A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation

Abstract

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.

Street Lighting and Charging Stations with PATs Location Applying Artificial Intelligence

“…The PATs placements focus on the major electricity generation; however, the present study tries to focus on small Distributed Generators (DGs) reducing power losses on distribution [5,6]; unlike grid-connected systems they can be installed at each consumer node which implies a great number of configurations. Thus, this presents a challenging combinatorial optimization problem, such as the one presented in the challenge of locating DGs off-grid on the electrical network to supply electricity to houses [6].…”