Optimal multi-objective distribution system reconfiguration with multi criteria decision making-based solution ranking and enhanced genetic operators

In electrical distribution system optimisation, the presence of multiple conflicting objectives is effectively addressed by using Pareto front analysis. This paper deals with optimal reconfiguration considering network losses and energy not supplied as multi-objectives. A set of original contributions are provided with reference to the construction and updating of the best-known Pareto front using a genetic algorithm-based solver. The crossover operator is extended to address multi-objective solutions. The mutation operator is extended to handle a broader number of cases. Multi-objective solution ranking is applied by resorting to multi criteria decision making methods during the creation of the offsprings in the crossover operator, as well as to provide an automatic support for the decision maker to identify the preferable solution in the final Pareto front. The proposed approach is applied on two reference test networks, for which the complete Pareto front is calculated from the entire set of multi-objective solutions. The resulting best-known Pareto front is compared with the complete Pareto front using a metric based on geometrical considerations. This comparison framework is helpful to assess the performance of the multi-objective optimisation solvers