Evaluation of methods to select representative days for the optimization of polygeneration systems
Evaluation of methods to select representative days for the optimization of polygeneration systems
复制标题
多联产系统优化代表性天数选择方法的评估
DOI：
10.1016/j.renene.2019.11.048
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发表时间：
2020
期刊：
影响因子：
8.7
通讯作者：
A. Lázaro
中科院分区：
文献类型：
--
作者：
E. Pinto;L. Serra;A. Lázaro
考虑一年中每小时时段的多电联产系统的优化是一项计算要求很高的任务，因此，采用选择代表日的方法来合理再现全年。然而，一种方法的适用性在很大程度上取决于系统中涉及的时间序列的可变性。这项工作通过对两个不同地点的建筑物的并网和独立多电系统进行优化，比较了平均方法、k-Medoids和OPT选择代表性天数的方法。针对多电联产系统的优化，对每种方法获得的代表性天数的适用性进行了评估。通过使用14个有代表性的天，实现了小于5%的大小误差，并且相对于全年数据的计算时间从几个小时减少到几秒钟。结果表明，平均方法适用于时间序列数据变异性较低的情况；但是，当时间序列具有较高的随机变异性时（例如考虑风能），OPT方法表现出更好的性能。此外，通过结合thek- mediids和OPT方法，开发了一种选择代表性天数的新方法，尽管其实现需要额外的计算工作量。
The optimization of polygeneration systems considering hourly periods throughout one year is a computationally demanding task, and, therefore, methods for the selection of representative days are employed to reproduce reasonably the entire year. However, the suitability of a method strongly depends on the variability of the time series involved in the system. This work compares the methods Averaging,k-Medoids and OPT for the selection of representative days by carrying out the optimization of grid-connected and standalone polygeneration systems for a building in two different locations. The suitability of the representative days obtained with each method were assessed regarding the optimization of the polygeneration systems. Sizing errors under 5% were achieved by using 14 representative days, and the computational time, with respect to the entire year data, was reduced from hours to a few seconds. The results demonstrated that the Averaging method is suitable when there is low variability in the time series data; but, when the time series presents high stochastic variability (e.g., consideration of wind energy), the OPT method presented better performance. Also, a new method has been developed for the selection of representative days by combining thek-Medoids and OPT methods, although its implementation requires additional computational effort.