[Federal Register Volume 85, Number 23 (Tuesday, February 4, 2020)]
[Notices]
[Pages 6148-6149]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2020-02123]


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DEPARTMENT OF ENERGY


Notice of Request for Information (RFI) on Prediction of Solar 
Variability for Better Grid Integration

AGENCY: Office of Energy Efficiency and Renewable Energy, Department of 
Energy (DOE).

ACTION: Request for information (RFI).

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SUMMARY: The U.S. Department of Energy (DOE) Solar Energy Technologies 
Office (SETO) is issuing this request for information (RFI) to solicit 
feedback from industry, academia, research laboratories, government 
agencies, and other stakeholders. This RFI will inform SETO's strategic 
planning on research related to the integration of solar energy 
resources. Specifically, this RFI will inform SETO's strategies 
relating to prediction of solar irradiance reaching the surface of the 
earth, and power output from solar generation plants, using either 
photovoltaic (PV) or concentrating solar power (CSP) technologies. 
Improving solar generation prediction will better inform grid operators 
as they consider the impacts of solar power variability on grid 
planning and operations technologies, as well as the owners and 
operators of utility-scale plants and aggregators of distributed PV 
systems.

DATES: SETO will accept response to the RFI for at least 30 days after 
February 4, 2020, the date this notice is published.

ADDRESSES: Interested parties are to submit comments electronically to: 
[email protected]. Include Prediction of Solar Variability for 
Better Grid Integration, in the subject of the title. Only electronic 
responses will be accepted. The complete RFI document DE-FOA-0002284 is 
located at https://eere-exchange.energy.gov.

FOR FURTHER INFORMATION CONTACT: Questions may be addressed to Mr. 
Tassos Golnas at telephone (202) 287-1793 or by email 
[email protected]. Further instructions can be found in the RFI 
document posted on EERE Exchange.

SUPPLEMENTARY INFORMATION: SETO's systems integration research focuses 
on enabling effective grid operations with increasing amounts of solar 
energy and improving system resilience. Topics include dynamic PV 
inverter models and adaptive distribution protection; grid services 
from integrating solar with energy storage and other technologies; 
advanced inverter controls and sensors; and standardized 
interconnection, interoperability, and cybersecurity for PV. The goal 
is to advance the understanding and technologies needed to integrate 
increasing amounts of solar generation into electric transmission and 
distribution systems in a cost-effective, secure, resilient, and 
reliable manner. SETO's recent R&D funding includes, but is not limited 
to, the SETO FY2019 Funding Opportunity,\1\ and the Advanced Systems 
Integration for Solar Technologies (ASSIST),\2\ Solar Forecasting 2,\3\ 
and Enabling Extreme Real-Time Grid Integration of Solar

[[Page 6149]]

Energy (ENERGISE) \4\ funding opportunities.
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    \1\ https://www.energy.gov/eere/solar/funding-opportunity-announcement-solar-energy-technologies-office-fiscal-year-2019.
    \2\ https://www.energy.gov/eere/solar/funding-opportunity-announcement-advanced-systems-integration-solar-technologies-assist.
    \3\ https://www.energy.gov/eere/solar/funding-opportunity-announcement-solar-forecasting-2.
    \4\ https://www.energy.gov/eere/solar/funding-opportunity-announcement-enabling-extreme-real-time-grid-integration-solar-energy.
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    SETO has supported solar prediction technologies in its Solar 
Forecasting funding program, launched in 2013, which delivered WRF-
Solar \5\--a version of the Weather Research and Forecasting (WRF) 
model \6\ that is optimized for solar irradiance, and more recently in 
the Solar Forecasting 2 funding program, launched in 2018. This latter 
program prioritizes improvements in the prediction of solar irradiance 
for horizons between 3 and 48 hours ahead, the successful integration 
of probabilistic solar power forecasts with generation unit scheduling, 
and the creation of an open-source framework for the efficient and 
transparent evaluation of irradiance and power forecast models.
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    \5\ https://ral.ucar.edu/projects/wrf-solar.
    \6\ https://www.mmm.ucar.edu/weather-research-and-forecasting-model.
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    SETO hosted a workshop on October 7-8, 2019, in Washington, DC to 
review the progress of projects awarded under the Solar Forecasting 2 
funding program and to better understand the remaining challenges 
associated with the variability and prediction uncertainty of solar 
generation. At the event, subject matter experts and SETO-funded 
researchers presented on the state-of-the-art of solar irradiance 
forecasting, opportunities for the integration of hybrid systems with 
solar plants in the bulk power system, and efforts associated with the 
DOE-funded projects. These efforts work to improve the WRF-Solar model, 
use machine learning and other artificial intelligence methods to 
better predict irradiance under variable cloud cover and during ramps, 
and calculate the optimal amount of generation reserves using 
probabilistic solar power forecasts. An extended session was dedicated 
to the demonstration of the current state of Solar Forecast Arbiter,\7\ 
which is an open-source platform designed to facilitate objective, 
transparent, and auditable evaluation of irradiance and power 
forecasts. The participants openly discussed emerging challenges 
regarding the prediction of solar irradiance and power in a world with 
increasing solar and renewable penetration, and an increasing 
population of behind-the-meter variable loads. The detailed workshop 
agenda and presentations are available on the SETO website.\8\
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    \7\ https://SolarForecastArbiter.org.
    \8\ https://www.energy.gov/eere/solar/downloads/solar-forecasting-2-workshop.
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    In this RFI, SETO is seeking additional feedback on these topics 
from industry, electric utilities, balancing authorities, academia, 
research laboratories, government agencies, and other stakeholders. The 
main goal is to lower the integration cost of high penetrations of 
solar power to the bulk power and distribution systems by making the 
prediction of solar generation more accurate and effective. Such a 
development could be realized by leveraging advances in ground and 
remote sensing, numerical modeling of atmospheric processes, artificial 
intelligence techniques, and stochastic optimization. The questions are 
given as follows and responders are welcome to answer all or any subset 
of the questions.

Confidential Business Information

    Pursuant to 10 CFR 1004.11, any person submitting information that 
he or she believes to be confidential and exempt by law from public 
disclosure should submit via email two well marked copies: One copy of 
the document marked ``confidential'' including all the information 
believed to be confidential, and one copy of the document marked ``non-
confidential'' with the information believed to be confidential 
deleted. DOE will make its own determination about the confidential 
status of the information and treat it according to its determination.

    Signed in Washington, DC, on January 27, 2020.
Rebecca Jones-Albertus,
Director, Solar Energy Technologies Office.
[FR Doc. 2020-02123 Filed 2-3-20; 8:45 am]
 BILLING CODE 6450-01-P