Multi-objective probabilistic distribution feeder reconfiguration considering wind power plants

This paper proposes an efficient probabilistic approach to investigate the multi-objective Distribution Feeder Reconfiguration (DFR) considering Wind Turbines (WTs). The proposed probabilistic method considers the uncertainty regarding the active and reactive load forecast errors as well as the WT output power variations concurrently. In this regard, 2m Point Estimate Method (PEM) as a proper probabilistic technique is utilized to properly model the uncertainty. The objective functions are the total active power losses, voltage deviation, total cost including the grid and WTs and the emission. In addition, a new optimization method based on Self Adaptive Modified Teacher Learning Optimization (SAMTLO) algorithm is proposed to solve the Multi-objective Probabilistic DFR (MPDFR) problem while the simultaneous effect of WTs is considered. In the proposed algorithm, a novel self adaptive modification phase is proposed to improve the overall ability of the algorithm for optimization applications. As the investigated problem is a kind of nonlinear constrained multi-objective optimization problem with conflicting objectives, the idea of Pareto-optimality is utilized to find the set of optimal solutions. The effectiveness and efficiency of the proposed method are demonstrated for two different test systems as case studies.