Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint
Frequency control from Photovoltaic (PV) plants has great potential to address the frequency response challenge of the power system with high renewable penetration. However, using model-based approaches to determine the optimal PV headroom reserve requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV 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. Furthermore, the proposed reserve determination strategy is applied on a realistic one-day operation profile of the WECC system and demonstrates over 40% PV headroom saving compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.
- 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:
- 1669372
- Report Number(s):
- NREL/CP-5D00-74829; MainId:14322; UUID:3609ec24-26d3-e911-9c26-ac162d87dfe5; MainAdminID:2802
- Country of Publication:
- United States
- Language:
- English
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Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System
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