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A Dual-Uncertainty Two-Stage Fractional Programming Model for Regional Power Systems in the Province of Ontario, Canada

J. Huang1 *, C. Z. Huang2, and S. Nie3

  1. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan S4S0A2, Canada
  2. Department of Chemical and Materials Engineering, Faculty of Engineering, University of Alberta, Edmonton, Alberta T6G2R3, Canada
  3. Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario M5S3G4, Canada

*Corresponding author. Tel.: +1 306-502-5668. E-mail address: (J. Huang).


This study proposed a dual-uncertainty two-stage fractional power system management (DUTSF-PSM) model to deal with uncertainties and dual objectives in the power management system of Ontario. This model integrates interval linear programming (ILP), chance-constrained programming (CCP), mixed-integer linear programming (MILP), and two-stage stochastic programming (TSP) methods into the framework of a linear fractional programming (LFP) model. Two-objective issues and capacity expansion schemes under multiple uncertainties can be addressed by the DUTSF-PSM model. Economic and environmental elements are considered in the objective function of the DUTSF-PSM model at the same time in order to get maximal system benefit with minimum environmental influence. This model can tackle effectively the tradeoff between the economic and environmental objectives. Through the DUTSF-PSM model for power systems in Ontario, the maximal system efficiency based on the least environmental influence under different levels of constraint-violation probabilities can be achieved. The results indicate that both hydroelectric and wind power have development potential when the economic and environmental factors are considered in the objective function at the same time. In addition, the results of factorial analyses reflected that the effect of CO2 emission of each power generation technology on the system revenue is most significant among the chosen three factors.

Keywords: reginal power system, interval linear programming, two-stage stochastic programming, factorial analysis

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