In this paper, we tackle the distribution network expansion planning (DNEP) problem by employing two evolutionary algorithms (EAs): the classical Genetic Algorithm (GA) and a linkage-learning EA, specifically a Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). We furthermore develop two efficiency-enhancement techniques for these two EAs for solving the DNEP problem: a restricted initialization mechanism to reduce the size of the explorable search space and a means to filter linkages (for GOMEA) to disregard linkage groups during genetic variation that are likely not useful. Experimental results on a benchmark network show that if we may assume that the optimal network will be very similar to the starting network, restricted initialization is generally useful for solving DNEP and moreover it becomes more beneficial to use the simple GA. However, in the more general setting where we cannot make the closeness assumption and the explorable search space becomes much larger, GOMEA outperforms the classical GA.
Additional Metadata
Keywords Electricity, Distribution Networks, Capacity Planning, Optimal Mixing, Linkage Learning
THEME Logistics (theme 3), Energy (theme 4)
Publisher ACM
Editor C. Igel
Persistent URL
Project Computational Capacity Planning in Electricity Networks
Conference Genetic and Evolutionary Computation Conference
Luong, N.H, Grond, M.O.W, La Poutré, J.A, & Bosman, P.A.N. (2014). Efficiency enhancements for evolutionary capacity planning in distribution grids. In C Igel (Ed.), Proceedings of Genetic and Evolutionary Computation Conference 2014 (Companion Material) (pp. 1189–1196). ACM. doi:10.1145/2598394.2605696