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Error-Level-Controlled Synthetic Forecasts for Renewable Generation
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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Xiangyu Zhang",
"@type": "vcard:Contact",
"hasEmail": "mailto:xiangyu.zhang@nrel.gov"
}
|
| dataQuality |
true
|
| description | 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. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "A Grid Resilience Paper Using This Dataset",
"format": "3209919",
"accessURL": "https://doi.org/10.1109/TPWRS.2022.3209919",
"mediaType": "application/octet-stream",
"description": "A link to our IEEE Transactions on Power Systems paper titled 'Curriculum-Based Reinforcement Learning for Distribution System Critical Load Restoration.' This paper compares the performance of reinforcement learning and model predictive control under imperfect renewable forecasts and provides an example of how this dataset is utilized.
The mechanism for generating this data set is explained in Section IV of this paper."
},
{
"@type": "dcat:Distribution",
"title": "A Grid Resilience Code Repo Using This Dataset",
"format": "HTML",
"accessURL": "https://github.com/NREL/rlc4clr",
"mediaType": "text/html",
"description": "A link to the GitHub Repo "RLC4CLR: Reinforcement Learning Control for Critical Load Restoration". This repo requires using this dataset."
},
{
"@type": "dcat:Distribution",
"title": "Synthetic Forecasts Dataset.zip",
"format": "zip",
"mediaType": "application/zip",
"description": "Renewable generation profiles and synthetic forecasts with five different error levels. See readme.txt inside for a detailed instruction.",
"downloadURL": "https://data.openei.org/files/5978/synthetic_forecasts_oedi.zip"
}
]
|
| DOI | 10.25984/2222585 |
| identifier | https://data.openei.org/submissions/5978 |
| issued | 2021-06-01T06:00:00Z |
| keyword |
[
"controllers",
"energy",
"energy systems integration",
"forecast",
"forecast error",
"forecasting",
"grid",
"optimal control",
"power",
"power systems operation",
"renewable forecasts",
"renewable generation",
"renewable uncertainty",
"short-term generation",
"solar power",
"stochastic optimization",
"synthetic forecast",
"uncertainty",
"wind power"
]
|
| landingPage | https://data.openei.org/submissions/5978 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2023-11-29T16:37:54Z |
| programCode |
[
"019:000",
"019:008",
"019:010"
]
|
| projectNumber |
"36292"
|
| projectTitle | Improving Distribution System Resiliency via Deep Reinforcement Learning |
| publisher |
{
"name": "National Renewable Energy Laboratory (NREL)",
"@type": "org:Organization"
}
|
| spatial |
"{"type":"Polygon","coordinates":[[[-180,-83],[180,-83],[180,83],[-180,83],[-180,-83]]]}"
|
| title | Error-Level-Controlled Synthetic Forecasts for Renewable Generation |
