Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China

Abstract

In this study, a multistage inexact stochastic robust model was developed for regional energy system management in Jining City, China. Three scenarios about the electric power structure adjustment, clean power generation, and the emission reduction target are designed. Methods of interval parameter programming (IPP), stochastic robust optimization (SRO), and multistage stochastic programming (MSP) were incorporated into the developed model to tackle uncertainties described by interval values and probability distributions. The results indicated that the model can provide an effective linkage between conflicting economic cost and the system stability, and different power demand levels correspond to different electricity generation schemes with varied system cost and system-failure risk. The net system cost, power generation schemes, exported electricity, and captured CO2 amount were analyzed. The results indicated that the power generation by traditional technology would be decreased with the improvement of regional energy structure adjustment and environmental protection request. The modeling results are valuable for supporting the adjustment or justification of the existing power generation schemes within a complicated energy system under uncertainty.