Indian Journal of Science and Technology
Year: 2012, Volume: 5, Issue: 3, Pages: 1-6
Ali Aref1*, Mohsen Davoudi2 and Majid Davoudi3
1 Department of Electrical Engineering,
2 Department of Electrical Engineering,
3 Department of Electrical Engineering, [email protected],
*Author For Correspondence
Department of Electrical Engineering,
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. Some of the so-called cost parameters are loss reduction, voltage profile improvement, environmental effects, installation and exploitation and maintenance expenses and costs of load prediction of each bus. Using an optimal Genetic Algorithm, proposed a destination function has been optimized which includes all of the cost parameters. 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. The cost parameters are variables dependent on the status and position of each bus in the network, putting forth an optimal DG placement. The proposed method 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.
Keywords: Distributed Generation (DG), Distribution Network, Optimization, Genetic Algorithm
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