A Synthetic Texas Power System with Time-Series High-Resolution Weather-Dependent Spatio-Temporally Correlated Grid Profiles
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| izdano v: | arXiv.org (Aug 16, 2024), p. n/a |
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| Drugi avtorji: | , , , , , , |
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Cornell University Library, arXiv.org
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| Online dostop: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2780573065 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2780573065 | ||
| 045 | 0 | |b d20240816 | |
| 100 | 1 | |a Lu, Jin | |
| 245 | 1 | |a A Synthetic Texas Power System with Time-Series High-Resolution Weather-Dependent Spatio-Temporally Correlated Grid Profiles | |
| 260 | |b Cornell University Library, arXiv.org |c Aug 16, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a This study introduced a synthetic power system with spatio-temporally correlated profiles of solar power, wind power, dynamic line ratings and loads at one-hour resolution for five continuous years, referred to as the Texas 123-bus backbone transmission (TX-123BT) system. Unlike conventional test cases that offer a static snapshot of system profile, the designed TX-123BT system incorporates weather-dependent profiles for renewable generation and transmission thermal limits, mimicking the actual Electric Reliability Council of Texas (ERCOT) system characteristics. Three weather-dependent models are used for the creation of wind and solar power production, and dynamic line rating (DLR) separately. Security-constrained unit commitment (SCUC) is conducted on TX-123BT daily profiles and numerical results are compared with the actual ERCOT system for validation. The long-term spatio-temporal profiles can greatly capture the renewable production versatility due to the environmental conditions. An example of hydrogen facilities integration studies is presented to illustrate the advantage of utilizing detailed spatio-temporal profiles of TX-123BT. | |
| 610 | 4 | |a Electric Reliability Council of Texas | |
| 651 | 4 | |a Texas | |
| 651 | 4 | |a United States--US | |
| 653 | |a Solar energy | ||
| 653 | |a Unit commitment | ||
| 653 | |a Clustering | ||
| 653 | |a Network reliability | ||
| 653 | |a Transmission lines | ||
| 653 | |a Climate models | ||
| 653 | |a Computer networks | ||
| 653 | |a Electric power systems | ||
| 700 | 1 | |a Li, Xingpeng | |
| 700 | 1 | |a Li, Hongyi | |
| 700 | 1 | |a Chegini, Taher | |
| 700 | 1 | |a Gamarra, Carlos | |
| 700 | 1 | |a Yang, Y C Ethan | |
| 700 | 1 | |a Cook, Margaret | |
| 700 | 1 | |a Dillingham, Gavin | |
| 773 | 0 | |t arXiv.org |g (Aug 16, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2780573065/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2302.13231 |