Optimization Method For Planning Greenhouse Gas Mitigation Under Uncertainty

In this study, a two-stage inexact-stochastic programming (TISP) method and a multi-stage inexact-stochastic integer programming (MISIP) method are developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed TISP incorporates techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within a general optimization framework; the MISIP incorporates techniques of multi-stage stochastic programming (MSP), interval-parameter programming (IPP) and integer programming (IP) within a general optimization framework. The TISP and MISIP can not only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated greenhouse gas (GHG) management policies and the associated economic implications. Moreover, the dynamics of system uncertainties and processes are reflected under MISIP model. The developed methods are applied to cases study of energy systems and CO2-emission trading planning under uncertainty. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired GHG abatement policies under various economic and system-reliability constraints.