Modelling the Role of Nuclear Power and Variable Renewables in Climate Change Mitigation

Doctoral thesis, 2016

As the number of people on Earth and our energy needs have increased the system for providing this energy has become ever more complex and complicated and thus the need for more systematic understanding of it has grown. However, change in energy system is slow and many of the challenges that we face such as mitigating climate change need global solutions. Energy system models with long time span and global reach provide a way to analyse questions related to these challenges. This thesis focuses on capturing the role of nuclear power and variable renewables in global long term energy models. Papers I, II and IV assess the potential role nuclear power can play in global climate mitigation as well as identify the determining factors of this contribution whereas Paper III looks at the possible effects of phase out of Swedish nuclear power on European CO2 emissions and electricity prices. We show that nuclear power can reduce the climate change mitigation cost if allowed to remain or expand. The main factors determining the cost reduction potential are availability and cost of carbon capture and storage and cost of renewable and nuclear technologies. However, to decide whether to allow for a large scale expansion of nuclear power, the observed cost savings must be weighed against increased risks of accidental radiation releases from reactor operation, waste storage and nuclear weapons proliferation. To make this decision economic as well as non-economic factors should also be considered. To analyse such concerns we use post analysis of model scenarios in Paper I to assess the nuclear power expansion’s effect on nuclear weapons’ proliferation and apply the multi-criteria model analysis (MCMA) method in Paper IV to actively include criteria such as proliferation concern and energy security into optimisation. We find that MCMA method significantly improves the analysis of attainability of multiple simultaneous goals such in large-scale energy-systems models compared to simple scenario analysis that is presented in Paper I. The approach is more intuitive and requires minimal mathematical skills on the part of the user. MCMA method also avoids infeasible or dominated solutions that are caused by the stringent constraints applied in parametric optimisation. Paper V presents a method for capturing the effects of intermittency induced by variable renewables into the power system. Our results show that this approach manages to capture many aspects such as need for flexible generation capacity and curtailment at high penetration levels. We also find optimal electricity production mixes to vary significantly between regions due to different endowments of solar and wind resources. We show that adding electricity storage to the system will favour solar power but has only a minor effect on wind and nuclear power.