Estimation of Behind-the-Meter Solar Generation by Integrating Physical with Statistical Models
- University of California, Riverside
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Accurate estimation of solar photovoltaic (PV) generation is crucial for distribution grid control and optimization. Unfortunately, most of the residential solar PV installations are behind-the-meter. Thus, utilities only have access to the net load readings. This paper presents an unsupervised framework for estimating solar PV generation by disaggregating the net load readings. The proposed framework synergistically combines a physical PV system performance model with a statistical model for load estimation. Specifically, our algorithm iteratively estimates solar PV generation with a physical model and electric load with the Hidden Markov model regression. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is validated against net load and solar PV generation data gathered from residential customers located in Austin, Texas. The validation results show that our method reduces mean squared error by 44% compared to the state-of-the-art disaggregation algorithm.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Grid Modernization Laboratory Consortium; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W); USDOE Office of Electricity Delivery and Energy Reliability (OE)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1592382
- Report Number(s):
- NREL/CP-5D00-74647
- Country of Publication:
- United States
- Language:
- English
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