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A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty

机译：不确定性下战略能源规划的机器学习和分布鲁棒优化框架

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This paper investigates how the choice of stochastic approaches and distribution assumptions impacts strategic investment decisions in energy planning problems. We formulate a two-stage stochastic programming model assuming different distributions for the input parameters and show that there is significant discrepancy among the associated stochastic solutions and other robust solutions published in the literature. To remedy this sensitivity issue, we propose a combined machine learning and distributionally robust optimization (DRO) approach which produces more robust and stable strategic investment decisions with respect to uncertainty assumptions. DRO is applied to deal with ambiguous probability distributions and Machine Learning is used to restrict the DRO model to a subset of important uncertain parameters ensuring computational tractability. Finally, we perform an out-of-sample simulation process to evaluate solutions performances. The Swiss energy system is used as a case study all along the paper to validate the approach.
机译：本文调查了随机方法选择如何影响能源规划问题的战略投资决策。我们制定了一个两级随机编程模型，假设输入参数的不同分布，并表明在文献中发表的相关随机解决方案和其他强大的解决方案之间存在显着差异。为了解决这种敏感性问题，我们提出了一种组合机器学习和分布稳健优化（DRO）方法，其在不确定假设方面产生了更强大和稳定的战略性的投资决策。应用程序以处理模糊的概率分布和机器学习用于将DRO模型限制为确保计算途径的重要不确定参数的子集。最后，我们执行一个采样的仿真过程来评估解决方案性能。瑞士能源系统用作沿纸张的案例研究以验证该方法。

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