The development of inexact dual-objective programming for regional energy systems planning in Guang-Fo-Zhao region, China

In this study, a chance-constrained fractional programming model is developed for energy systems in Guang-Fo-Zhao region, China. The developed model can tackle multiple uncertain parameters expressed as interval values, random variables presented as probability distributions in constraints, and provide optimized solutions under different fractional objectives. The proposed method is then applied for renewable energy system and air pollutant management in Guang-Fo-Zhao region. Two objectives are embedded in this strategic planning. One is to maximize renewable power generation per unit system cost, and the other one is to maximize gas air pollutant reduction per unit system cost. The result shows that, in the first objective, the increased rate of generating capacity from renewable energy per unit cost in Guang-Fo-Zhao region is much higher than that of a cost-efficient energy planning. In the second objective, the pollutant emission reduction per unit cost will be greatly improved in Guang-Fo-Zhao region. The proposed model can provide profound solutions and insights for decision makers regarding different energy-environmental considerations and targets. The proposed application in Guang-Fo-Zhao regional energy planning is particularly important for the extension to similar megalopolis under the severe situation of resource limitations, serious environmental pollution situations and ecosystem degradations.