This paper tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. We compare two evolutionary algorithms (EAs) for optimizing expansion plans: the classic genetic algorithm (GA) with uniform crossover and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) that learns and exploits linkage information between problem variables. We study the impact of incorporating different levels of problem-specific knowledge in the variation operators as well as two constraint-handling techniques: constraint domination and repair mechanisms. Experiments show that the use of problem-specific variation operators is far more important for the classic GA to find high-quality solutions to the DNEP problem. GOMEA is found to have far more robust performance even when an out-of-box variant is used that doesn't exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for real-world applications like DNEP, EAs that have the ability to model and exploit problem structures, such as GOMEAs and estimation-of-distribution algorithms, should be given priority, especially when problem-specific knowledge is not straightforward to exploit, e.g. in the case of black-box optimization.
Additional Metadata
Keywords Power System, Capacity Planning, Linkage Learning, Variation Operators, Problem-Specific Knowledge
THEME Energy (theme 4), Software (theme 1)
Publisher ACM
Persistent URL dx.doi.org/10.1145/2739480.2754682
Project Computational Capacity Planning in Electricity Networks
Conference Genetic and Evolutionary Computation Conference
Citation
Luong, N.H, La Poutré, J.A, & Bosman, P.A.N. (2015). Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning. In Proceedings of Genetic and Evolutionary Computation Conference 2015 (GECCO 2015). ACM. doi:10.1145/2739480.2754682