Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms. (March 2016) - Abedini, Mohammad; Moradi, Mohammad H.; Hosseinian, S.M.
Abstract: This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method. Highlights: A novel solution is provided for technical and reliability problems of MGs. To control DGs in MGs, a decentralize scheme based on droop controller is adopted. A multi objective optimization problem is formulated. A fuzzy HS-GA method is developed to solve the optimization problem. For the design of self-adequate autonomous sub-MGs clustering thinking is used.