Energy Decisions in an Uncertain Climate and Technology Outlook: How Stochastic and Robust Methodologies Can Assist Policy-Makers

Uncertain conditions may deeply affect the relevance of deterministic solutions proposed by optimization or equilibrium models as well as leave the decision maker in a quandary at the moment of defining policy. This chapter presents two applications of stochastic programming and robust optimization to climate and energy decisions using respectively TIAM-WORLD at the global level and MIRET in the case of France. At the global level, stochastic analysis demonstrates that the hedging strategy usually presents a smoother technology transition and is not equivalent to an average of deterministic solutions. Combined with a parametric analysis of the probability of the future outlooks, the approach produces a hedging strategy where the energy system prepares early for high mitigation even in the case of a low probability for such an outcome. Moreover, some technologies appear to be particularly appealing since they penetrate more in the hedging than in deterministic strategies; the penetration of gas power without carbon capture and sequestration in China, coal power plants with carbon capture in India, renewable electricity in Central and South America are examples of these “super-hedging” choices. In the case of the French transportation sector, robust optimization illustrates the crucial role of biofuels as a robust mitigation strategy in both moderate and severe emission reduction cases.