Optimization of Low-Carbon Energy Systems from Industrial to National Scale

Climate change mitigation requires a reduction of greenhouse gas (GHG) emissions. The main emitter of GHG emissions is the energy sector, which today is based on fossil fuels. To mitigate climate change, we need to transform the energy systems to low-carbon technologies. For this purpose, new energy system designs are required along with appropriate operational strategies. In principle, these new designs and operational strategies can be identified best using mathematical optimization. However, low-carbon technologies impose challenges in solving and assessing the resulting optimization problems.

Low-carbon technologies are volatile, which increase the complexity of optimal synthesis and operation. To cope with the complexity of operational optimization, we develop a time-series decomposition method. The method decomposes the complex, time-coupled operational problem into smaller subproblems, while still providing feasible, near-optimal solutions. For the increased complexity in synthesis problems, we propose a method based on time-series aggregation. The method divides the original synthesis problem into two separate problems: one aggregated relaxed problem and another aggregated restricted problem, leading to feasible, near-optimal solutions.

In addition, the transformation process requires a rigorous assessment of greenhouse gas emissions and potential burden-shifting. In particular, the assessment of emissions due to electricity usage on the industrial scale is difficult, as the underlying national electricity system is not modeled. Therefore, we propose methods to compute industrial greenhouse gas emission factors for electricity. By exploiting these emission factors, industrial energy systems can significantly reduce their emissions. On the national scale, burden-shifting towards environmental impacts besides climate change needs to be prevented in the transformation. Hence, we develop a national energy system model and extend the optimization with life-cycle assessment considering 15 further environmental impacts. With the model, we compute a cost-optimal transformation pathway to a low-carbon energy system. The transformation leads to many co-benefits, but also to severe burden-shifting, which needs to be considered during the transformation process and in the development of new low-carbon technologies.

Overall, the methods and models in this thesis facilitate the integration of low-carbon technologies in energy systems.