Optimal dispatch of shunt capacitors and load tap changers in distorted distribution systems using ant colony algorithms

Abstract

This thesis investigates the performances of a class of intelligent system algorithms in solving the volt/VAr/THD control problem for large distribution systems. For this purpose, optimal dispatch of Load Tap Changers (LTCs) and Switched Shunt Capacitors in distribution networks with high penetration of nonlinear loads is studied. The optimization problem consists of determination of LTC positions, switched shunt capacitors statuses and proper coordination of these switched elements such that power loss is minimized, voltage profile is improved and total harmonic voltage distortion (THDv) is acceptable while network and operational constraints are satisfied. The Decoupled Harmonic Power Flow (DHPF) is employed for solving the optimization problem. In the next step, an Ant Colony algorithm (ACA) is developed and implemented as an effective and new technique to capture the near global solution of the dispatch problem. Simulation results based on ACA, Genetic Algorithm (GA) and Fuzzy-GA are presented and compared to show the accuracy of the proposed approach.Finally, the application of the developed dispatch ACA in smart grids with Plug-In Electric Vehicle (PEV) charging activities in the residential networks is considered. ACA is first applied on the distribution part of the smart grid to minimize losses, improve voltage profile and mitigate harmonic distortions. Then, a smart load management (SLM) algorithm is proposed and tested for the coordination of PEVs on the residential feeders. The developed algorithm is tested on smart grid configuration with 449 buses consisting of the IEEE 31-bus distribution system connected to a number of low voltage residential feeders populated with PEVs. Simulation results are presented and compared for uncoordinated (random) and SLM coordinated PEV charging considering consumer designated priorities and charging zones.