Planning of regional energy systems: An inexact mixed-integer fractional programming model

• Published in: Applied energy Vol. 113; pp. 500 - 514
• Main Authors: Zhu, H., Huang, W.W., Huang, G.H.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.01.2014; Elsevier
• Subjects: Applied sciences; Decision making; Economic data; Energy; Energy economics; Energy system planning; Exact sciences and technology; Fractional programming; General, economic and professional studies; Methodology. Modelling; Sustainable management; Uncertainty; Energy system planning; Sustainable management; Uncertainty; Fractional programming; Decision making
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2013.07.053

•An inexact fractional energy system planning (IMIF-EP) model is developed.•IMIF-EP generates useful results for a case of sustainable energy management (SEM).•Issues related to sustainability, uncertainties and dynamics can be reflected.•A comparative case of economical energy management (EEM) is also considered.•Results of two cases show significant differences between SEM and EEM. In this study, an inexact mixed-integer fractional energy system planning (IMIF-EP) model is developed for supporting sustainable energy system management under uncertainty. Based on a hybrid of interval-parameter programming (IPP), fractional programming (FP) and mixed integer linear programming (MILP) techniques, IMIF-EP can systematically reflect various complexities in energy management systems. It not only handles imprecise uncertainties and dynamic features associated with power generation expansion planning, but also optimizes the system efficiency represented as output/input ratios. An interactive transform algorithm is proposed to solve the IMIF-EP model. For demonstrating effectiveness of the developed approach, IMIF-EP is applied to support long-term planning for an energy system. The results indicate that interval solutions obtained from IMIF-EP can provide flexible schemes of resource allocations and facility expansions towards sustainable energy management (SEM) under multiple complexities. A comparative economical energy management (EEM) system is also provided. Compared with least-cost models that optimize single criterion, IMIF-EP can better characterize practical energy management problems by optimizing a ratio between criteria of two magnitudes. In application, IMIF-EP is advantageous in balancing conflicting objectives and reflecting complicated relationships among multiple system factors.