A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.

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.

A particle swarm optimization technique (PSO) combined with a greedy algorithm (PRIM) is proposed to design low voltage distribution networks (LVDNs), from scratch. The approach is able to construct a radial network using the GPS coordinates of the utility poles, furthermore, it can also find the optimal placement for the distribution transformer (DT) and select the most suitable conductors for each branch. The evaluation of the distribution network is based on fuzzy rules, to obtain a satisfaction degree regarding the imposed constraints of the system, all this in order to minimize voltage deviation (VD) and quality issues (QI). Finally, several tests have been made in four real LVDNs, where the effectiveness of the proposed algorithm to design them has been proved.