Power grids face challenges in their infrastructure related to the integration of electric vehicles (EV). In particular, EV charging stations may induce instability in key system parameters such as substantial voltage drops, active power losses, and transformer overload due to high demand during charging periods. This article presents a seasonal reconfiguration strategy based on the differential evolution (DE) algorithm, aimed at enhancing system performance under highly variable and stochastic load profiles, particularly those driven by EV charging. The DEA algorithm is hybridized with the find-union (FU) algorithm to efficiently ensure network radiality throughout the optimization process. The proposed methodology is validated on a hybrid distribution system composed of the IEEE 33-bus network, a modified IEEE 13-bus system, and a specific 13-bus microgrid. Results have demonstrated that seasonal reconfiguration significantly reduces active power losses and mitigates transformer loading during critical demand hours, thereby quantifiably increasing the system’s performance. As an integral component of the proposed approach, an analysis of CO₂ emissions associated with energy losses is included, allowing a contextualized assessment of the environmental benefits of seasonal reconfiguration in various geographical areas.

The electrical power system is composed of three essential sectors, generation, transmission, and distribution, with the latter being crucial for the overall efficiency of the system. Enhancing the capabilities of active distribution networks involves integrating various advanced technologies such as distributed generation units, energy storage systems, banks of capacitors, and electric vehicle chargers. This paper provides an in-depth review of the primary strategies for incorporating these technologies into the distribution network to improve its reliability, stability, and efficiency. It also explores the principal metaheuristic techniques employed for the optimal allocation of distributed generation units, banks of capacitors, energy storage systems, electric vehicle chargers, and network reconfiguration. These techniques are essential for effectively integrating these technologies and optimizing the active distribution network by enhancing power quality and voltage level, reducing losses, and ensuring operational indices are maintained at optimal levels.

As the electrical grid modernizes, grid reconfiguration and optimization problems become increasingly complex, significantly increasing the computational burden. Faster and more efficient algorithms are required to operate without specialized hardware in both offline and on-site applications. A critical step in grid reconfiguration is evaluating the radiality of every potential solution proposed by the optimization algorithm in each iteration. Alternatives for this evaluation include Prim's algorithm---the most widespread---eigenvalue analysis with the Fiedler theorem, Depth-First Search~(DFS), and the zero-eigenvalue Multiplicity in the Laplacian matrix. This work first proposes efficient alternatives to calculate the Fiedler value and evaluate matrix multiplicity, and then evaluates the performance of these four strategies in MATLAB and in a dedicated micro-computer under the Processor-in-the-Loop premise. The results indicate that DFS has a mean execution time nearly eight times less than Prim's algorithm, with the Multiplicity algorithm performing similarly on the embedded device. In MATLAB, the Multiplicity algorithm performs slightly better than DFS for grids with fewer than 50 buses. Although the Fiedler algorithm reduces execution time by half compared to Prim's, its performance is still higher than both DFS and the Multiplicity algorithm.