We propose an algorithm for scheduling subject to time‐variable electricity prices using nonlinear process models that enables long planning horizons with fine discretizations. The algorithm relies on a reduced‐space formulation and enhances our previous work (Schäfer et al., Comput Chem Eng, 2020;132:106598) by a sensitivity‐based refinement procedure. We therein expose the coefficients of the wavelet transform of the time series of independent process variables to the optimizer. The problem size is reduced by truncating the transform and iteratively adjusted using Lagrangian multipliers. We apply the algorithm to the scheduling of a multi‐product air separation unit. The nonlinear power consumption characteristic is replaced by an artificial neural network trained on data from a rigorous model. We demonstrate that the proposed algorithm reduces the number of optimization variables by more than one order of magnitude, whilst furnishing feasible schedules with insignificant losses in objective values compared to solutions considering the full dimensionality.

中文翻译：

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使用基于灵敏度的小波变换截断，对时变电价进行非线性调度

我们提出了一种使用非线性过程模型根据时变电价进行调度的算法，该算法可以实现长期规划和精细离散化。该算法依赖于缩小空间的公式并增强了我们以前的工作（Schäfer等人，Comput Chem Eng，2020; 132：106598）。我们在其中向优化器公开了独立过程变量的时间序列的小波变换的系数。通过截断变换来减少问题的大小，并使用拉格朗日乘数进行迭代调整。我们将该算法应用于多产品空分设备的调度。非线性功耗特征被人工神经网络代替，该人工神经网络对来自严格模型的数据进行了训练。我们证明了所提出的算法将优化变量的数量减少了一个以上的数量级，同时与考虑了全维的解决方案相比，为可行的计划提供了客观值的微不足道的损失。