论文标题
在气候不确定性下进行能源计划的稳定弯曲器分解
Stabilized Benders decomposition for energy planning under climate uncertainty
论文作者
论文摘要
本文将Benders的分解应用于两个阶段的随机问题，以在气候不确定性下进行能源计划，这是可再生能源系统设计的关键问题。为了提高性能，我们将各种改进对弯曲者的分解进行调整到问题的特征上 - 一个简单的连续主题，很少但很大的子问题。主要重点是稳定，特别是将既定的捆绑方法与连续问题的二次信任区域方法进行了比较。广泛的计算比较表明，所有稳定方法都可以显着减少计算时间。但是，二次信任区域和非季度框 - 步骤方法是最强大，最直接实现的。当并行化时，引入的算法的表现优于Benders的分解倍数。与现成的求解器相比，当场景数量增加时，计算时间保持恒定。总而言之，该算法可以对具有大量气候年限的可再生能源系统进行强有力的规划。除了气候不确定性之外，它还可以在能源计划计算中进行大量其他分析，例如，内源性学习和建模以生成替代方案。
This paper applies Benders decomposition to two-stage stochastic problems for energy planning under climate uncertainty, a key problem for the design of renewable energy systems. To improve performance, we adapt various refinements for Benders decomposition to the problem's characteristics -- a simple continuous master-problem, and few but large sub-problems. The primary focus is stabilization, specifically comparing established bundle methods to a quadratic trust-region approach for continuous problems. An extensive computational comparison shows that all stabilization methods can significantly reduce computation time. However, the quadratic trust-region and the non-quadratic box-step method are the most robust and straightforward to implement. When parallelized, the introduced algorithm outperforms the vanilla version of Benders decomposition by a factor of 100. In contrast to off-the-shelf solvers, computation time remains constant when the number of scenarios increases. In conclusion, the algorithm enables robust planning of renewable energy systems with a large number of climatic years. Beyond climate uncertainty, it can make an extensive range of other analyses in energy planning computationally tractable, for instance, endogenous learning and modeling to generate alternatives.
