In the contemporary landscape of power systems, the escalating integration of renewable energy resources and electric vehicle infrastructures into distribution networks has intensified the imperative to ensure power quality, operational optimization, and system reliability. Distribution network reconfiguration emerges as a pivotal strategy to mitigate power losses, facilitate the seamless assimilation of renewable generation, and regulate the charging and discharging dynamics of EVs, thereby constituting a critical endeavor in modern electrical engineering. While the Particle Swarm Optimization algorithm is renowned for its rapid convergence and effective exploitation of solution spaces, its capacity to thoroughly explore complex search domains remains limited, particularly in multifaceted optimization challenges. Conversely, the Grey Wolf Optimization algorithm excels in global exploration, offering robust mechanisms to circumvent local optima traps. Leveraging the complementary strengths of these approaches, this study proposes a hybrid PSO-GWO framework to address the distribution network reconfiguration problem, explicitly accounting for the integration of renewable energy sources and EV systems. Empirical validation, conducted on the IEEE 33-bus test system across diverse operational scenarios, underscores the efficacy of the proposed methodology, revealing exceptional precision and dependability. Notably, the approach achieves substantial reductions in power losses during peak demand periods with distributed generation incorporation while maintaining voltage profiles within the stringent operational bounds of 0.94 to 1.0 per unit, thus ensuring stability amidst variable load conditions. Comparative analyses further demonstrate that the hybrid method surpasses conventional optimization techniques, as evidenced by enhanced convergence rates and superior objective function outcomes. These findings affirm the proposed strategy as a potent tool for advancing the resilience and efficiency of next-generation distribution networks.

Reconfiguring distribution networks involves modifying their topological structure by managing switch states. This process is crucial in smart grids, as it can isolate faults, minimize power loss, and enhance system stability. However, in existing research, the reconfiguration task is often treated as a problem of either single- or multi-objective optimization and frequently overlooks the issue's multimodality. As a result, the solutions derived may be inadequate or unfeasible when facing environmental changes. In this study, the objective function of minimizing power loss considers the case of faults in the distribution grid. Coordinating the initial population division of the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) and the Teaching and Learning-Based Optimization (TLBO) algorithms accelerates the process of finding the optimal solution, resulting in faster and more reliable results. The proposed method was tested on the IEEE-33 bus test system and was compared with other methods, demonstrating reliable results and superior efficiency.

The problem of distribution reconfiguration is an important issue in the optimal operation of distribution networks. Nowadays, with the diverse development of renewable energy sources, the uncertainty of the load becomes more complex, and the need for competitive retail electricity markets is more evident. This paper presents an optimal solution to this problem, utilizing the global advantage of the simulated annealing algorithm, improving the time parameter, and combining it with the rapid mutation ability of the genetic algorithm. Simultaneously, the Zbus, EBE, and PS models were integrated to optimize the costs, considering transmission costs under constraints related to taxes and economic indicators when connected to the distribution grid. The proposed method was simulated and tested on the IEEE 33-node standard power grid with three different scenarios. The simulation results showed that the proposed method provides optimal results, which can be applied to calculate the optimal operation of the distribution grid when participating in retail electricity markets.

The maximum power point tracking (MPPT) solutions improve power generation efficiency, quickly stabilizing the output waveform of photovoltaic (PV) systems under variable operating conditions. Along with new algorithms, improved and adjusted methods to exploit energy from PV systems are increasingly being researched and proposed. However, the proposed solutions based on the traditional algorithms and their improvements have poor performance, while the advanced algorithms or hybrid methods bring high performance but need to be simplified, and the response speed is higher. Moreover, a suitable PV configuration makes choosing a simple but highly efficient algorithm, especially in low-power PV system applications such as rooftop solar power, traffic lights, and moving vehicles…where the number of PV panels is insufficient to implement flexible configurations. This paper proposes a modified version of the Perturb and Observe (MPO) algorithm to improve MPPT performance and increase convergence speed in the parallel structure of PV panels. The Short-Circuit Current (I_sc) and Open-Circuit Voltage (V_oc) are calculated directly at specific operating conditions to quickly determine the potential maximum power point (MPP) that will reduce power interruptions and increase power generation efficiency compared to periodic updates. Therefore, the proposed solution converges faster, with higher efficiency, and the output signal in static and dynamic MPPT situations is more stable. The results show that the highest efficiency in simulation and experiment is 99.99% and 99.93%, respectively, while the convergence speed is 0.01 s and 0.03 s, respectively. They are better than the traditional Perturb and Observe (P&O) algorithm, the Variable Step Size Perturb and Observe (VSSP&O) method, and the Particle Swarm Optimization (PSO) technique under the same operating conditions. In addition, its performance and convergence speed are also compared with the latest introduced algorithms. The results show that it is valuable and reliable for parallel PV configuration.

The increasing penetration of intermittent Renewable Energy Sources (RES), coupled with the decentralized architecture of modern power distribution networks, has introduced substantial challenges in maintaining system stability and ensuring power quality—particularly under fault conditions and nonlinear operating regimes. This paper proposes a dynamic self-healing model for smart distribution systems based on an Improved Whale Optimization Algorithm (IWOA), with the objective of simultaneously optimizing active power loss, voltage deviation, Total Harmonic Distortion (THD), and Phase Voltage Unbalance Ratio (PVUR). The proposed IWOA incorporates a nonlinear shrinking mechanism, discrete solution mapping, and a normalized, equally weighted multi-objective function structure. These enhancements significantly improve convergence behavior, solution accuracy, and optimization performance in complex combinatorial search spaces. The proposed framework is validated on a modified IEEE 33-bus distribution system featuring unbalanced topologies, harmonic disturbances, and distributed RES integration. Simulation results demonstrate that IWOA outperforms conventional metaheuristics such as Particle Swarm Optimization (PSO), Differential Evolution (DE), and the original WOA in terms of convergence speed, energy efficiency, and power quality enhancement. This study highlights a promising direction for advanced automated optimization strategies in resilient and sustainable energy distribution infrastructures.