown integrated energy system optimization models. Along the way advanced modeling techniques such as linearization and “big M” methods were introduced and deployed. Finally, a session on decision making under uncertainty elaborated on stochastic modeling techniques such as scenario modeling, fuzzy modeling, and robust optimization.