Stochastic optimal reactive power dispatch method based on point estimation considering load margin

Conventional optimal reactive power dispatch approaches operate mostly in deterministic form where the power injections are fixed. In practice, however, power injections, especially from intermittent renewable sources, and demand are of uncertainties. To address this problem, in this paper, we devel...

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Bibliographic Details
Published in2014 IEEE PES General Meeting | Conference & Exposition pp. 1 - 5
Main Authors Sidun Fang, Haozhong Cheng, Yue Song, Pingliang Zeng, Liangzhong Yao, Bazargan, Masoud
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2014
Subjects
chance-constrained programing
genetic algorithm
Genetic algorithms
LMC-SORPD
Load flow
point estimation
Power system stability
Reactive power
stochastic power flow
Stochastic processes
Thermal stability
Uncertainty
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Summary:Conventional optimal reactive power dispatch approaches operate mostly in deterministic form where the power injections are fixed. In practice, however, power injections, especially from intermittent renewable sources, and demand are of uncertainties. To address this problem, in this paper, we develop a load margin constrained stochastic optimal reactive power dispatch (LMC-SORPD) method. We first formulated the considered problem into a chance-constrained programming, which is then solved through genetic algorithm and stochastic power flow based on point estimation. Simulation results on several cases demonstrate that the proposed method is able to prevent the risk of under and over-voltage and increase load margin at a cost of a small but acceptable increase of active power loss. Specified chance-constrained handling techniques are adopted to improve the computational speed. Numerical examples validate the effectiveness of those techniques.
ISSN:1932-5517
DOI:10.1109/PESGM.2014.6939909