An Inexact Dynamic Optimization Model for CO2 Emission Reduction in Subei Region, Northeast China

Abstract

In this study, an inexact mixed-integer fuzzy robust linear programming model for coupled management of coal and power with consideration of CO2 emissions mitigation system planning (IMIFLP-CCPM) was developed under uncertainty. This model could reach into the closed relationship and interactive characteristics of China’s coal production, electric power generation, and CO2 emissions in coupled coal and power management system and thus explore the applicability of the decarburization facilities and mechanism incorporated in the system through scenario analysis. Based on the integration of interval linear programming, fuzzy robust linear programming, and mixed-integer linear programming, the IMIFLP-CCPM could effectively incorporate and handle uncertainties presented in terms of interval values and fuzzy sets. Also, dynamic analysis of capacity expansion, facility improvement, and inventory planning within a multi-period and multi-option context could be facilitated in this model. The developed IMIFLP-CCPM was applied to a long-term coupled coal and power management with CO2 reduction systems in Subei region, Northeast China. One base scenario and four CO2 reduction scenarios were presented and analyzed to examine the optimal coal-flow allocation patterns and carbon mitigation schemes for the studied system when forced to comply with a given CO2 emission limit. The results indicated that the IMIFLP-CCPM model could provide in-depth analysis of tradeoffs between system costs, energy security, and CO2 emission reduction, thus helping investigate interactive relationships among multiple economical, environmental, and energy structural targets within the study system. Moreover, the attempt of planning coupled coal and power management with CO2 mitigation under uncertainty would provide an effective reference to cope with the dilemma of energy development and CO2 mitigation under the climate change situation in China.