Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization

Abstract

Purpose

This paper aims to address not only technical and economic challenges in electrical distribution system but also environmental impact and the depletion of conventional energy resources due to rapidly growing economic development, results rising energy consumption.

Design/methodology/approach

Generally, the network reconfiguration (NR) problem is designed for minimizing power loss. Particularly, it is devised for maximizing power loss reduction by simultaneous NR and distributed generation (DG) placement. A loss sensitivity factor procedure is incorporated in the problem formulation that has identified sensitivity nodes for DG optimally. An adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for ascertaining a feasible solution.

Findings

In AWIDPSO, the adaptively varying inertia weight increases the possible solution in the global search space and it has obtained the optimum solution within lesser iteration. Moreover, it has provided a solution for integrating more amount of DG optimally in the existing distribution network (DN).

Practical implications

The AWIDPSO seems to be a promising optimization tool for optimal DG placement in the existing DN, DG placement after NR and simultaneous NR and DG sizing and placement. Thus, a strategic balance is derived among economic development, energy consumption, environmental impact and depletion of conventional energy resources.

Originality/value

In this study, a standard 33-bus distribution system has been analyzed for optimal NR in the presence of DG using the developed framework. The power loss in the DN has reduced considerably by indulging a new and innovative approaches and technologies.

Citation

Subramaniyan, M., Subramaniyan, S., Veeraswamy, M. and Jawalkar, V.R. (2019), "Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 38 No. 1, pp. 247-262. https://doi.org/10.1108/COMPEL-08-2017-0344