Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 33, Issue 3, Pages 3029-3039Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2017.2746379
Keywords
Dynamic time warping; hierarchical clustering; k-means clustering; power system planning; representative days
Categories
Funding
- National Science Foundation [1029337, 1548015]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1548015] Funding Source: National Science Foundation
Ask authors/readers for more resources
Power system capacity-expansion models are typically intractable if every operating period is represented. This issue is normally overcome by using a subset of representative operating periods. For instance, representative operating hours can be selected by discretizing the load-duration curve, which captures the effect of load levels on system-operation costs. This approach is inappropriate if system-operating costs depend on parameters other than load (e.g., renewable-resource availability) or if there are important intertemporal operating constraints (e.g., generator-ramping limits). This paper proposes the use of representative operating days, which are selected using clustering, to surmount these issues. We propose two hierarchical clustering techniques, which are designed to capture the important statistical features of the parameters (e.g., load and renewable-resource availability), in selecting representative days. This includes temporal autocorrelations and correlations between different locations. A case study, which is based on the Texan power system, is used to demonstrate the techniques. We show that our proposed clustering techniques result in investment decisions that closely match those made using the full unclustered dataset.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available