A two-point estimate method for uncertainty modeling in multi-objective optimal reactive power dispatch problem. (February 2016)

• Record Type: Journal Article
• Authors: Mohseni-Bonab, Seyed Masoud; Rabiee, Abbas; Mohammadi-Ivatloo, Behnam; Jalilzadeh, Saeid; Nojavan, Sayyad
• Is Part Of: International journal of electrical power & energy systems. Volume 75(2016:Feb.)
• Page Start: 194
• Page End: 204
• Publication Date: 2016-02
• Subjects: Monte Carlo simulation (MCS) -- Multi objective optimal reactive power dispatch (MO-ORPD) -- Real power loss (PL) -- Two-point estimate method (TPEM) -- Uncertainty -- Voltage deviation (VD); Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2015.08.009
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A probabilistic multi-objective optimal reactive power dispatch model is proposed. Load uncertainty is modeled by two-point estimate method. Active power losses and voltage deviations are used as the objective functions. Monte-Carlo simulations are utilized to verify the obtained results. Abstract: Due to nonlinear and discrete variables and constraints, optimal reactive power dispatch (ORPD) is a complex optimization problem in power systems. In this paper, the purpose is to solve multi objective ORPD (MO-ORPD) problem considering bus voltage limits, the limits of branches power flow, generators voltages, transformers tap changers and the amount of compensation on weak buses. The objectives of this paper are real power losses and voltage deviations from their corresponding nominal values, which are conflicting objectives. Because of the stochastic behavior of loads, the MO-ORPD problem requires a probabilistic approach. Hence, in this paper, a two-point estimate method (TPEM) is proposed to model the load uncertainty in MO-ORPD problem. Moreover, the proposed method is compared with some other methods such as deterministic approaches and Monte Carlo simulations (MCS). The obtained results approve the efficiency of the proposed methodology. The proposed models are implemented and solved using GAMS optimization package and verified using IEEE 14-bus and IEEE 30-bus standard test systems.