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, ...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.展开更多
文摘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.
