Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report
- Univ. of California, San Diego, CA (United States
In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior in real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.
- Research Organization:
- Univ. of California, San Diego, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- EE0006330
- OSTI ID:
- 1245475
- Report Number(s):
- Final Report for EE0006330 SUNRISE
- Resource Relation:
- Related Information: Zagouras, A., Inman, R. H., & Coimbra, C. F. M. (2014a). On the determination of coherent solar microclimates for utility planning and operations. Solar Energy,102, 173-188.Zagouras, A., Pedro, H. T. C., & Coimbra, C. F. M. (2014b). Clustering the solar resource for grid management in island mode. Solar Energy, 110, 507-518.Inman, R. H., Pedro, H. T. C., & Coimbra, C. F. M. (2013). Solar forecasting methods for renewable energy integration. Progress in energy and combustion science, 39(6), 535-576.H.T.C. Pedro and C.F.M. Coimbra (2015) “Nearest-Neighbor Methodology for Prediction of Intra-Hour Global Horizontal and Direct Normal Irradiances,” Renewable Energy (80) pp. 770-782.A. Zagouras, A. Kolovos and C.F.M. Coimbra (2015) “Objective Framework for Optimal Distribution of Solar Irradiance Monitoring Networks,” Renewable Energy (80) pp. 153-165.A. Zagouras, H.T.C. Pedro and C.F.M. Coimbra (2015) “On the Role of Lagged Exogenous Variables and Spatio-Temporal Correlations in Improving the Accuracy of Solar Forecasting Methods,” Renewable Energy (78) pp. 203-218.
- Country of Publication:
- United States
- Language:
- English
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