Analyzing the impact of power loss reduction in radial distribution network using diverse distributed generators by utilizing ZIP load models

Distributed generators (DGs) are recent emerging technologies which are executed in distribution system (DS) to reduce power losses (PLs). However, optimal location and sizing of DG (OLSDG) remain a challenging combinatorial optimization problem due to its large search space and load uncertainties. Numerous scholars attempted to effectively handle this optimization problem characterized by a fixed load model (constant power (CP)), which fails to represent the real-world consumer behavior. This assumption causes a research gap, as practical DS is mainly dependent on characteristics of voltage magnitude and ZIP load models better represents this behavior. To overcome these challenges, this work reports a novel metaheuristic Algorithm inspired by quantum principles (Multipartite Adaptive Quantum inspired Evolutionary Algorithm (MAQiEA)) to discover the OLSDG. The originality of the work stands out by using realistic load model (ZIP load model), utilizing multipartite adaptive variation operator and analyzing the influence of diverse DGs with CP and ZIP load models. This study investigates two different scenarios to analyze the effect of various DGs on CP and ZIP load models. In first case, a study was conducted to analyze the influence of diverse DGs with CP load which are tabulated in the statistical analysis results that demonstrate the efficacy and soundness of MAQiEA. For performance assessment, proposed algorithm is compared with some well-known other metaheuristic algorithms. Tabulated results demonstrate that, MAQiEA outperforms other metaheuristic algorithms in PL reduction. Finally, the approach is suitable in terms of overall operational framework which is an important investigation in mitigating the PLs.