An integrated optimization modeling approach for planning emission trading and clean-energy development under uncertainty

The growing concern for global warming caused by the increased atmospheric concentration of carbon dioxide (CO2) has a significant effect on environmental and energy policies and economic activities, due to the ever-increasing use of fossil fuels such as coal, oil and natural gas throughout the world. A variety of complexities and uncertainties exist in CO2-emission-related processes and various impact factors, such as CO2-emission inventory, mitigation measure, and cost parameter. Decision makers face problems of how many clean-energy resources (or carbon credits) are needed to be replaced (or bought) by measuring electric-power benefits and uncertain economic penalties from random excess CO2 exceeding to given discharge permits. In this study, an integrated optimization modeling approach is developed for planning CO2 abatement through emission trading scheme (ETS) and clean development mechanism (CDM), where uncertainties presented in terms of fuzzy sets, interval values, and random variables can be addressed. The developed model is also applied to a case study of planning CO2-emission mitigation for an electric-power system (EPS) that involves three fossil-fueled power plants (i.e., gas, oil and coal-power plants). Different trading schemes and clean-energy development plans corresponding to different CO2-emission management policies have been analyzed. The results demonstrate that CO2-emission reduction program can be performed cost-effective through emission trading and clean-energy development projects. Violation analyses are also conducted to demonstrate that different violation levels for model’s objective and constraints have different effects on system benefit and satisfaction degree as well as emission trading and clean-energy development.