Identifying optimal clean-production pattern for energy systems under uncertainty through introducing carbon emission trading and green certificate schemes

In this study, a two-stage type-2 fuzzy stochastic programming (TTSP) method is developed for supporting clean production of energy systems with carbon and pollutant mitigation under uncertainty. TTSP can handle multiple uncertainties expressed as type-2 fuzzy sets, random variables and interval values; it can also provide an effective linkage between the pre-regulated energy and environmental policies as well as the associated economic implication. The TTSP method is then applied to planning energy system of Shanghai through introducing carbon emission trading (CET) and green certificate (GC) schemes. The solutions obtained can help generate energy-supply and electricity-generation schemes under different carbon trading ratios and various development plans of renewable energy. Results reveal that (i) the city's future energy structure would transit to the clean-production pattern on the basis of CET and GC policies; (ii) replacing fossil fuels with renewable energy sources (i.e. wind and photovoltaic power) can effectively facilitate reducing the emissions of pollutants (e.g., SO2, NOx and PM) and greenhouse gas (e.g., CO2). The results can help decision makers adjust energy and electricity supply, make appropriate mitigation plan, as well as gain insight into the relationship between mitigation schemes and optimal clean-production pattern.