On Time Aggregation Techniques for Power System Planning Applications
Date
2023-01-10
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Due to the evergrowing renewable energy resources and their penetration in the electrical grid, the power system operators face the difficulty of handling such uncertainty while obtaining the result of operation and planning studies in a tractable time. This thesis is focused on developing time aggregation frameworks for compressing renewable energy as well as electricity demand data into a limited number of representative periods from which the complex power system studies can be solved in a reasonable time without reducing the loss of accuracy. First, an overview of different parts of a clustering process is provided. The pros and cons of methods are discussed to help the reader understand the rationale behind the author narrows down the comparative methods in this thesis. A section is also provided to discuss the centroid selection process, which is often an optional step in the clustering process. Yet, it is a crucial step in the time aggregation in the context of power system studies. This thesis tackles the time aggregation challenge by developing a method based on an elastic-based distance that can reflect the volatility of time series data such as wind power while preserving its co-movement with electricity demand. Next, the spatiotemporal factor is taken into account and a method is proposed to handle time aggregation in the presence of several renewable energy resources. Finally, this thesis brings in the concept of stability, and develops a framework that can help with obtaining a set of representative periods that are more stable compared to the existing methods. In all of the aforementioned works, the proposed approach is compared against a set of comparative methods considering both data- and model-based evaluation. Regarding the former, the methods are compared according to their performance in reflecting different characteristics of data. In the latter, the method is evaluated in the context of a power system problem in an electrical network. To better show the impact of our method in preserving shape, energy storage units are added to the network. Different indices are measured to reflect the performance of the proposed approach from different perspectives.
Description
Keywords
Renewable Energy, Time Aggregation, Machine Learning, Power System Planning, Machine Learning, Unsupervised Learning
Citation
Sarajpoor, N. (2022). On time aggregation techniques for power system planning applications (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.