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Turkish Journal of Electrical Engineering and Computer Sciences

DOI

10.3906/elk-2003-46

Abstract

Global environmental problems associated with traditional energy generation have led to a rapid increasein the use of renewable energy sources (RES) in power systems. The integration of renewable energy technologiesis commercially available nowadays, and the most common of such RES technology is photovoltaic (PV). This paperproposes an application of hybrid teaching-learning and artificial bee colony (TLABC) technique for determining theoptimal allocation of PV based distributed generation (DG) and battery energy storage (BES) units in the distributionsystem (DS) with the aim of minimizing the total power losses. Besides, some potential nodes identified by the powerloss sensitivity factor (PLSF). Thereupon TLABC is applied to determine the location of the DG and its size from thecandidate nodes. The beta probability distribution function (PDF) is employed to characterize the randomness of solarradiation. High penetration of RES can lead to a high level of risk in DS stability. To maintain system stability, BES isconsidered to smooth out the fluctuations and improve supply continuity. The benefits of using BES is mainly dependenton operational strategies related to PV and storage in DS. The performance of the developed approach is tested on the69 node and 118 node DSs and compared with the differential evolution (DE) algorithm, genetic algorithm (GA), for afair comparison. Besides, the developed approach compared with other methods in literature which are solved the sameproblem. The results show how practical is the developed approach compared with other techniques

Keywords

Distributed generation, optimization, battery energy storage, energy loss, uncertainty

First Page

773

Last Page

795

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