Optimization of Placement and Size of Distribution Generators Using Quantum Genetic Algorithms to Improve Power Quality in Bali Distribution Networks

Abstract

World energy requirement increased significantly, the main energy source from an oil is very limited. This problem drive an enhancement develop which support small scale generator to be connected near distributed network or near load center. Distributed Generator (DG) is a power plant which have a little capacity range between 15 kW to 10 MW. Basically, DG instalation is one way to fix a voltage profile where an installed DG would inject voltage to a transmission system or electric power distribution. Bali is a tourism area which it’s electric power source got a supply from Java and some large scale plant which use fuel of oil and gas, which until now still needed more of electric energy. An addition small scale generator for Bali is very helpful where economic profit is distribution cost and transmission cost’s reduction, electric cost and saving fuel energy. Technically a distributor of DG must be done correctly and optimal from it’s size or location so that give a maximum result from economic side, minimalizing electricity loss and increase voltage profile which result an electric power quality is improved. For that, in this research will use heuristic optimation with use Quantum Genetic Alghorithm method to placing distributed generator to Bali Electricity Network. To counting electicity loss and voltage profile, a method which used to solve it is Newton Raphson method. The result of this research, DG is installed to feeder which plaed in Abang Sub-District, Karangasem District where Abang Feeder had a total 43a bus which is a part from Bali Distribution System. With using QGA, DG is installed to bus 1, 5, 7, and 302 with each DG capacity is 0,374 MW, 1,894 MW, 1,988 MW and 0,500 MW, after installment of DG, voltage profile can be fixed. Voltage profile for some bus to Abang Feeder could be fixed from 0,83 pu to 0,98 pu. Electricity loss from 1,105 MW become 0,234 MW.