The increasing integration of renewable energy sources has accelerated the adoption of microgrids, necessitating efficient power-sharing and control techniques for reliable operation. This study proposes an optimized droop control technique for parallel inverters in islanded AC microgrids, focusing on improving system efficiency. Conventional droop methods often encounter challenges in power-sharing accuracy under varying load conditions due to mismatched feeder impedances and differing power loss characteristics of distributed generators (DGs). To address these issues, the proposed method dynamically adjusts droop coefficients using Particle Swarm Optimization (PSO) to optimize power distribution, reduce circulating currents, and improve energy conversion efficiency while maintaining system modularity. A system-level microgrid efficiency model is designed to identify optimal operating points under diverse load profiles. Comparative analysis demonstrates that the proposed PSO-based controller consistently outperforms conventional droop methods, achieving system efficiency improvements ranging from 0.11% to 0.52% across various load conditions and power factors. Simulation results from PSIM and MATLAB/Simulink further highlight reduced circulating currents, enhanced energy conversion efficiency, and improved system stability. These findings underscore the potential of PSO-driven control as a scalable and communication-free solution for efficiency optimization in decentralized microgrids. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

In recent years, rising demand for electricity has caused significant increases in power transmission and distribution losses in the distribution system. As consequence, there is a growing need for technologies and methods to reduce distribution system losses. One such strategy is to integrate distributed generation (DG) into the system, which has the potential to reduce losses while increasing total distribution system efficiency. In this study, Nutcracker Optimizing Algorithm (NOA) is proposed to resolve the objective constraints. This technique will be evaluated using the IEEE 69-bus test system. This study includes four sets of case study constructed for evaluating the NOA's performance. NOA is used to determine the placement and size of DG, considering the use of DG types I and II as well as to evaluate the losses obtained. In the context of optimizing DG placement and sizing within distribution systems, the influence of population size on the performance of NOA remains under explored. Therefore, this study aims to investigate the impact of varying population size on the convergence speed, solution quality, and computational efficiency is applied to the DG optimization problem by systematically analyzing different population sizes, this research seeks to identify an optimal balance that maximizes the algorithm’s effectiveness while minimizing computational overhead, thereby enhancing the practical implementation of DG optimization in real-world distribution networks. The results indicate that larger population number is the best when considering the convergences. Furthermore, the results demonstrate that DG type I is the optimum choice of DG since the power losses reduction achieved is larger when compared to DG type II.

Microgrids, small-scale autonomous power systems, are becoming essential for incorporating renewable energy sources while improving the reliability and efficiency of electrical networks. These systems consist of multiple distributed generation units connected in parallel, which are controlled through coordinated strategies and can function in both grid-connected and islanded modes. Ensuring stability and effective load sharing in islanded microgrids, where the system operates independently, is critical to maintaining performance. Although numerous surveys have examined microgrid control techniques, few have systematically addressed hierarchical control methods from an efficiency standpoint. This paper presents a comprehensive review of droop control strategies in AC microgrids with distributed energy resources, focusing on hierarchical control approaches, power-sharing mechanisms, optimization challenges, and technical issues. Additionally, multiple control strategies are evaluated based on their benefits and drawbacks. A detailed analysis of several optimization techniques is also provided, highlighting their merits and demerits. Finally, the paper addresses emerging trends and potential future research directions for islanded microgrids.

This research introduces an adaptive improved particle swarm optimization (AIPSO) approach for maximum power point tracking (MPPT) approach designed to enhance energy harvesting from photovoltaic (PV) systems under dynamic irradiance conditions. The proposed AIPSO algorithm addresses the challenges associated with traditional MPPT methods, particularly in scenarios characterized by fluctuating solar irradiance, such as step changes and partial shading. By incorporating a robust reinitialization strategy along with updated velocity and position equations, the algorithm demonstrates superior performance in terms of convergence accuracy, tracking speed, and tracking efficiency. This modification enables the algorithm to effectively escape local maxima and explore a wider search space, leading to improved convergence and optimal power point tracking. Furthermore, the adaptive nature of the PSO enhances the algorithm’s ability to respond to real-time changes in environmental conditions, making it particularly suitable for large- scale PV systems subjected to varying atmospheric factors. Here, “adaptive” denotes coefficient scheduling (C3) and a re-initialization trigger that responds to irradiance regime changes; “intelligent” denotes robust regime shift detection and safe duty ratio clamping. Across uniform, step change, and partial shading conditions, the proposed AIPSO achieves fast reconvergence and high tracking efficiency with negligible steady state oscillations, as summarized in the results. Building on this contribution, future research will focus on evaluating its scalability across different PV architectures and large-scale grid integration with real hardware setup.

This article presents an adaptive Particle Swarm Optimization (PSO) based Maximum Power Point Tracking (MPPT) algorithm for photovoltaic (PV) systems operating under dynamic conditions, including partial shading (PS) and sudden changing irradiance. Standard MPPT techniques such as Perturb and Observe (P&O) and Incremental Conductance (INC) frequently fail to track the global MPP under PS due to the presence of multiple local optima. While metaheuristic algorithms like standard PSO provide better global search capabilities, they tend to stagnate after convergence and are less responsive to abrupt irradiance changes. To address these limitations, the proposed approach integrates adaptive inertia weights and learning factors for improved convergence and accuracy, along with a reinitialization mechanism that resets particle positions upon detecting significant environmental changes. The algorithm is implemented in MATLAB/Simulink and evaluated under constant irradiance, step-change irradiance, and partial shading conditions. Simulation results demonstrate enhanced tracking performance, faster convergence, and increased robustness compared to conventional PSO, highlighting its effectiveness for real-world solar energy applications.

The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.

This article introduces Maximum Power Point Tracking (MPPT) approach based on Particle Swarm Optimization (PSO), designed to enhance energy extraction from photovoltaic (PV) systems under dynamic irradiance conditions. Conventional MPPT approaches such as Hill climbing (HC) and Incremental Conductance (IncCond) frequently fails to identify the global MPP under partial shading due to multiple local optima. While metaheuristic techniques like PSO offer improved global search capabilities, they are prone to early convergence and are less effective under rapidly changing irradiance conditions. This study proposes a reinitialization-enhanced PSO-based MPPT algorithm that detects environmental changes and dynamically resets particle positions to avoid stagnation in local optima. The algorithm is implemented in MATLAB/Simulink and evaluated under constant, stepchange, and partial shading scenarios. Results demonstrate superior tracking accuracy, faster convergence, and greater robustness compared to standard PSO, highlighting its potential for real-world PV applications under dynamic conditions.

Stand-Alone Photovoltaic (SAPV) systems play a vital role in providing clean and reliable electricity for remote and off-grid communities where grid expansion is economically or technically unfeasible. Their economic feasibility and technical reliability, however, depend strongly on accurate component sizing and system configuration, which require advanced optimization techniques. In this study, the Meerkat Optimization Algorithm (MOA) is applied to optimize two SAPV configurations. System 1 integrates a photovoltaic array, battery storage, and a hybrid inverter, while System 2 consists of a photovoltaic array, battery storage, a solar inverter, and a charge controller. The optimization focuses on minimizing Life Cycle Cost (LCC) and Levelized Cost of Energy (LCOE), which are widely recognized as reliable indicators of long-term cost-effectiveness and financial viability. To validate the performance of MOA, its results are benchmarked against three well-established metaheuristic algorithms: Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Slime Mould Algorithm (SMA). Simulation results show that System 1 consistently achieves lower LCC and LCOE compared to System 2, primarily due to its reduced component count and simplified integration. Moreover, MOA demonstrates enhanced optimization performance by converging more rapidly and delivering more stable solutions across multiple independent runs. In contrast, PSO, FA, and SMA exhibit slower convergence and greater variability in outcomes. Importantly, the performance differences are statistically meaningful, as MOA achieved consistently lower mean values and smaller standard deviations. These findings highlight MOA as an effective and reliable optimization tool for SAPV systems and provide practical insights to support sustainable rural electrification planning.

The development of microgrids (MGs) has attracted significant interest in the integration of renewable energy sources (RES) into the national grid. MGs offers a promising solution to enhance energy reliability and optimize energy consumption through the incorporation of distributed energy resources (DERs) such as solar photovoltaics (PV), wind turbines (WT), and energy storage systems (ESS). Among ESS, battery sizing plays a pivotal role in ensuring the stability and efficiency of MGs. Accurate battery sizing is crucial for the efficient operation and reliability of MGs. An undersized battery can lead to poor reliability in MG operations while an oversized battery can result in high capital and operating costs. This study presents a verification analysis using Two-Way Analysis of Variance (Two-Way ANOVA) to evaluate the effects of initial battery conditions and battery capacity on the operating cost of MGs, utilizing the Manta Ray Foraging Optimization (MRFO) algorithm. The statistical inference results demonstrate that the interaction between battery capacity and initial battery conditions significantly impacts the operating cost of the MG system.

This study utilizes a newly developed nature-inspired algorithm to provide a multi-objective optimization method for Distributed Generation (DG) placement and sizing in electrical distribution systems. The Marine Predator Algorithm (MPA) mimics marine hunters' foraging strategies, while the Osprey Optimization Algorithm (OOA) simulates osprey birds' hunting techniques. DG integration such as solar PV and wind turbines, enhances efficiency, reduces power losses, and improves voltage stability, but improper placement can cause under or overcompensation across the system. To address these challenges, this study incorporates fault current constraints, particularly three-phase balanced faults, in addition to lowering power loss and improving the voltage profile. The proposed techniques are tested through MATLAB simulations on the system of IEEE-69 buses, demonstrating their effectiveness across various case studies. Results show that MPA outperforms OOA, achieving a 69% Loss Reduction Index (LRI) compared to OOA's 66%, while also maintaining voltage stability and reducing fault current levels. MPA provides more stable simulation results with minimal deviations but requires longer computational time. This study offers valuable insights into optimal DG placement, emphasizing the need to balance multiple objective functions for enhanced reliability and performance in current power distribution systems.

Voltage uncertainty and power loss are the most prevalent complexities in the design of distribution systems. This issue may be resolved by effectively utilizing network reconfiguration that integrates Distributed Generation (DG). To function efficiently in distribution networks, DG requires proper installation with appropriate location and capacity in order to achieve the maximum benefits and reduce the risk of negative DG integration aspects. An effective method for solving the optimization issues is to utilize bio-inspired based metaheuristic optimization algorithms. This paper evaluates the efficacy of the Artificial Hummingbird Algorithm (AHA), a novel bio-inspired algorithm, in analyzing power losses in a distribution system that is implemented between various types of DG. The cost of energy and voltage profile are taken into account. The study implemented a thorough analysis involving DG type 1 and DG type 2. The proposed methodology was evaluated using the bus test system of 69. This study investigated three distinct situations of DG, scenario 1 represents the baseline condition, where there is no DG. Scenario 2 involves DG that injects active power, whereas scenario 3 involves DG that injects reactive power solely. The suggested approach was tested under various population situations to determine the ideal DG size and assess AHA prediction accuracy.

The Directional Overcurrent Relays (DOCRs) Coordination with Distributed Generation (DG) optimization problem is addressed in this study using the optimization method Particle Swarm Optimization (PSO). Changes in fault current, bus voltages, power flow, and reliability may result from DG integration. Thus, it might have an impact on the current protection coordination system. The formulation is built on a Mixed Integer Non-Linear Programming (MINLP) problem to address this DOCR issue. MATLAB was used to validate the technique on the IEEE-14 bus system, and Electrical Test Transient Analyzer Programming (ETAP) version 2021 software was used to model the test system. According to the simulation results, the suggested PSO with DG for Case 2 has reduced power loss by 6.24% and relay operating time by 46.79% when compared to PSO without the presence of DG.

This paper presents a modified grasshopper optimization algorithm (GOA) tailored for optimizing the power extraction capability of a solar photovoltaic (PV) system. The algorithm`s focus is on addressing one of the issues associated with mismatch loss (MML), particularly the mismatch (MM) in solar irradiance conditions, to attain maximum output power. The core strategy of the GOA involves optimizing the duty cycles of the converter to achieve the maximum power point (MPP) for the PV system. The PV system configuration comprises three PV modules connected in series and a SEPIC converter. To facilitate efficient maximum power point tracking (MPPT), the paper proposes using the GOA as a controlling mechanism. The study employs a comparative approach, contrasting the performance of the proposed system against established algorithms, such as PSO and GWO. The results of these evaluations exhibit the superior performance of the proposed GOA when compared to other optimization techniques. The GOA exhibits exceptional MPPT tracking characteristics, characterized by rapid tracking speed, heightened efficiency, and minimal oscillations within the PV system. Consequently, the GOA effectively addresses one of the MML issues.

Photovoltaic (PV) cells are integral in harnessing solar energy, yet their performance is hindered by excessive heat generation, impacting efficiency and sustainability. Addressing the challenge of efficiency loss in photovoltaic (PV) cells due to overheating, this study focuses on optimizing active water cooling control for PV modules. The aim is to develop a dynamic, sustainable model and integrate a PID controller tuned by Sine Cosine Algorithm (SCA), targeting optimal operating temperatures. This study introduces a dynamic model and a closed-loop control system to manage PV cell temperature, investigating the correlation between water flow and temperature regulation. Experimental data is gathered using a pseudo-random binary sequence (PRBS) as an excitation signal, forming the foundation of an Auto Regressive eXogenous (ARX) model. The closed-loop system incorporates a PID controller and tuned using the Sine Cosine Algorithm (SCA) to optimize performance. The resulting model is rigorously validated through experimental investigation, demonstrating its precision in capturing the system's dynamics. Moreover, the implementation of a controller-based cooling system substantiates the model's practical efficacy. The research demonstrates significant improvements when implementing a controller-based water-cooling system for photovoltaic (PV) modules. Compared to the baseline scenario without cooling, the system achieves a 34.5% reduction in average PV temperature (from 59.2 degrees C to 38.9 degrees C) and a 9.46% increase in average power output (from 196.7W to 215.3W). Moreover, this system utilizes only 248.8 liters of water, marking a substantial 64% decrease in water consumption compared to traditional free-flow cooling methods, which use 790.9 liters. The research demonstrates that the controller-based cooling approach is a sustainable option, delivering power output comparable to the free-flow method, yet significantly lowering water consumption. This research signifies a turning point for sustainability, offering an efficient and water-conscious approach for enhancing PV system performance, a crucial step toward a greener and more environmentally responsible energy future.

This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm's stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables - albedo values (25 %, 50 %, and 75 %), tilt angles (10 degrees, 25 degrees, and 35 degrees), and module elevations (0.5 m, 1.5 m, and 2 m) - was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system's Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.

Contingencies in power system are an ever-present problem for electrical engineers. They may cause the system to have low voltages due to active and reactive power deficiencies. Engineers shold consider the many solutions to approach this problem. The solution has to be appropriate to the contingency faced so as to be able to solve the issue without major damage to a power system. A drastic measure that can be used is by removing low voltage loads or ‘shedding’ the problematic loads. This method of Under Voltage Load Shedding is done by shedding or ‘islanding’ buses that have fallen to a predetermined minimum voltage for a predetermined time to prevent a cascade of blackouts in the widespread power system. The method to automate the process is by using two optimization techniques, Evolutionary Programming (EP) and Particle Swarm Optimization (PSO). This paper proposes a new hybrid algorithm, named Integrated Chaotic Swarm Based-Evolutionary Programming Optimization (ICSBEP) Technique for Voltage Security Control using Under Voltage Load Shedding (UVLS) approach. Voltage security is indicated by the reduction in voltage stability index, FVSI. In this study, EP, PSO and ICSBEP optimizations algorithms were developed and tested on a IEEE 30-bus reliability test system (RTS). Comparative studies were conducted to observe the advantages of the hybrid algorithm over traditional EP and PSO algorithms. The results obtained from the study can help manage the power quality of the system in ensuring that the customer receive secure electricity supply.

The networked microgrid (NMG) interconnects multiple microgrids (MGs) to improve load shedding and utilization of renewable energy sources (RES) effectively. However, using centralized energy management system (EMS) strategies for NMG may suffer from communication and computational burden, while decentralized EMS are costly and focus on local optimization. This paper presents a hierarchical EMS strategy for an islanded NMG. Firstly, the strategy is developed to minimize the operating costs within independent MGs. Based on the shortage/surplus MG results, the strategy is designed to make optimal decisions on a flexible inter-microgrid power exchange plan. Dispatchable distributed generators (DGs) share power to shortage MGs for load shedding minimization. In contrast, energy storage systems (ESSs) are used to maximize the utilization of surplus RES in the NMG. The NMG topology is configured in a radial or mesh structure to fulfill the network requirement. The proposed strategy is implemented on an islanded NMG test system of four interconnected IEEE MGs with different sizes (6, 9, 18, and 7 buses) and topologies (radial and mesh). The simulation results of decentralized and hierarchical energy management strategies for the NMG are presented under normal and abnormal operation modes. The results reveal that the proposed strategy reduces the total NMG operating costs by 36.56% and 53.10% under normal and abnormal conditions with zero load shedding and unutilized RES compared to the decentralized strategy.

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This paper presents an approach for the optimal integration of multiple distributed generation (DG) sources in a radial distribution system. The integration of DG sources poses various challenges such as can lead to higher power losses caused by reverse power flow, voltage exceeding secure limits, voltage stability, power quality, and economic operation. To address these challenges, a hybrid algorithm is proposed which combines the benefits of both Evolutionary Programming and Firefly Algorithm. The proposed hybrid Evolutionary - Firefly Algorithm is employed for the determination of the optimal size of the DG sources. The objective of the proposed algorithm is to minimize the total system power losses and improve the voltage profile. The algorithm considers various constraints including the DG capacity limits and voltage limits. A comprehensive case study is conducted on a radial distribution system to demonstrate the effectiveness of the proposed approach. The simulation results show that the hybrid algorithm can find the optimal size and location of DG sources while achieving the desired system performance. The integration of multiple DG sources leads to a significant reduction in power losses and improved voltage profile. Furthermore, the proposed approach provides a flexible framework for the optimal integration of DG sources in radial distribution systems, allowing for the accommodation of different types and capacities of DG sources. The proposed technique is tested on the IEEE Reliability Test systems, specifically the IEEE 69-bus. The combination of DG at bus 61 and bus 27 yields a loss reduction index of 94%.