Stochastic Programming Models for Long-Term Energy Transition Planning

Abstract

With growing concern over the effects of green-house gas emissions, there has been an increase in emission-reducing policies by governments around the world, with over 70 countries having set net-zero emission goals by 2050-2060. These are ambitious goals that will require large investments into the expansion of renewable and low-carbon technologies. The decisions about which technologies should be invested in can be difficult to make since they are based on information about the future, which is uncertain. When considering emerging technologies, a source of uncertainty to consider is how the costs will develop over time. Learning curves are used to model the decrease in cost as the total installed capacity of a technology increases. However, the extent to which the cost decreases is uncertain. To address the uncertainty present in multiple aspects of the energy sector, multistage stochastic programming is employed considering both exogenous and endogenous uncertainties. It is observed in scenarios when costs of emerging technologies decrease to competitive prices, decisions to invest in these technologies should be made earlier to allow for the decrease in costs to be taken advantage of in the future. Noticeably, a wider variety of energy and biofuel technologies are invested in when uncertainty is included. Interestingly, it is also seen that there are lower carbon emissions when uncertainty is considered.