Machine learning-based PV reserve determination strategy for frequency control on the WECC system / Haoyu Yuan, Jin Tan, Yingchen Zhang, Samanvitha Murthy, Shutang You, Hongyu Li, Yu Su, and Yilu Liu.

Format:

Book

Government document

Author/Creator:

Yuan, Haoyu (Harry), author.

Contributor:

National Renewable Energy Laboratory (U.S.), issuing body.

Series:

NREL/PO ; 5D00-76048.

NREL/PO ; 5D00-76048

Language:

English

Subjects (All):

Photovoltaic power generation--Computer simulation.

Photovoltaic power generation.

Electric power systems--Control.

Electric power systems.

Machine learning.

Physical Description:

1 online resource (1 page) : color illustrations.

Place of Publication:

Golden, CO : National Renewable Energy Laboratory, 2020.

Summary:

This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal photovoltaic (PV) power plants reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. On a realistic 1-day operation profile of the WECC system, the ML model demonstrates a savings of more than 40% PV headroom compared to a conservative approach.

Notes:

Presented at the Innovative Smart Grid Technologies (ISGT 2020) North America, 17-20 February 2020, Washington, D.C.

"February 18, 2020."

Description based on online resource; title from PDF title page (NREL, viewed on Oct. 14, 2020).

OCLC:

1200350058

Publisher Number:

0000-0002-0599-7730 orcid

0000-0002-5559-0971 orcid

1606133 OSTI ID

Access Restriction:

Publicly released