An enhanced optimizer of social network search for multi-dimension optimal power flow in electrical power grids

Abstract

This paper presents an enhanced computational optimizer of the Social Network Search Technique (ESNST) for multi-dimension Optimal Power Flow (OPF) in Electrical Power Grids (EPGs). The SNST is motivated by social network users who have various moods: conversation, imitation, innovation, and disputation. The proposed ESNST is introduced with two strategies. The first is the aggressive exploit process, which aims to increase the number of people seeking the best possible perspective to develop SNST performance. The second strategy proposes an adaptive parameter to aid in higher exploitation towards the end of iterations. This paper considers the multi-dimension OPF in EPGs with several minimization objectives for fuel costs, grid losses, and produced emissions. Not only that but also the system's power demands and losses must be satisfied. The performance of the proposed SNST and ESNST is evaluated on IEEE standard 30-bus, 57-bus, 118-bus, and practical West Delta Region EPGs. The results reveal the solution quality and robustness of the proposed ESNST compared with the SNST and other relevant techniques reported in the literature.