Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System
Conference
·
OSTI ID:1606133
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- University of Tennessee, Knoxville, Tennessee
- Oak Ridge National Laboratory
This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal photovoltaic (PV) power plants reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. On a realistic 1-day operation profile of the WECC system, the ML model demonstrates a savings of more than 40% PV headroom compared to a conservative approach.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1606133
- Report Number(s):
- NREL/PO-5D00-76048
- Country of Publication:
- United States
- Language:
- English
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