# Abstract – Optimal allocation of distributed generators

Abstract – Optimal allocation of distributed generators (DG) plays a significant role in improving the performance of the radial distribution system. The positive and negative impact of the distributed generators will depend on the optimal location and size of distributed generators. Therefore this paper focuses on selecting optimal size and location for distributed generator in the radial distribution systems (RDS) to minimizes losses and improve voltage profile. Backward Forward Sweep methodology for the load flow analysis of the distribution system is used to determine the power losses in the system and Particle Swarm Optimization (PSO) is used to solve the optimization problem. A 33-bus system has been taken as the test system.

Keywords: Distributed Generators (DG), Particle Swarm Optimization (PSO), Radial Distribution system (RDS).

I. INTRODUCTION

Electrical distribution network systems include distribution feeder, which are arranged in mesh or radial pattern. The radial distribution system has high resistance to reactance ratio. Because of its high resistance to reactance ratio there will be increase in power losses and reduction in the magnitude of bus voltage. Therefore, to increase the performance of distribution system, introduction of DG’s takes place in the network 1-2.

Distributed generation (DG) is a small-scale power generation that is usually connected to distribution system. Allocating distributed generators helps in minimizing losses and improving voltage stability in power systems. This can be achieved when they are optimally placed and/or sized. DG may degrade the performance of the distribution system, if it is not planned carefully.This has made the problem of optimal allocation of distributed generation to be an interesting research topic for many researchers.

Various methods like Mixed Integer Linear Programming (MILP) 3, Dynamic Programming 4, Analytical 5, Improved Analytical (IA) 6 methods are used to solve DG allocation problem for reducing power losses in the distribution system. But the above mentioned techniques are depending upon certain assumptions like continuity, differentiability and convexity. Also, these methods solve only linear optimization problems effectively. But the DG location and sizing problem is not a linear optimization problem, it is a discrete nonlinear optimization problem. So these techniques are not that much effective in solving optimal allocation problem in the distribution system. So it is necessary to consider search algorithms importance in solving above mentioned problem. Recently many search algorithms, i.e. Genetic Algorithm (GA) 7, Particle Swarm Optimization (PSO) 8, Combined Genetic and Particle Swarm Optimization 9, Cuckoo search algorithm (CSA) 10, Tabu Search (TS) 11, Simulated Annealing (SA) 12, Artificial Bee Colony (ABC) 13 are used to solve DG allocation problem effectively with reduced power losses as an main objective.

To select appropriate location and to calculate DG size for minimum real power losses Naresh Acharya et al presented a heuristic method in 14. But more computational efforts are required to solve the problem. At each bus the optimal value of DG is calculated for minimum system losses. Placing the calculated DG size for each and every bus of the system, corresponding system losses are calculated and compared to decide the appropriate location.

PSO was introduced by Kennedy, J. and R. Eberhart 15. AlRashidi M.R. and El Hawary M.E. 16 have noted the advantages of PSO technique over other optimization techniques.

This paper is aimed at reducing power loss and improving voltage profile of distribution systems using DG sources. Optimal placement of DG is obtained by considering the voltage profile. Here Particle Swarm Optimization (PSO) is employed to obtain the optimal sizing of DG. The analysis has been carried out for IEEE 33 radial bus test system.