A heap-based algorithm with deeper exploitative feature for optimal allocations of distributed generations with feeder reconfiguration in power distribution networks

摘要

The optimal combination of power distribution feeder reconfiguration (PDFR) with distributed generators (DGs) is one of the most attractive combinatorial optimization issues. This paper proposes an improved Heap-based algorithm to enhance the performance of a recently published technique called Heap-based optimizer (HO). HO’s performance is enhanced with a Deeper Exploitative Improvement (DEI) by applying an effective exploitation feature to boost the searching around the leader position with the goal of enhancing its global search capabilities and avoiding becoming trapped in a local optimum. The effectiveness of the proposed HODEI in comparison with the conventional HO algorithm is checked on 35 benchmark optimizing functions which have either unimodal and multimodal characteristics. The proposed HODEI and the conventional HO are employed for simultaneous DG allocations and PDFR using two IEEE standard distribution networks of 33 and 69-bus at various loading conditions. Additionally, a large-scale 137-bus distribution network is utilized to assess the validity and efficiency of the proposed HODEI with several independent variables. The proposed HODEI is contrasted with several recent algorithms of harmony search, genetic, equilibrium, marine predators, fireworks, and firefly optimizers. The simulation results demonstrates that the proposed HODEI always gives better performance compared to the conventional HO algorithm. The proposed HODEI shows significant improvement for the voltages at all buses. Furthermore, the proposed HODEI outperforms the compared algorithms in acquiring the minimum best, mean, worst and standard deviations of the considered fitness.