Adaptive PV Frequency Control Strategy Based on Real-time Inertia Estimation
Journal Article
·
· IEEE Transactions on Smart Grid
- Univ. of Tennessee, Knoxville, TN (United States)
- Univ. of Queensland, Brisbane, QLD (Australia)
- Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
The declining cost of solar Photovoltaics (PV) generation is driving its worldwide deployment. As conventional generation with large rotating masses is being replaced by renewable energy such as PV, the power system’s inertia will be affected. As a result, the system’s frequency may vary more dramatically in the case of a disturbance, and the frequency nadir may be low enough to trigger protection relays such as under-frequency load shedding. The existing frequency-watt function mandated in power inverters cannot provide grid frequency support in a loss-of-generation event, as PV plants usually do not have power reserves. Here, a novel adaptive PV frequency control strategy is proposed to reserve the minimum power required for grid frequency support. A machine learning model is trained to predict system frequency response under varying system conditions, and an adaptive allocation of PV headroom reserves is made based on the machine learning model as well as real-time system conditions including inertia. Case studies show the proposed control method meets the frequency nadir requirements using minimal power reserves compared to a fixed headroom control approach.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1764455
- Journal Information:
- IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 3 Vol. 12; ISSN 1949-3053
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System
Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System
Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint
Conference
·
Thu May 07 00:00:00 EDT 2020
·
OSTI ID:1669504
Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System
Conference
·
Fri Jan 31 23:00:00 EST 2020
·
OSTI ID:1764458
Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint
Conference
·
Tue Jul 14 00:00:00 EDT 2020
·
OSTI ID:1669372