文献类型：会议论文

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

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

机构：[1]School of Business, East China University of Science and Technology;

会议论文集：The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)论文集

会议日期：20090424

会议地点：三亚

主办单位：中科院系统院

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.