Inexact Fuzzy Chance-Constrained Fractional Programming for Sustainable Management of Electric Power Systems

Abstract

An inexact fuzzy chance-constrained fractional programming model is developed and applied to the planning of electric power systems management under uncertainty. An electric power system management system involves several processes with socioeconomic and environmental influenced. Due to the multiobjective, multilayer and multiperiod features, associated with these various factors and their interactions extensive uncertainties, may exist in the study system. As an extension of the existing fractional programming approach, the inexact fuzzy chance-constrained fractional programming can explicitly address system uncertainties with complex presentations. The approach can not only deal with multiple uncertainties presented as random variables, fuzzy sets, interval values, and their combinations but also reflect the tradeoff in conflicting objectives between greenhouse gas mitigation and system economic profit. Different from using least-cost models, a more sustainable management approach is to maximize the ratio between clean energy power generation and system cost. Results of the case study indicate that useful solutions for planning electric power systems management practices can be generated.