Optimal DG Placement in Distribution Networks Using Intelligent Systems

Abstract

Distributed Generation (DG) unlike centralized electrical generation aims to generate electrical energy on small scale as near as possible to the load centers, interchanging electric power with the network. Moreover, DGs influence distribution system parameters such as reliability, loss reduction and efficiency while they are highly dependent on their situation in the distribution network. This paper focuses on optimal placement and estimation of DG capacity for installation and takes more number of significant parameters into account compare to the previous studies which consider just a few parameters for their optimization algorithms. Using a proposed optimal Genetic Algorithm, a destination function that includes the cost parameters (such as loss reduction, fuel price, etc.) has been optimized. This method is also capable of changing the weights of each cost parameter in the destination function of the Genetic Algorithm and the matrix of coefficients in the DIGSILENT environment. It has been applied and simulated on a sample IEEE 13-bus network. The obtained results show that any change in the weight of each parameter in the destination function of the Genetic Algorithm and in the matrix of coefficients leads to a meaningful change in the location and capacity of the prospective DG in the distribution network

Detection and localization of faults in smart hybrid distributed generation systems: A Stockwell transform and artificial neural network-based approach

“…Further, a 200-kW MPPT-based solar plant is connected to bus 15. It is also found that the reported locations for DG placements in IEEE 13-bus unbalanced test feeder [14][15][16]18,19,22 are mostly different from each other. In this work, 500-kW DFIG-based wind turbine is placed at bus 8 (node 680 as per 22 ) which is found to be the most common location for DG placement.…”

“…Several researchers proposed methods using hyperbolic ST 14,15 for different islanding detection and fault analysis. On the other hand, many researchers also suggested different approaches [16][17][18][19] for optimum DG placements. In Mohanta et al, 20 power loss minimization approach has been applied to achieve the optimum location for the DG placement.…”

Low-Capacity Exploitation of Distribution Networks and Its Effect on the Planning of Distribution Networks

“…To validate the proposed indices and their advantages to determine if the DN is used efficiently, a validation process is implemented based on the IEEE 13 bus distributions test system [34]. The validation process is developed by simulating three different cases in which the efficiency, the pondering factor, the exploitation capacity of feeders, and the exploitation capacity of the DN are calculated.…”

Augment dynamic and transient capability of DFIG using optimal design of NIOPID based DPC strategy

“…On the other hand, in order to control the human health, environment and global economy, the governments were compelled to considerably reduce their dependency on fossil fuel resources. As a result, particular attentions have been paid to the renewable resources e.g., wind, solar, wave, geothermal and biomass . Among these resources, wind energy due to including the several benefits such as: low electricity cost against the rational initial installation cost, trivial destructive effects on the ecosystem, flexibility along with the state of the art technology, wide infrastructure availability and high cumulative capacity, many various power plants based on this resource has been extensively installed all over the world .…”