The Identification of Optimal Co2 Emissions-Trading Strategies Based on an Inexact Two-Stage Chance-Constrained Programming Approach

In this study, an inexact two-stage chance-constrained model was developed for identifying optimal CO2 emissions-trading strategies under uncertainty. The model was formulated through incorporating two-stage and chance-constrained programming approaches within a general interval optimization framework. It can deal with uncertainties expressed as both probability distributions and intervals. A solution method was proposed to solve problems under such uncertainties. The proposed model was then applied to a hypothetical case of power generation planning in a region where emissions trading policy needed to be identified and implemented. Three policy scenarios corresponding to different CO2 emission quotas under emissions trading policies were advanced and analyzed. Interval solutions associated with different electricity generation patterns and CO2 emission quotas were obtained. The results could be used for generating decision alternatives for helping decision makers identify desired policies under environmental, system-availability, and electricity demand constraints.