Optimal allocation of capacitors in radial/mesh distribution systems using mixed integer nonlinear programming approach

•A new MINLP approach is proposed for capacitor placement.•The proposed method is applied to the radial and mesh distribution systems.•Objective of capacitor placement is reduction costs of capacitor and power losses.•The capacitor placement solves using GAMS software with SBB and MINLP solvers.•The...

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Bibliographic Details
Published in:Electric power systems research Vol. 107; pp. 119 - 124
Main Authors: Nojavan, Sayyad, Jalali, Mehdi, Zare, Kazem
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.02.2014
Elsevier
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ISSN:0378-7796, 1873-2046
Online Access:Get full text
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Summary:•A new MINLP approach is proposed for capacitor placement.•The proposed method is applied to the radial and mesh distribution systems.•Objective of capacitor placement is reduction costs of capacitor and power losses.•The capacitor placement solves using GAMS software with SBB and MINLP solvers.•The proposed method is capable to solve large scale systems. Capacitors in radial/mesh distribution systems are used to supply reactive power to minimize loss and to improve the voltage profile. The appropriate placement of capacitors is also important to ensure that system power losses and total investment capacitor costs are minimal. The capacitor placement problem consists of finding specific sitting and sizing to install capacitor banks in an electrical distribution system. Consequently, the losses are reduced due to the compensation of the reactive component of power flow. This paper presents a new mixed integer nonlinear programming approach for capacitor placement in radial/mesh distribution systems that determine the optimal sitting and sizing of capacitors with an objective of reduction power loss and investment capacitor costs. The proposed method is applied to 10, 34, and 85-bus radial distribution systems and CIVANLAR mesh distribution system. Various-scale application systems are used to compare the performance of the proposed method with the Fuzzy reasoning, particle swim optimization (PSO), plant growth simulation algorithm (PGSA), and Heuristic based. Numerical results show that the performance of the proposed MINLP method is better than the other methods. Also, the MINLP method is superior to some other methods in terms of solution power loss and costs.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2013.09.019