Energy Systems Modelling

Course goals

• explain the context in which energy systems models are used and for what purposes;
• explain the technological and economic principles and modelling methods underlying the state of the art energy system models;
• build simple energy systems models (linear programming models in excel, power system simulation model in Plexos, and system dynamics models in Python);
• evaluate the results of an energy systems model based on its modelling method and inputs;
• evaluate scientific articles and reports based on energy system model results considering the strengths and weaknesses of the models;
• understand the uncertainties related to  energy system modelling processes;
• apply Monte Carlo simulation to assess uncertainties in energy systems modelling processes.

Content

This course covers:

• The uses of energy system models by, among others, energy and climate policy makers, energy companies, and grid operators.
• Distinction between different types of models ranging from macro-economic to technology oriented models, simulation to optimisation models, static to dynamic models.
• The way processes in the energy-economic system are modelled in different types of models. These concern short and long term dynamic processes related to energy supply, energy demand, energy conversion, population growth, economic growth, economic structural change, resource depletion, substitution, innovation, intermittent renewable electricity generation, energy storage, and technological learning.
• Uncertainty assessment in energy systems modelling processes.
• Techniques of System Dynamic models, Linear programming models, power system simulation models, Monte Carlo analysis, sensitivity analysis, and scenario analysis.

This course is an entry requirement for:

• Master’s thesis (GEO4-2510)
• Consultancy project ES (GEO4-2519)