Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation

This paper proposes a multi-objective approach to a distribution network planning process that deals with the challenges derived from the integration of Distributed Generation (DG). The proposal consists of a multi-objective version of the well-known Evolutionary Particle Swarm Optimization method (MEPSO). A broad performance comparison is made between the MEPSO and other multi-objective optimization meta-heuristics, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a Multi-objective Tabu Search (MOTS), using two distribution networks in a given DG penetration scenario. Although the three methods proved to be applicable in distribution system planning, the MEPSO algorithm has presented promising attributes and a constant and high level performance when compared to other methods.

► The impact of DG is evaluated using multi-objective optimization tools. ► A new multi-objective optimization algorithm is proposed. ► Comparison of three multi-objective optimization meta-heuristics is provided. ► The methodology is used considering two test feeders.