Risk-Constrained Stochastic Optimization Methods for Dealing with Uncertain Technological Learning in Energy Systems

To date, optimization models of uncertain endogenous technological change models commonly add cost resulting from overestimating technological learning rates into an objective function with a subjective risk factor. This paper explores two risk-constrained stochastic optimization methods for dealing with uncertain technological learning with a simplified energy system model. The model assumes one primary resource and the economy demands one homogenous goods. There are three technologies, namely existing, incremental, and revolutionary, can be used to produce the goods from the resource. The existing technology has no learning potential; the incremental technology has a deterministic mild leaning potential; and the revolutionary technology has high but uncertain learning potential.