Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system. (15th September 2016)

• Record Type: Journal Article
• Authors: Sultana, U.; Khairuddin, Azhar B.; Mokhtar, A.S.; Zareen, N.; Sultana, Beenish
• Is Part Of: Energy. Volume 111(2016)
• Page Start: 525
• Page End: 536
• Publication Date: 2016-09-15
• Subjects: Distribution system -- Distributed generation -- Grey wolf optimizer -- Active power loss -- Reactive power loss -- DG units; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.05.128
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The role of distributed generation (DG) for empowering the performance of the distribution system is becoming better known in the power sector. This paper presents a competent optimization approach based on the Grey Wolf Optimizer (GWO) for multiple DG allocation (i.e. siting and sizing) in the distribution system. The multiple objectives are to minimize reactive power losses and improve the voltage profile of the distribution system, without violating power system constraints. GWO is a newly proposed meta-heuristic optimization algorithm, inspired by grey wolves (Canis lupus). Alpha, beta, delta, and omega are the four categories of grey wolves, which are utilized to simulate leadership hierarchy. Despite this, GWO takes three main steps in hunting: searching for prey, encircling prey and attacking prey in order to complete the optimization process. The proposed study, based on GWO, is compared with the Gravitational Search Algorithm (GSA) and the Bat Algorithm (BA) based meta-heuristic methods. The different case studies of multiple DG type allocations in a 69-bus distribution system are carried out to show the effectiveness of the proposed methodology and distribution system performance. The comparative numeric results, voltage profile and convergence characteristic curves indicate better performance of the GWO against the GSA and BA. Highlights: DG placement for minimization of reactive power loss and voltage deviation. Weighted sum formulation for optimal allocation of multiple DG units. Grey Wolf Optimizer based methodology to identify the best site and size of DGs. Verification and comparison with Bat and Gravitational Search Algorithm. Examination of three case studies with different DG types.