A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning

Abstract

This study developed a copula-based fuzzy chance-constrained programming (CFCCP) model and applied it to electric power generation systems planning under multiple uncertainties. The CFCCP model was formulated by incorporating existing joint-probabilistic constrained programming and generalized fuzzy linear programming techniques within a general mixed-integer linear programming framework. The CFCCP model can not only effectively reflect uncertain interactions among random variables even when the random variables follow different probability distributions and have previously unknown correlations, but can also provide information about the membership grades for the decision variables and objective-function values. Thus, it would have a wider application scope than existing optimization models for power generation systems planning. Its applicability has been demonstrated through a case study of electric power generation planning within a region of North China. As a result, fuzzy interval solutions related to power generation and capacity expansion patterns of electricity-generation facilities, and primary energy supply structures were generated within six scenarios of constraint-violation levels under different α-cut levels. The results are helpful to investigate dynamic features of the regional power generation system, identify desired decision alternatives, and analyze the influences of interactions among multiple uncertainties on system outputs.