Abstract: Unplanned placement of the distributed energy resources in the existing network can cause severe problems like voltage instability, increase in power losses, system islanding, reverse power flows, air pollution, etc. Power losses and voltage profile maintenance are the most significant restrictions of the existing power system. Therefore, optimal placement of distributed energy resources is required to overcome the above problems and the use of renewable distributed energy resources is required for the reduction of air pollution. For optimal placement, many researchers have proposed various techniques but many of them have neglected the iteration convergence rate for the solution. Optimal placement of distributed energy resources has to deal with constraints like size, location, number, power factor and type. Enhanced particle swarm optimisation and genetic algorithm technique for optimal penetration and sizing of renewable distributed energy resources in the IEEE 33 bus radial distribution network has been applied. Enhanced particle swarm optimisation and genetic algorithm techniques have been applied for power loss reduction, enhancing voltage profile and minimising the iteration for the convergence rate of the solution.
Keywords: Distributed energy resources, enhanced particle swarm optimisation technique, genetic algorithm, radial distribution network
