A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning. (1st February 2017)

• Record Type: Journal Article
• Authors: Chen, F.; Huang, G.H.; Fan, Y.R.; Chen, J.P.
• Is Part Of: Applied energy. Volume 187(2017)
• Page Start: 291
• Page End: 309
• Publication Date: 2017-02-01
• Subjects: Copula -- Joint-probabilistic constrained programming -- Generalized fuzzy linear programming -- Multiple uncertainties -- Electric power generation systems planning; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.11.065
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A copula-based fuzzy chance-constrained programming model is developed. This model can reflect interactions among random variables of systems. Copulas are used to construct joint distribution functions of the random variables. A GFLP technique is introduced to deal with uncertainties expressed as fuzzy sets. This model has a wider range of applications than the previous models. 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.