Centrum Wiskunde & Informatica (CWI), 1098 XG Amsterdam, The Netherlands
Centrum Wiskunde & Informatica (CWI), 1098 XG Amsterdam, The Netherlands Delft University of Technology, 2628 CD Delft, The Netherlands
Evol Comput. 2018 Fall;26(3):471-505. doi: 10.1162/EVCO_a_00209. Epub 2017 Apr 7.
This article 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. Because of many real-world details involved, the structure of the problem is not exploited easily using mathematical programming techniques, for which reason we consider solving this problem with evolutionary algorithms (EAs). We compare three types of EAs for optimizing expansion plans: the classic genetic algorithm (GA), the estimation-of-distribution algorithm (EDA), and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). Not fully knowing the structure of the problem, we study the effect of linkage learning through the use of three linkage models: univariate, marginal product, and linkage tree. We furthermore experiment with the impact of incorporating different levels of problem-specific knowledge in the variation operators. Experiments show that the use of problem-specific variation operators is far more important for the classic GA to find high-quality solutions. In all EAs, the marginal product model and its linkage learning procedure have difficulty in capturing and exploiting the DNEP problem structure. GOMEA, especially when combined with the linkage tree structure, is found to have the most robust performance by far, even when an out-of-the-box variant is used that does not exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for power system expansion planning problems, EAs that have the ability to effectively model and efficiently exploit problem structures, such as GOMEA, should be given priority, especially in the case of black-box or grey-box optimization.
本文解决了配电网扩展规划（DNEP）问题，该问题需要由配电网运营商来决定应在何处以及何时引入电网增强措施，以满足未来的电力需求。由于涉及许多实际细节，因此该问题的结构不易使用数学规划技术来解决，因此我们考虑使用进化算法（EA）来解决此问题。我们比较了三种用于优化扩展计划的 EA：经典遗传算法（GA），分布估计算法（EDA）和基因池最优混合进化算法（GOMEA）。由于不完全了解问题的结构，我们通过使用三种链接模型（单变量，边际产品和链接树）来研究链接学习的效果。此外，我们还尝试了在变异算子中包含不同程度的特定于问题的知识对问题的影响。实验表明，对于经典 GA 来说，使用特定于问题的变异算子对于找到高质量的解决方案非常重要。在所有 EA 中，边际产品模型及其链接学习过程都难以捕获和利用 DNEP 问题结构。GOMEA 尤其是与链接树结构结合使用时，被发现具有迄今为止最稳健的性能，即使使用不利用特定于问题的知识的即开即用的变体也是如此。基于实验，我们建议在为电力系统扩展规划问题选择优化算法时，应优先考虑具有有效建模和高效利用问题结构的 EA，例如 GOMEA，尤其是在黑盒或灰盒优化的情况下。
