Public defence in Systems and Operations Research, M.Sc. (Tech) Olli Herrala

Title of the thesis: Mixed-integer formulations for large-scale energy-environmental optimization

Doctoral student: Olli Herrala
Opponent: Associate Professor Giovanni Pantuso, University of Copenhagen, Denmark
Custos: Professor Fabricio Oliveira, Aalto University School of Science, Department of Mathematics and Systems Analysis

During the past 10 years, we have seen stronger and more frequent impacts of climate change. It is increasingly evident that action should be taken to at least slow down this change. However, making decisions about what exactly should be done is challenging, largely because of the substantial uncertainty in many parts of the problem.

Decision-making under uncertainty has been researched widely in the past 70 years, but most of the research assumes the uncertainty to be independent of our decisions. This is however not the case when making decisions in researching new climate change mitigation techniques such as carbon capture and storage, as these decisions have an impact on the highly uncertain future costs of climate change mitigation. To address this research gap, this thesis presents results on solving problems with decision-dependency in both probabilities and information structures.

This thesis also considers a hierarchical decision-making setting where an international policymaker wants to reduce emissions from electricity production by setting a carbon tax, while avoiding significant decreases in the total production that would increase electricity prices. However, the problem also includes transmission system operators and electricity producers, each with their own goals. The interactions between these players result in a complex problem where a carbon tax might have unexpected consequences such as shifting production from one country to another or simply increasing the price of electricity.

The methods presented in this dissertation allow decision-makers to model and anticipate the effects of decision-dependent uncertainty and hierarchical decision-making processes. The solution methods are based on mixed-integer optimization, leveraging the substantial developments in solving such models during the past 20 years. The case studies presented in the dissertation illustrate the capabilities of the proposed methods and show how they could be used to support decision-making in these complex systems.

Key words: mixed-integer optimization, climate change mitigation

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/ 

Contact information:

Doctoral theses of the School of Science: https://aaltodoc.aalto.fi/handle/123456789/52 

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