Fulfilling the Paris climate accords means reaching net-zero carbon dioxide emissions by mid-century. To find the most cost-effective pathways to achieve these targets, researchers build optimisation models of the energy system. Naturally, models of future energy systems come with many uncertainties. These can be either of structural or parametric nature. Structural uncertainty refers to model inaccuracies introduced by using simplified physical models, for instance for the flow of power in electricity networks, which only approximately reflect reality. On the other hand, parametric uncertainty refers to inconclusive assumptions today about the investment costs and technical parameters of generators, transmission infrastructure and storage units in 2050. However, with changing sets of combinations of cost assumptions, the optimal technology mix may change significantly. Therefore, finding good parameter estimates (or at least accounting for their uncertainty) is pivotal to achieve robust investment decisions. Methods from optimisation and duality theory can be used to perform a systematic sensitivity analysis of the optimisation results, i.e. investigating how decisions change as a certain parameter is varied. Moreover, techniques of stochastic optimisation can incorporate uncertainty directly in the model formulation. In this project you will investigate the role of parametric uncertainty in long-term investment planning for an energy system model with European scale.
Potential Research Paths
- Investigate approaches to sensitivity analysis in linear programming and stochastic optimisation.
- Identify the extent of sensitivities and which parameters are most dominant.
- Formulate and implement a stochastic optimisation problem derived from the deterministic linear programme taking into account distributions of the most sensitive techno-economic parameters.
- Analyse how investment decisions in the energy system change as uncertainties are taken into account.
We are looking for motivated students in computer science, physics, mathematics, economics or related fields who are interested in energy systems, economics, data processing and numerical optimisation. Basic knowledge in these topics is desirable, as well as experience with object-oriented programming. The project will primarily be in Python using packages such as pandas and pyomo. If you know about this already, it is also a big plus.
The Energy System Modelling Group
We are a young, international and friendly research group at the Institute for Automation and Applied Informatics that is committed to preventing catastrophic global warming and open-source software development. We look forward to working with you!
Literature and Links
- Convex Optimization by Stephen Boyd and Lieven Vandenberghe (Cahpter 5.6)
- An Introduction to Continuous Optimization by Niclas Andreasson, Anton Evgrafov, and Michael Patriksson (primarily Chapter 10)
- PyPSA-Eur: https://github.com/pypsa/pypsa-eur
- PyPSA: https://github.com/pypsa/pypsa and https://pypsa.readthedocs.io/en/latest/