Modeling low emission scenarios for the European power sector

The long-term ambition for the European power sector is to almost completely decarbonize generation of electricity. There are potentially many ways of achieving this, however, assessing an optimal transition to a low-carbon power system requires the use of advanced modeling tools. This thesis presents a collection of papers addressing various topics related to capacity expansion modeling of the European power system. The aim of the modeling is to evaluate cost-efficient decarbonization strategies. The most significant contribution of this work is the development of the European Model for Power system Investments (with high shares of) Renewable Energy, EMPIRE. This is a multi-horizon stochastic programming model where investments are optimized subject to operational uncertainty. The model simultaneously considers long-term and short-term system dynamics, in addition to short-term operational uncertainty. Inclusion of all these features is currently not used by any other capacity expansion model for the European power sector. The papers presented here focus on the formulation and applications of EMPIRE. Essentially all the papers touch upon analysis of decarbonization pathways for the European power sector. In addition, the role of carbon capture and storage (CCS) for decarbonizing the European power sector is analyzed in one paper. In the same paper, an evaluation of support mechanisms for enabling investments in demonstration CCS projects is presented. Another topic covered is integration of global climate change mitigation strategies computed by an integrated assessment model (IAM) in a study of the European power sector. This is handled through soft-linking of the IAM called GCAM and EMPIRE. By linking top-down and bottom-up models in this way, added detail can be provided to the IAM results. One paper presents a study where capacity factors from EMPIRE are used in life cycle assessment of electricity generation technologies in Europe. Improved estimations of utilization of different generation technologies can make the LCA impact analysis more accurate. In addition to the aforementioned topics, the thesis presents a contribution to the development of convergence improvements for the Benders decomposition method applied to large-scale power system investment planning problems. Also, a technique for improved handling of seasonal storage in power system capacity expansion models is discussed. The modeling studies show that large-scale deployment of wind po