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Solution of reactive power optimisation including interval uncertainty using genetic algorithm
Author(s) -
Zhang Cong,
Chen Haoyong,
Ngan Honwing,
Liang Zipeng,
Guo Manlan,
Hua Dong
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.1195
Subject(s) - genetic algorithm , interval (graph theory) , ac power , algorithm , computer science , power (physics) , mathematical optimization , mathematics , physics , quantum mechanics , combinatorics
The reactive power optimisation (RPO) is generally used to design an optimal profile of the voltage and reactive power of power systems in the steady state for deterministic sets of demand loads and generation values, and it is a significant procedure in voltage control. However, the input data of a power system is actually uncertain in practise, which makes RPO an uncertain non‐linear programme. To address this problem, the input data were considered as intervals, and the RPO incorporating interval uncertainties model was proposed. To solve this model, the genetic algorithm was employed as the solution algorithm, where the reliable power flow calculation was used to judge the constraints of the model. The Pareto front was established as the solution of the proposed model since it has two objective functions, i.e. the midpoint and radius of real power losses. The application of this technique to the uncertain RPO was explained in detail. Two numerical results were analysed to demonstrate the effectiveness of the proposed method, especially in comparison with the previously proposed chance‐constrained programming method.

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