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doi:10.3808/jeil.202200088
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Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition

M. N. Li1, G. H. Huang1 *, X. Y. Zhang1, and J. P. Chen1

  1. Environmental Systems Engineering Program, University of Regina, Regina, Saskatchewan, Canada S4S 0A2

*Corresponding author. Tel.: +13065854095; fax: +13065855755. E-mail address: huangg@uregina.ca (G. H. Huang).

Abstract


In this study, an inexact mixed-integer interval stochastic fractional model (IMSFP) is developed for Shandong’s sustainable power system management under uncertainties. Shandong has a high proportion of fossil-fuel power, which has resulted in significant greenhouse gas emissions. Future is an essential period for energy structure transition. Developed IMSFP can effectively tackle dual objective, system efficiency represented as output/input ratios, as well as uncertainties described as interval values and probability distributions in the constraints and objectives. The results indicate that the clean power transition and capacity expansion scheme are sensitive to different constraint-violation risk levels. Obtained interval solutions can provide flexible strategies for resource allocation and expansion capacities under multiple complexities. An economic single objective model (IMCLP) is also developed, which aims at minimizing the system cost. The comparative results illustrate that the IMSFP model can better characterize the real-world power system problems through optimizing a ratio between clean energy utilization and system cost. Biomass and wind power would be major developed electricity forms in the future, and solar energy has great development potential. In short, the proposed IMSFP model is advantageous in balancing conflicting dual objectives and reflecting complicated interactions among system efficiency, economic cost, system reliability, and constraint-violation scenarios.

Keywords: dual-objective linear programming; interval linear programming; constraint-violation risk; power system planning; clean power transition


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