论文标题
更难，更好，更快，更强大：理解和改善大型能源系统模型的障碍
Harder, better, faster, stronger: understanding and improving the tractability of large energy system models
论文作者
论文摘要
基于线性编程的能源系统模型的规模一直在增长，随着对可再生能源为高空间和时间细节的可再生能源的需求越来越多。较大的模型导致了很高的计算要求。此外，模型中看似很小的变化可能会导致运行时的巨大差异。在这里，我们研究了解决此问题的措施。我们回顾了典型的能源系统模型的数学结构，并讨论了稀疏性，退化性和较大数值范围的问题。我们介绍并测试一种自动扩展模型以改善数值范围的方法。我们测试了此方法以及调整以建模公式和求解器偏好，发现调整可能会对运行时产生重大影响。特别是，没有交叉的屏障方法可能非常快，但会影响所得最佳解决方案的结构。我们以一系列有关能源系统模块的建议结束。
Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue. We review the mathematical structure of a typical energy system model, and discuss issues of sparsity, degeneracy and large numerical range. We introduce and test a method to automatically scale models to improve numerical range. We test this method as well as tweaks to model formulation and solver preferences, finding that adjustments can have a substantial impact on runtime. In particular, the barrier method without crossover can be very fast, but affects the structure of the resulting optimal solution. We conclude with a range of recommendations for energy system modellers.
