Distributed Generation (DG) plays a vital role in modern power systems to achieve the requirements and satisfaction of end users while transmitting and distributing power from one to another point. It is a small scale generation which is also known as the embedded generation, dispersed generation or the decentralized generation. The optimal allocation of distributed generation (DG) on distributed network plays a crucial role in maximizing the benefits such as reduction of network losses and voltage profile improvement. There are many techniques used for the optimal allocation and sizing of DG in Radial Distribution System (RDS). The paper presented an overview and review the techniques used in some of the most popular methods includes load flow based methods, numerical techniques, intelligent techniques and evolutionary techniques. The evolutionary techniques include various optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Ant Colony Optimization and so on. The paper also provides advantages and disadvantages of each method and useful guidelines for the further research in this area.
In the electric power systems, the role of Distributed Generation (DG) is playing a key role, but the selection and sizing of DG is a challenging issue. In the literature many algorithms are available, each having its own advantages and disadvantages. Krill Herd Algorithm (KHA) is one of the nature inspired intelligent algorithm, which consider the herding behaviour of the krill individual for simulation. It has been utilized to solve many optimization problems in different fields and shown to be most efficient. In this paper Krill Herd Algorithm is used for optimal placing and sizing of DG in a radial distribution system for reduction of losses and improvement of voltage profile by considering the various constrains like DG real power limit, DG location Constraint, voltage limit and power balance constraint. The proposed algorithm is implemented on IEEE 33-bus test system. The results of the base case are compared with GA and Krill Herd Algorithms.
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