An interval-fuzzy possibilistic programming model to optimize China energy management system with CO^sub 2^ emission constraint

Energy system contains multiple uncertainties, and it is hard to express all its uncertainties by only one method. In order to solve this problem, an interval-fuzzy possibilistic programming (IFPP) method was developed based on the interval parameter programming (IPP), the fuzzy possibilistic programming (FPP) and fuzzy expected value equation within a general optimization framework. In this model, uncertainties presented in terms of crisp intervals and fuzzy-boundary intervals in both the objective function and constraints can be effectively addressed, and decision maker can choose the credibility degree of constraints based on his preference. The method was applied to optimize China energy management system with CO2 emission constraint, in which a CO2 emission coefficient model was employed to estimate the CO2 emission of each province. The study set two CO2 emission scenarios to analyze China energy system planning. The optimization results showed the approach could be used for generating a series of optimization schemes under multiple credibility levels, ensuring the energy system could meet the society demand, considering a proper balance between expected energy system costs and risks of violating the constraints of CO2 emission. Strengthening the CO2 emission constraint suggests the increasing of non-fossil energy generation and a higher system costs.