Selecting Representative Days for Capturing the Implications of Integrating Intermittent Renewables in Generation Expansion Planning Problems

• Published in: IEEE transactions on power systems 2017-05, Vol.32 (3), p.1936-1948
• Main Authors: Poncelet, Kris, Hoschle, Hanspeter, Delarue, Erik, Virag, Ana, Drhaeseleer, William
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Clustering algorithms; Computational modeling; Energy-system planning; generation expansion planning; Indexes; Load modeling; Optimization; Partitioning algorithms; Planning; power system economics; power system modeling; Time series analysis; wind energy integration
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2016.2596803

Due to computational restrictions, energy-system optimization models (ESOMs) and generation expansion planning models (GEPMs) frequently represent intraannual variations in demand and supply by using the data of a limited number of representative historical days. The vast majority of the current approaches to select a representative set of days relies on either simple heuristics or clustering algorithms and comparison of different approaches is restricted to different clustering algorithms. This paper contributes by: i) proposing criteria and metrics for evaluating representativeness, ii) providing a novel optimization-based approach to select a representative set of days, and iii) evaluating and comparing the developed approach to multiple approaches available from the literature. The developed optimization-based approach is shown to achieve more accurate results than the approaches available from the literature. As a consequence, by applying this approach to select a representative set of days, the accuracy of ESOMs/GEPMs can be improved without increasing the computational cost. The main disadvantage is that the approach is computationally costly and requires an implementation effort.