Sitting and sizing of distributed generation through Harmony Search Algorithm for improve voltage profile and reducuction of THD and losses

Abstract

Presence of the distributed generation (DG) in electric systems can represent a significant impact on the operational characteristics of distribution networks. The optimal placement and sizing of generation units on the distribution network has been continuously studied in order to achieve different aims. In this paper our aim would be optimal distributed generation allocation for voltage profile improvement, loss and Total harmonic Distortion (THD) reduction in distribution network. Harmony Search Algorithm (HSA) was used as the solving tool, which referring two determined aim; the problem is defined and objective function is introduced according to losses, security and THD indices. The applied fast harmonic load flow method is based on the equivalent current injection that uses the bus-injection to branch-current (BIBC) and branch-current to bus-voltage (BCBV) matrices which were developed based on the topological structure of the distribution systems. This method is executed on 12 bus harmonic unbalanced distribution system and show robustness of this method in optimal and fast placement of DG, efficiency for improvement of voltage profile, reduction of power losses, and THD.

A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems

“…But considering cost function in finding optimal location and DG size may deviate from the original problem. Author in [7], used the Harmony Search Algorithm as a new approach; however, the optimal penetration limit for DG is set by the user before running the optimal allocation routine. Hung et al in [8], combined the loss sensitivity concept with optimal siting and sizing, but only studies the real and reactive power loss reduction objectives.…”

Study impact of various load models on DG placement and sizing using backtracking search algorithm

“…Several analytical approaches minimizing line losses are proposed for the DG allocation [5][6][7][8][9][10] and optimal power flow [11,12]. For the same purpose of DG allocation, an evolutionary algorithm (EA) uses genetic algorithm and an ε-constrained method [13] and other heuristic algorithm methods through harmony search algorithm [14], particle swarm optimization [15,16], artificial bee colony algorithm [17], and differential evolution (DE) [18] and so on have been applied to sit single and/or multi-DGs for various objectives. In addition, the reader can be referred to [19,20], in which very comprehensive reviews covering the available DG placement models and various approaches with satisfactory classifications of researches are covered.…”

Optimal DG Allocation in Radial Distribution Systems with High Penetration of Non-linear Loads

“…From the methodology point of view, various approaches have been used for proper DG apportionment such as tabu search [17], harmony search algorithm [18], particle swarm optimization [19], and a combined GA with methods such as tabu search [20], fuzzy system [21,22], particle swarm optimization [23], and graph theory [24]. Optimizing the distribution system for power loss minimization and voltage profile improvement has been proposed and implemented by optimally allocating and sizing a number of distributed generators without taking harmonics into account and also this was carried out assuming constant load conditions and ignoring the reality of load variations in the distribution system.…”

“…Several optimization techniques have been applied to DG placement [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The major objective of DG placement techniques is to minimize the losses of power systems.…”