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    • Error-Level-Controlled Synthetic Forecasts for Renewable Generation

    Metadata Updated: November 30, 2023

    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.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: Creative Commons Attribution

    Downloads & Resources

    Dates

    Metadata Created Date November 30, 2023
    Metadata Updated Date November 30, 2023

    Metadata Source

    Harvested from OpenEI data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date November 30, 2023
    Metadata Updated Date November 30, 2023
    Publisher National Renewable Energy Laboratory (NREL)
    Maintainer
    Doi 10.25984/2222585
    Identifier https://data.openei.org/submissions/5978
    Data First Published 2021-06-01T06:00:00Z
    Data Last Modified 2023-11-29T16:37:54Z
    Public Access Level public
    Bureau Code 019:20
    Metadata Context https://openei.org/data.json
    Metadata Catalog ID https://openei.org/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Data Quality True
    Harvest Object Id c873e5ab-11ee-4697-b9f6-71ea0dd7ba95
    Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
    Harvest Source Title OpenEI data.json
    Homepage URL https://data.openei.org/submissions/5978
    License https://creativecommons.org/licenses/by/4.0/
    Old Spatial {"type":"Polygon","coordinates":-180,-83,180,-83,180,83,-180,83,-180,-83}
    Program Code 019:000, 019:008, 019:010
    Projectnumber 36292
    Projecttitle Improving Distribution System Resiliency via Deep Reinforcement Learning
    Source Datajson Identifier True
    Source Hash 0eae13262b69c5b3973c75d0ad8360cca241a4d13b0bc18e978f6b7c8e78d7d6
    Source Schema Version 1.1
    Spatial {"type":"Polygon","coordinates":-180,-83,180,-83,180,83,-180,83,-180,-83}

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