ARPA-E PERFORM datasets

Sergi, Brian; Feng, Cong; Zhang, Flora; Hodge, Bri-Mathias; Ring-Jarvi, Ross; Bryce, Richard; Doubleday, Kate; Rose, Megan; Buster, Grant; Rossol, Michael

doi:10.25984/1891136

Title: ARPA-E PERFORM datasets

Dataset
Other Related Research

Abstract

Time-coincident load, wind, and solar data including actual and probabilistic forecast datasets at 5-min resolution for ERCOT, MISO, NYISO, and SPP. Wind and solar profiles are supplied for existing sites as well as planned sites based on interconnection queue projects as of 2021. For ERCOT actuals are provided for 2017 and 2018 and forecasts for 2018, and for the remaining ISOs actuals are provided for 2018 and 2019 and forecasts for 2019. There datasets were produced by NREL as part of the ARPA-E PERFORM project, an ARPA-E funded program that aim to use time-coincident power and load seeks to develop innovative management systems that represent the relative delivery risk of each asset and balance the collective risk of all assets across the grid. For more information on the datasets and methods used to generate them see https://github.com/PERFORM-Forecasts/documentation.

Authors:

; Feng, Cong; Zhang, Flora;

; Ring-Jarvi, Ross; Bryce, Richard; Doubleday, Kate; Rose, Megan; Buster, Grant; Rossol, Michael

National Renewable Energy Laboratory (NREL)

Publication Date:: Thu Aug 18 00:00:00 EDT 2022

Other Number(s):: 5772

Research Org.:: DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory (NREL)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Multiple Programs (EE)

Collaborations:: National Renewable Energy Laboratory (NREL)

Subject:: 5-min data; ARPA-E; Array; ERCOT; MISO; NYISO; PERFORM; SPP; actual; balance; data; delivery; forecast; generation; grid; load; power; power systems; probabilistic forecast; risk; solar; time-coincident; wind

OSTI Identifier:: 1891136

DOI:: https://doi.org/10.25984/1891136

Citation Formats


                    Sergi, Brian, Feng, Cong, Zhang, Flora, Hodge, Bri-Mathias, Ring-Jarvi, Ross, Bryce, Richard, Doubleday, Kate, Rose, Megan, Buster, Grant, and Rossol, Michael. ARPA-E PERFORM datasets.  United States: N. p., 2022. 
        Web.  doi:10.25984/1891136.

Copy to clipboard


                    Sergi, Brian, Feng, Cong, Zhang, Flora, Hodge, Bri-Mathias, Ring-Jarvi, Ross, Bryce, Richard, Doubleday, Kate, Rose, Megan, Buster, Grant, & Rossol, Michael. ARPA-E PERFORM datasets.  United States.  doi:https://doi.org/10.25984/1891136

Copy to clipboard


                    Sergi, Brian, Feng, Cong, Zhang, Flora, Hodge, Bri-Mathias, Ring-Jarvi, Ross, Bryce, Richard, Doubleday, Kate, Rose, Megan, Buster, Grant, and Rossol, Michael. 2022.  
"ARPA-E PERFORM datasets".  United States.  doi:https://doi.org/10.25984/1891136.  https://www.osti.gov/servlets/purl/1891136. Pub date:Thu Aug 18 00:00:00 EDT 2022

Copy to clipboard


                    
@article{osti_1891136,

  title        = {ARPA-E PERFORM datasets},

  author       = {Sergi, Brian and Feng, Cong and Zhang, Flora and Hodge, Bri-Mathias and Ring-Jarvi, Ross and Bryce, Richard and Doubleday, Kate and Rose, Megan and Buster, Grant and Rossol, Michael},

  abstractNote = {Time-coincident load, wind, and solar data including actual and probabilistic forecast datasets at 5-min resolution for ERCOT, MISO, NYISO, and SPP. Wind and solar profiles are supplied for existing sites as well as planned sites based on interconnection queue projects as of 2021. For ERCOT actuals are provided for 2017 and 2018 and forecasts for 2018, and for the remaining ISOs actuals are provided for 2018 and 2019 and forecasts for 2019. There datasets were produced by NREL as part of the ARPA-E PERFORM project, an ARPA-E funded program that aim to use time-coincident power and load seeks to develop innovative management systems that represent the relative delivery risk of each asset and balance the collective risk of all assets across the grid. For more information on the datasets and methods used to generate them see https://github.com/PERFORM-Forecasts/documentation.},

  doi          = {10.25984/1891136},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Aug 18 00:00:00 EDT 2022},

  month        = {Thu Aug 18 00:00:00 EDT 2022}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.25984/1891136

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

NREL Price Series Developed for the ARPA-E FLECCS Program

Dataset Cohen, Stuart ; Durvasulu, Venkat

The price data for four regions (CAISO, ERCOT, MISO-W, and PJM-W) are developed using the ReEDS to PLEXOS conversion as described in (Gagnon et al. 2020). The reference ReEDS case chosen is based on the 2020 Standard Scenario Mid-case, which uses the 2020 ReEDS model version (Cole et al. 2020; Ho et al. 2021). All ReEDS model inputs use 2020 Standard Scenarios Mid-case assumptions except for CO2 prices, which are implemented as linearly increasing CO2 price trajectories beginning at $0/tCO2 in 2020 and ending at either $100/tCO2 or $150/tCO2 in 2035 to dive capacity expansion towards a low-carbon system thatmore »« less
ARPA-E Grid Optimization (GO) Competition Challenge 2

Dataset Elbert, Stephen ; Holzer, Jesse ; Veeramany, Arun ; ...

The ARPA-E Grid Optimization (GO) Competition Challenge 2, from 2020 to 2021, expanded upon the problem posed in Challenge 1 by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment. Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. Specifically, the economic surplus, defined as the benefit of serving load minus the cost of generation, is being maximized. It was expected that the objective value of a given solution should be positive, representing economic gain, but negative objectives from poor solutions weremore »« less
ARPA-E Grid Optimization (GO) Competition Challenge 1

Dataset Elbert, Stephen ; Holzer, Jesse ; Veeramany, Arun ; ...

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases butmore »« less
ARPA-E Grid Optimization (GO) Competition Challenge 3

Dataset Elbert, Stephen ; Holzer, Jesse ; Veeramany, Arun ; ...

Synthetic Input Data and Team Results for the GO Competition Challenge 3 for Events 1 - 4 and the Sandbox, along with problem and format descriptions and code to validate data and solutions, are available here. Data for industry scenarios will not be made public. The Grid Optimization (GO) Competition Challenge 3 focused on the security-constrained optimal power flow (SCOPF) problem. It is part of a continuing effort begun with Challenges 1 and 2, to successfully discover, develop, and test innovative and disruptive software solutions for critical energy challenges and to overcome existing barriers. The broader goal of the ofmore »« less
Dataset for manuscript "Rotational Memory Function of SPC/E water"

Dataset Asthagiri, Dilipkumar ; Matyushov, Dmitry V.

Memory effect are essential for dynamics of condensed materials and are responsible for non-exponential relaxation of correlation functions of dynamic variables through the memory function entering the memory equation. Memory functions of dipole rotations for polar liquids have never been calculated. We present here calculations of memory functions and single-dipole rotations and of the overall system dipole moment for SPC/E water measured by dielectric spectroscopy. The memory functions for single-particle and collective dynamics turn out to be nearly identical. This result validates theories of dielectric spectroscopy in terms of single-particle time correlation function and the connection between the collective andmore »« less

Similar Records