Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System

Haoyu Yuan, Jin Tan, Yingchen Zhang, Shutang You, Hongyu Li, Yu Su, Yilu Liu, Samanvitha Murthy

Research output: NRELPoster

Abstract

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.
Original languageAmerican English
StatePublished - 2020

Publication series

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

NREL Publication Number

  • NREL/PO-5D00-76048

Keywords

  • frequency control
  • machine learning
  • photovoltaics
  • PV
  • WECC
  • Western Electricity Coordinating Council

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