A tailored decomposition approach for optimization under uncertainty of carbon removal technologies in the EU power system

Abstract

The broad portfolio of negative emissions technologies calls for integrated analyses to explore the synergies between them and the power sector, with which they display strong links. These analyses should be conducted at a regional level, considering system uncertainties, assessing local benefits and the impact on carbon removal potential. This study investigates how uncertainty in electricity demand affects the optimal design of integrated carbon removal and power generation systems using multistage stochastic programming. Given the model complexity, we propose a tailored decomposition algorithm by extending previous work on the shrinking horizon approach that reduces the computational time by 90 %, enabling insights into various European scenarios. A combination of conventional technologies and biomass could satisfy the electricity demand while providing up to 9 Gt of net CO₂ removal from the atmosphere. Omitting uncertainties leads to an underestimation of the total cost and the selection of different technologies possibly leading to suboptimal performance.