An Immune Inspired Memetic Algorithm for power distribution system design under load evolution uncertainties

This work proposes an immune inspired memetic algorithm for the expansion planning of electric distribution systems. This algorithm is based on a clonal selection algorithm and a local search method which is built using network distance concepts abstracted from continuous spaces. The memetic algorithm is intended to find not only the optimal solution for the design conditions, but a whole set of viable solutions, that can be considered as alternatives under perturbed operation conditions. Those alternatives are used for handling with load evolution uncertainties, which are inherently related with long term evaluation of the distribution system. The post-optimization analysis of solutions has been made using a Monte Carlo simulation and a multiobjective sensitivity analysis, in order to estimate their robustness under perturbed load conditions. The results achieved by the proposed algorithm in a practical problem indicate that this method can be more suitable for designing distribution system under load evolution uncertainties.