Novel Amalgamation of Bat and Elephant Herd Optimization Algorithms for Generation Mix in Renewable Active Distribution Networks

Abstract

In this article, a hybrid approach is proposed that amalgamates two prominent nature-inspired, swarm-based, meta-heuristic techniques. The amalgamation is done to solve the mix generation, complex real-life optimization problems for renewable active distribution network (ADN). Some observed shortcomings of the bat algorithm (BA) are overcome by an effective introduction of elephant herding optimization (EHO) without altering its internal mechanism. The new transformations are validated by solving a real-life optimization problem of distributed generations for power loss minimization in power distribution networks. The performance of the proposed hybrid method is found to be promising when compared with some of the well-known optimizations in the literature. The proposed amalgamation has strengthened the exploration and exploitation potential of the new algorithm in a way that, their precise balance seeks the global optima. After validations, this technique is deployed to solve a multiobjective problem of optimal generation mix for renewable ADN for various test cases. Key objectives of the designed model of mix generation in ADN include annual energy losses, bus voltage profiles, and net demand variants of the networks over multiple load levels. The optimization problems model is implemented and solved for two test systems that include a benchmark 33 bus and a real-life 108 bus distribution network. The results comparison reveals that the proposed method significantly improved the system performances in terms of reduced annual energy loss by 55.95% and 44.48%, improved bus voltage profiles by 7.94% and 11.20%, and reduced net power demand fluctuations by 52.00% and 37.60% for 33 bus and 108 bus distribution network, respectively. The suggested optimization model outperforms the current existing approaches and has promising exploration and exploitation capabilities to address optimization problems in engineering.