Comparison of temporal resolution selection approaches in energy systems models
Comparison of temporal resolution selection approaches in energy systems models
复制标题
DOI：
10.1016/j.energy.2022.123969
复制
发表时间：
2022-04-21
期刊：
影响因子：
9
通讯作者：
Brown, Maxwell
中科院分区：
文献类型：
--
作者：
Marcy, Cara;Goforth, Teagan;Brown, Maxwell
电力部门的产能扩张模型用于通过模拟电力使用的投资和运营决策来预测未来几十年的决策。由于模型性能的限制，这些模型通常不会显式地模拟一年内的每一个小时，而是模拟具有代表性的时间段(小时组)。本文评估了三种不同的选择时间段的方法：顺序、分类和聚类，涉及广泛的时间段数量，总共204个时间段。为了衡量每个配置文件准确表示数据的能力的性能，将每个配置文件的时间段的均方根误差与数据的原始每小时数据进行比较。还测量了跨区域的时间排列(即，多大风天跨区域排列的频率)。不同的空间分辨率被应用于时间选择方法的子集，以考察空间分辨率对性能的影响。本文提供了一个框架，用于衡量不同的时间选择方法的价值，并为能源系统模型添加更多的细粒度数据。总体而言，在所有评估的数据集中，多标准聚类产生的均方根误差最低，并提供了可再生能源发电和电力需求之间相互交织的关系的完整视图。由爱思唯尔有限公司出版。这是一篇在CC by License(http://creativecommons.org/licenses/by/4.0/).下的开放获取文章
Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model per-formance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different ap-proaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holis-tic view of the intertwined relationships between renewable generation and electricity demand.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).