Abstract
This paper discusses the application of nature-inspired swarm intelligence methods for optimal allocation and sizing of distributed generation (DG) in the radial distribution system (RDS). Introducing DG units in the RDS will enhance the technical and economic benefits if they are optimally deployed. The objective functions considered are to improve the technical aspects and net economical saving cost with DG units integration on RDS. In this paper, a weighted multi-objective index considers a wide range of technical issues such as active and reactive power losses of the system, voltage profile, line loading, and the voltage stability, these are assumed as technical improvement aspects in the RDS. A recent optimization method, i.e. improved raven roosting optimization (IRRO) algorithm has been implemented for optimal deployment of DG in RDS. The state of the art of IRRO algorithm parameters will improve the ability for exploration and prevent premature convergence. Pareto optimality is used in making a set of the best solutions between two conflicting objectives considered, i.e. technical and economical aspects. The main contribution in this paper is to utilize a game theory based (minimax) algorithm in taking the best decision from a set of non-dominated solutions obtained by Pareto optimality criteria. IEEE 33-bus and 69-bus RDS’s are considered as the test systems for verifying the effectiveness of the IRRO algorithm. A comparative analysis with other nature-inspired swarm optimization techniques such as particle swarm optimization (PSO), modified teaching learning based optimization (MTLBO), Jaya algorithm (JAYA), and grey wolf optimizer (GWO) is also presented in this work. The simulation results of IRRO are compared with similar existing papers. It is observed that the IRRO algorithm can produce better results for the considered multi-objective functions. The MATLAB software is employed for the purpose. The novelty of the paper lies in the use of Pareto optimal and game theory in obtaining better results to the problem of optimal deployment of DG in RDS to improve technical as well as economic benefits.
Published Version
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