文献类型：会议论文

英文题名：Risk-Constrained Stochastic Optimization Methods for Dealing with Uncertain Technological Learning in Energy Systems

作者：Ma, Tieju[1];Chi, Chunjie[1];Chen, Jun[1]

机构：[1]E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

会议论文集：2nd International Joint Conference on Computational Sciences and Optimization (CSO)

会议日期：APR 24-26, 2009

会议地点：Sanya, PEOPLES R CHINA

语种：英文

摘要：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.