Mitigating price risks for organizations' energy systems en route to net-zero using two-stage stochastic programming to consider uncertainty in energy & emission prices

Decarbonizing the energy systems of organizations is a crucial step towards a sustainable fu-ture. However, the design of decarbonization pathways can be challenging due to complexity and numerous uncertainties in the energy system environment. One approach to cope with high complexity is using optimization models. As real-world decisions cannot be based on per-fect knowledge and rather deal with a variety of different uncertainties, it is crucial to verify the robustness of model-based solutions. This work analyzes the benefits of using a two-stage stochastic programming (SP) method that considers uncertainty of future CO2 and energy prices, as important input parameters during the optimization process. The results of the SP approach are compared with the results obtained using deterministic optimization for a real-world energy system of an organization. The results underline that solutions generated by SP are more robust, with summed total cost deviation (STCD) for different scenarios not exceeding 52 %. In contrast, deterministic results have a deviation of up to 96 %. In general, this robustness to different price development pathways is achieved by installing a variety of energy generation technologies rather than se-lecting one to two technologies adapt for the given single price projection. Additionally, this work compares the robustness of SP for different optimization methods and assumptions (e.g., myopic vs. perfect foresight and green- vs. brownfield) and underlines that organizations should consider uncertain CO2 and energy prices to mitigate the risk of unfore-seen price developments. The findings have significant implications for organizations looking for cost-effective and robust decarbonization strategies.