This paper addresses the problem of electric distribution network expansion under condition of uncertainty in the evolution of node loads in a time horizon. An immune-based evolutionary optimization algorithm is developed here, in order to find not only the optimal network, but also a set of suboptimal ones, for a given most probable scenario. A Monte-Carlo simulation of the future load conditions is performed, evaluating each such solution within a set of other possible scenarios. A dominance analysis is then performed in order to compare the candidate solutions, considering the objectives of: smaller infeasibility rate, smaller nominal cost, smaller mean cost and smaller fault cost. The design outcome is a network that has a satisfactory behavior under the considered scenarios. Simulation results show that the proposed approach leads to resulting networks that can be rather different from the networks that would be found via a conventional design procedure: reaching more robust performances under load evolution uncertainties.
Carrano, E. G., Guimaraes, F. G., Takahashi, R. H. C., Neto, O. M., & Campelo, F. (2007). Electric Distribution Network Expansion Under Load-Evolution Uncertainty Using an Immune System Inspired Algorithm. IEEE Transactions on Power Systems, 22(2), 851 - 861. https://doi.org/10.1109/tpwrs.2007.894847