A new evolutionary-analytical two-step optimization method for optimal wind turbine allocation considering maximum capacity

In this paper, a new two-step optimization algorithm is introduced for optimal placement of a Wind Turbine Generator (WTG) in a distribution network. The locations and the maximum capacities of WTGs, as well as their optimum power factor, are determined simultaneously in two different steps. In the first step, the locations and power factors of the WTGs are considered as solutions of a meta-heuristic optimization algorithm and in the next step, the optimal capacities of the WTGs are determined analytically. A recently introduced optimization algorithm known as the Lightning Attachment Procedure Optimization algorithm is employed for the first step, and a fast analytical method is used for the second one. The objective function of the optimization problem is considered for annual energy loss minimization. The proposed approach is applied on 85-bus test system, and the results are discussed. Fast convergence, best global answer finding, and robustness are the characteristics of the proposed method, which are concluded from the results and discussion.In this paper, a new two-step optimization algorithm is introduced for optimal placement of a Wind Turbine Generator (WTG) in a distribution network. The locations and the maximum capacities of WTGs, as well as their optimum power factor, are determined simultaneously in two different steps. In the first step, the locations and power factors of the WTGs are considered as solutions of a meta-heuristic optimization algorithm and in the next step, the optimal capacities of the WTGs are determined analytically. A recently introduced optimization algorithm known as the Lightning Attachment Procedure Optimization algorithm is employed for the first step, and a fast analytical method is used for the second one. The objective function of the optimization problem is considered for annual energy loss minimization. The proposed approach is applied on 85-bus test system, and the results are discussed. Fast convergence, best global answer finding, and robustness are the characteristics of the proposed method, which ar...

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