Abstract

The integration of a renewable energy distributed generation into microgrids poses a significant constraint in the way power is managed, further so due to the natural variability in renewable generation and the variability in the load demands. To address these issues, this paper introduces a novel approach to the Spider Swarm Optimization (SSO) algorithm, the Dynamic LoadAdaptive Power Splitting (DLAPS) strategy, to enable real-time adaptive power sharing and enhance system resilience. Unlike the classical methods of power allocation that are static, according to which the power is divided between sources of renewable energy and storage systems, and between these sources and critical loads, the DLAPS-SSO applies the idea of a machine learning based predictive model to predict the power and dynamically optimize power allocation between the sources of renewable energy and storage systems and the sources and the critical loads. The model provides a multi-objective optimization framework that aims to minimize power losses and grid frequency variations, and to maximize the system’s resilience to disturbances, including disconnection from the grid, component malfunctions, and the availability of renewable energy sources. The comparison of simulation results with those of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) methods shows that the energy efficiency of the DLAPS-SSO increases by 15%–20%, and the amount of power losses across various load profiles decreases by 30%–35%. Moreover, the proposed solution offers 60% faster recovery time in case of grid disconnection, maintains 65.9% of the critical load in case of component failure, and provides 40%–50% less resilience than state-of-the-art techniques. The analysis of seasons and real data shows that there is stability of the behavior with the increase of efficiency (18%–22% during winter, and 23%–25% during summer), and the ability of the suggested approach to be robust when changing plant configuration/operation. Integration of optimization of dynamic load management and adaptive power splitting will spur microgrid control strategies and offer a viable strategy to stabilize the grid, reduce operation costs, and enable sustainable changes in energy transformations. The results demonstrate the essential role of bio-inspired optimization and reactivity in the next generation of smart grids.

Abstract

This paper presents the design and performance analysis of an Ultra Gain Boost Converter (UGBC) intended for solar-powered electric vehicle (SPEV) applications. The proposed converter is derived from a cascaded boost–boost structure with a capacitor–diode voltage-lift cell, allowing high voltage gain without operating at extreme duty ratios. The proposed structure provides continuous input current, improved voltage boosting capability, and reduced voltage stress on semiconductor devices, making it suitable for photovoltaic based energy systems. Complete steady-state analysis, voltage-gain derivation, device stresses estimation, power-loss estimation, and small-signal dynamic modelling are carried out to evaluate the converter characteristics. The UGBC topology is validated with MATLAB/Simulink simulations for an operating condition of 24 to 72 V at a duty ratio of 0.33, indicating stable voltage regulation, low current ripple, and higher efficiencies. Additionally, system-level performance is given by interfacing the converter with a BLDC motor drive, where stable speed regulation and smooth torque response are observed. The results confirm that the proposed UGBC converter with a capacitor–diode cell is a feasible and efficient solution for high-gain Ultra Gain DC–DC conversion in solar-based electric mobility applications.OPEN ACCESS Received: 22/11/2025 Accepted: 29/01/2026 Published: 16/04/2026

Abstract

The integration of renewable energy sources (RES) into electrical power systems introduces critical challenges to grid stability, including frequency deviation, voltage fluctuation, and reduced transient performance. This study investigates the impact of increasing RES penetration (15%, 30%, 45%, and 60%) on grid stability using the IEEE 39-bus benchmark system. A coordinated mitigation framework integrating battery energy storage systems (BESS), synthetic inertia, and advanced inverter-based controls is proposed and evaluated. Simulation results demonstrate that at 60% RES penetration without mitigation, the frequency nadir declines to 49.32 Hz, the rate of change of frequency (RoCoF) increases to 1.82 Hz/s, voltage deviations exceed 9.3%, and the critical clearing time (CCT) reduces to 180 ms, indicating significant stability deterioration. The proposed mitigation strategy improves these metrics to 49.76 Hz, 0.94 Hz/s, 4.6%, and 260 ms, respectively, representing improvements of+0.44 Hz, –48.4%, –50.5%, and+44.4%. Benchmarking against recent literature confirms the superior performance of the coordinated approach. These findings provide quantitative guidance for grid planners and operators to maintain reliable operation under high renewable penetration scenarios.OPEN ACCESS Received: 19/01/2026 Accepted: 28/02/2026

Abstract

The integration of renewable energy sources (RES) into electrical power systems introduces critical challenges to grid stability, including frequency deviation, voltage fluctuation, and reduced transient performance. This study investigates the impact of increasing RES penetration (15%, 30%, 45%, and 60%) on grid stability using the IEEE 39-bus benchmark system. A coordinated mitigation framework integrating battery energy storage systems (BESS), synthetic inertia, and advanced inverter-based controls is proposed and evaluated. Simulation results demonstrate that at 60% RES penetration without mitigation, the frequency nadir declines to 49.32 Hz, the rate of change of frequency (RoCoF) increases to 1.82 Hz/s, voltage deviations exceed 9.3%, and the critical clearing time (CCT) reduces to 180 ms, indicating significant stability deterioration. The proposed mitigation strategy improves these metrics to 49.76 Hz, 0.94 Hz/s, 4.6%, and 260 ms, respectively, representing improvements of+0.44 Hz, –48.4%, –50.5%, and+44.4%. Benchmarking against recent literature confirms the superior performance of the coordinated approach. These findings provide quantitative guidance for grid planners and operators to maintain reliable operation under high renewable penetration scenarios.OPEN ACCESS Received: 19/01/2026 Accepted: 28/02/2026