Improved Manta Ray Foraging Algorithm for Optimal Allocation Strategies to Power Delivery Capabilities in Active Distribution Networks

Abstract

Losses minimization in distribution networks (DNs) is a critical concern for power utilities worldwide, especially in both mature and developing power systems. The growing integration of distributed generation (DG) at the distribution level has transformed traditionally passive networks into active ones, introducing new challenges and opportunities. This paper presents a novel approach to optimally determine the placement, size, and number of various types of DG units—specifically Type-I, Type-II, and Type-III—to minimize real power losses (RPL) while adhering to system constraints. The proposed optimization technique proposes an Improved Manta Ray Foraging Optimizer (IMRFO) to achieve robust and effective results, maximizing the economic benefits for distribution companies deploying DGs. The IMRFO has been improved with a hybrid two-stage approach that combines traditional MRFO with a Genetic Algorithm (GA) to improve global search capabilities. The MRFO searches for the optimal solution, transitions to the second stage if no improvement is observed after five iterations, and if GA finds a better solution, restarts the search, ensuring adaptive exploration and exploitation. Additionally, IMRFO has been rigorously tested in comparison to the original MRFO on a range of standard benchmark functions, which serve to validate the robustness, efficiency, and reliability of the algorithm under different optimization scenarios. These benchmark tests, including unimodal, multimodal, and constrained functions, demonstrate IMRFO’s superior performance compared to traditional methods. The method’s efficacy is validated on standard IEEE radial DNs with 33 and 69 buses, utilizing forward-backward sweep power flow analysis. Comparative studies with existing algorithms demonstrate that the proposed approach offers superior solutions with consistent convergence, highlighting its potential to significantly reduce power losses and enhance voltage profiles. The results underscore the technique’s capability in achieving optimal DG placement, contributing to the advancement of efficient and reliable power DNs. The proposed IMRFO algorithm achieved significant reductions in active power losses, with a 1.45% reduction for three DG units of Type-I in the IEEE 33-bus DN and a 21.70% reduction for three DG units of Type-III. In the IEEE 69-bus DN, IMRFO achieved a remarkable reduction of over 98% in active losses for three Type-III DG units.

Keywords

• Distribution networks
• losses minimization
• manta ray foraging optimizer
• power delivery capabilities
• validation on benchmarks