Smart general Volt/VAr devices placement problem. The general

Smart grid concept in the power systems area has attracted new attention to make distribution systems more efficient and save more energy. Volt/VAr control is a commonly performed technique which manages the reactive power and enables utilities to run distribution systems on lower voltage resulting in higher energy savings. Volt/VAr control devices such as On-Load Tap Changer of transformer (OLTC), Voltage Regulator (VR), and Capacitor Bank (CB) are installed in distribution systems for reactive power compensation to achieve power and energy loss reduction, voltage regulation and system capacity release. The scope of these benefits relies on greatly on how they are placed on the system. The problem of how to place them on the system such that these benefits are achieved and maximized against the cost associated with their placement is described as the general Volt/VAr devices placement problem. The general Volt/VAr devices placement problem is formulated as an optimization problem to determine the location of these devices, the types and size of them to be installed and the control scheme for them at the buses of radial distribution networks. In this thesis, three hybrid techniques which are fuzzy-genetic, a fuzzy-particle swarm and fuzzy-dragonfly algorithms used for solving the general capacitor placement problem. The first technique has a two-stage methodology for the optimal capacitor placement problem. In the first stage, fuzzy expert system approach is used to find the optimal capacitor locations and in the second stage, the genetic algorithm is used to find their size. Similarly, the second technique has a two-stage methodology. In the first stage, a fuzzy approach is used to find the optimal capacitor locations and in the second stage, the particle swarm optimization algorithm is used to find their size. Similarly, the third technique has a two-stage methodology. In the first stage, a fuzzy approach is used to find the optimal capacitor locations and in the second stage, the dragonfly algorithm is used to calculate their size. These techniques are tested on IEEE bus radial distribution systems level of smart grids as well as data from Saudi Electricity Company using Matlab programming.

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