Ali S. Aljumah

For radial distribution systems (RDSs) to operate efficiently, reliably, and sustainably, distributed generation (DG), capacitor banks (CBs), and network reconfiguration (NR) must be optimally allocated and sized. The main objectives considered in this research are minimizing real power losses, improving voltage profiles, enhancing energy utilization efficiency, and strengthening the operational reliability of distribution networks. To address this challenge, an Improved Frilled Lizard Optimizer (IFLO) is proposed to determine the optimal placement and sizing of DGs, CBs, and NR while satisfying system operational constraints. FLO is inspired by the adaptive survival and movement characteristics of frilled lizards in their natural ecosystem. The optimization mechanism of FLO is driven by hunting behavior for broad exploration and tree-climbing behavior for localized movement, enabling effective search and exploitation of promising regions. The IFLO introduces a defensive strategy phase, mimicking the lizard’s survival responses, and an adaptive local search phase, which models agile movement and stabilization behaviors. These enhancements improve the algorithm’s capability to reduce power losses, improve voltage regulation, increase network efficiency, and facilitate the effective integration of distributed energy resources into modern power distribution infrastructures. Comprehensive simulations on the IEEE 69-bus and the practical large-scale 141-bus RDS evaluate the impacts of DG and CB installation under practical operating constraints. This study investigates six scenarios involving different combinations of DG, CB, and NR to support efficient network planning and operation. Furthermore, recent optimization techniques, including Bezier Curve-Based Optimization (BCO), Horned Lizard Optimization Algorithm (HLOA), Whale Optimization Algorithm (WOA), Jaya Algorithm, and Particle Swarm Optimization (PSO), are implemented on the studied systems, and their results are compared with those of the proposed IFLO. The findings demonstrate that the suggested strategy outperforms existing optimization approaches in terms of convergence speed, solution quality, and network performance enhancement. The IFLO algorithm achieves an active power loss reduction of 92.69% for the large-scale system, while significantly improving voltage stability and operational efficiency. These outcomes contribute to the development of resilient, energy-efficient, and intelligent distribution infrastructures capable of supporting increased penetration of distributed energy resources under diverse operating conditions.

In this study, a modified chaos game optimization technique (MCGOT) is developed for the optimal allocation of renewable distributed generators (DGs) and capacitors (CRs) with a reconfiguration network (RN) while considering the reconfiguration ability of power distribution systems (RPDS). The CGOT is inspired by fractal geometry and chaos theory, where candidate solutions are iteratively updated based on geometric interactions among the global best, mean group, and current solutions within the search space. To address the high computational burden associated with multiple offspring evaluations, an MCGOT is further developed. In MCGOT, an adaptive offspring selection mechanism is introduced to generate and evaluate only a single candidate solution per iteration, thereby significantly reducing the number of function evaluations. In addition, a crossover operator is incorporated to enhance solution diversity, while a stage‐based control strategy dynamically balances exploration and exploitation. These modifications improve the computational efficiency and convergence characteristics of the original CGOT, making it more suitable for large‐scale and computationally expensive optimization problems. The proposed MCGOT and the conventional CGOT are applied to a practical case study of the 59‐bus Cairo distribution system in Egypt and the large IEEE 135‐bus distribution system. The simulation results show that integrating renewable DGs and CRs through MCGOT significantly minimizes active power losses and mitigates voltage fluctuations. When tested on two systems, MCGOT outperformed traditional methods like CGOT, slime mold algorithm (SMA), artificial rabbits optimization (ARO), and honey badger algorithm (HBA) in mean, worst, and standard deviation metrics. Moreover, to enhance the practical applicability and clean renewable integration, a probabilistic multiobjective formulation was developed considering annual operating losses and carbon emission costs amid renewable generation and load uncertainties using the Hong point estimate method (PEM). Results from the 135‐bus distribution feeder indicate that the proposed MCGOT improves convergence characteristics, lowering the expected annual operating cost from $91,394.95 to $88,307.80 and simultaneously reducing expected emissions under stochastic conditions regarding renewable resources.

This article introduces an advanced optimization‐based control framework for a hybrid microgrid (MG) system that includes renewable energy sources and energy storage components. An optimal controller is designed by tuning its parameters through an improved exponential distribution optimizer (IEDO), aimed at addressing the load frequency control (LFC) problem, essential for maintaining system stability amidst intermittent generation and stochastic disturbances. The MG comprises photovoltaic (PV) generation, wind turbines, battery storage systems (BSSs), and flywheel storage systems (FSSs), operating reliably despite variable loads and renewable uncertainties. The proposed IEDO enhances conventional exponential distribution optimizer (EDO) through three modifications: it integrates a differential evolution (DE)/rand/1‐inspired global search equation to boost exploration and prevent premature convergence; introduces a dimensional perturbation module (DPM) for selective decision variable updates, improving convergence accuracy; and employs a Bounce Out Operation (BOO) to reinitialize some solutions, maintaining diversity and allowing escape from local optima. The effectiveness of the proposed IEDO‐based control strategy is validated through extensive simulations under various operating scenarios, including load variations and renewable intermittency. Comparative results demonstrate that the proposed method achieves superior frequency regulation, reduced steady‐state error, faster dynamic response, and improved robustness compared to existing optimization techniques.

This study presents an advanced optimization approach, the self‐adaptive bonobo optimization technique (SABOT), designed specifically to facilitate the seamless integration of photovoltaic‐distributed generation (PV‐DG) in distribution networks. While retaining the foundational principles of the standard BOT, SABOT incorporates four distinct mating strategies: promiscuous, restrictive mating, consortship, and extra‐group mating. To enhance its capabilities, SABOT introduces advanced features such as a memory mechanism and a repulsion‐based learning technique for dynamic parameter adjustment across successive iterations. These enhancements significantly improve the algorithm’s exploration potential, enabling more effective identification of optimal solutions. The developed SABOT seeks to minimize the costs associated with carbon dioxide (CO ₂ ) emissions from the power grid, operational expenses of PV units, and energy losses. To accurately model the variability of solar power generation, the beta probability density function (PDF) is employed, capturing the daily fluctuations in solar irradiation. The improved SABOT was rigorously evaluated on two test systems: a real‐world Ajinde Nigerian distribution network and the widely‐used IEEE 69‐bus system. The simulation results highlight SABOT’s superior performance, demonstrating substantial decreases in emissions and losses of energy, thereby underscoring its effectiveness as a robust optimization tool for sustainable energy solutions. The aggregate yearly costs of emissions and lost energy for the Ajinde system are significantly reduced by 31% using the suggested SABOT version in comparison to the original scenario. It also achieves a significant 35% decrease for the IEEE 69‐bus system. Additionally, the simulation results demonstrate the competitive performance of the proposed SABOT version in comparison to differential evolution (DE), particle swarm optimizer (PSO), the techniques, and the conventional BOT.

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.