A fractional multi-stage simulation-optimization energy model for carbon emission management of urban agglomeration

Carbon emission reduction and carbon sink growth are essential to realize climate change mitigation. In this study, a fractional multi-stage simulation-optimization energy model is developed to tackle multiple uncertainties in regional energy systems and reflect system efficiency under conflicting objectives. Specially, simulation method is used for projecting energy demand and associated carbon emissions through integrating support-vector-regression, Monte Carlo simulation and stochastic impacts by regression on population, affluence, and technology tool into a general framework. Meanwhile, multiple complexities in terms of multi-region, multi-stage, and conflicting objectives are addressed through optimization techniques of fractional programming and multi-stage stochastic programming. To illustrate the applicability and superiority of the developed model, it is employed to the energy system and carbon emission management in the Pearl River Delta urban agglomeration. The major findings in the research include: electricity demand would grow by 26.6% from 2020 to 2035. The rate of renewable energy generation per unit cost under economic-environmental objectives would be 18.7% higher than that under single economic objective. Carbon emissions can be reduced under scenarios of climate change mitigation and socioeconomic development pathway. Meanwhile, forest carbon sink can be an effective alternative to mitigate carbon emissions.