Assessing robust policies for the adoption of low-carbon technologies under uncertainty

Increasing the adoption of alternative technologies is vital to ensure a successful transition to net-zero emissions in the manufacturing sector. However, existing models are limited in their ability to analyse technology adoption and the impact of policy interventions in generating sufficient demand to reduce cost in the face of uncertainty. Such a model is vital for assessing policy-instruments for the implementation of future uncertain energy scenarios. We formulate a novel robust market potential assessment problem under uncertainty to support low carbon technology adoption, resulting in policies that are more immune to uncertain factors. We demonstrate two case studies: the potential use of carbon capture and storage for iron and steel production across the EU, and the transition to hydrogen from natural gas in steam boilers across the chemicals industry in the UK. We show that when parameters are jointly 5% uncertain, the robust policy for CCUS adoption results in a 40% increase in cost. Each robust optimisation problem is solved using an iterative cutting planes algorithm which enables existing models to be solved under uncertainty. By taking advantage of parallelisation we are able to solve the nonlinear robust market assessment problem for technology adoption in times within the same order of magnitude as the nominal problem. Our model demonstrates the possibility of locating robust policies for the implementation of low-carbon technologies, as well as providing direct insights for policy-makers into the decrease in policy effectiveness that results from increasing robustness. The approach we present is extensible to a large number of alternative technology adoption problems under uncertainty.