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Error-Level-Controlled Synthetic Forecasts for Renewable Generation

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Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.

Citation Formats

TY - DATA AB - Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions. AU - Zhang, Xiangyu A2 - Knueven, Bernard A3 - Eseye, Abinet Tesfaye A4 - Reynolds, Matthew A5 - Jones, Wesley A6 - Liu, Weijia DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.25984/2222585 KW - energy KW - power KW - renewable forecasts KW - forecast error KW - stochastic optimization KW - optimal control KW - uncertainty KW - forecast KW - forecasting KW - power systems operation KW - renewable generation KW - synthetic forecast KW - energy systems integration KW - wind power KW - solar power KW - grid KW - renewable uncertainty KW - controllers KW - short-term generation LA - English DA - 2021/06/01 PY - 2021 PB - National Renewable Energy Laboratory (NREL) T1 - Error-Level-Controlled Synthetic Forecasts for Renewable Generation UR - https://doi.org/10.25984/2222585 ER -
Export Citation to RIS
Zhang, Xiangyu, et al. Error-Level-Controlled Synthetic Forecasts for Renewable Generation. National Renewable Energy Laboratory (NREL), 1 June, 2021, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2222585.
Zhang, X., Knueven, B., Eseye, A., Reynolds, M., Jones, W., & Liu, W. (2021). Error-Level-Controlled Synthetic Forecasts for Renewable Generation. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory (NREL). https://doi.org/10.25984/2222585
Zhang, Xiangyu, Bernard Knueven, Abinet Tesfaye Eseye, Matthew Reynolds, Wesley Jones, and Weijia Liu. Error-Level-Controlled Synthetic Forecasts for Renewable Generation. National Renewable Energy Laboratory (NREL), June, 1, 2021. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2222585
@misc{OEDI_Dataset_5978, title = {Error-Level-Controlled Synthetic Forecasts for Renewable Generation}, author = {Zhang, Xiangyu and Knueven, Bernard and Eseye, Abinet Tesfaye and Reynolds, Matthew and Jones, Wesley and Liu, Weijia}, abstractNote = {Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.}, url = {https://data.openei.org/submissions/5978}, year = {2021}, howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory (NREL), https://doi.org/10.25984/2222585}, note = {Accessed: 2025-04-25}, doi = {10.25984/2222585} }
https://dx.doi.org/10.25984/2222585

Details

Data from Jun 1, 2021

Last updated Nov 29, 2023

Submitted Nov 29, 2023

Organization

National Renewable Energy Laboratory (NREL)

Contact

Xiangyu Zhang

303.275.4068

Authors

Xiangyu Zhang

National Renewable Energy Laboratory NREL

Bernard Knueven

National Renewable Energy Laboratory NREL

Abinet Tesfaye Eseye

National Renewable Energy Laboratory NREL

Matthew Reynolds

National Renewable Energy Laboratory NREL

Wesley Jones

National Renewable Energy Laboratory NREL

Weijia Liu

National Renewable Energy Laboratory NREL

DOE Project Details

Project Name Improving Distribution System Resiliency via Deep Reinforcement Learning

Project Number 36292

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