Abstract
This work presents methods for evaluating the Power Transfer Distribution Factor (PTDF) and Line Outage Distribution Factor (LODF) matrices by employing sparse linear algebra for large-scale computing applications. These matrices play a critical role in many power system applications, such as the Unit Commitment Problem (UC), pre- and post-contingency power flow analysis, and transmission expansion. These matrices are typically dense, which means they require a significant amount of time and memory to be computed for large networks. However, by analyzing the structure of the matrices and their computation method, it is possible to use reduced memory methods based on sparse matrix operations. This paper shows that sparse linear algebra algorithms are faster and require less memory and time than traditional dense approaches. Additionally, we explore the effect of matrix sparsification by eliminating trailing digits on power flow calculations.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - 2024 |
Event | 2024 IEEE Power & Energy Society General Meeting - Seattle, Washington Duration: 21 Jul 2024 → 25 Jul 2024 |
Conference
Conference | 2024 IEEE Power & Energy Society General Meeting |
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City | Seattle, Washington |
Period | 21/07/24 → 25/07/24 |
Bibliographical note
See NREL/CP-6A40-88141 for preprintNREL Publication Number
- NREL/CP-6A40-91907
Keywords
- large scale
- power flow
- sparse linear algebra