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, Yilu Liu

Research output: Contribution to conferencePaperpeer-review

7 Scopus Citations

Abstract

Frequency control from photovoltaic (PV) power plants has great potential to address the frequency response challenge of the power system with high penetrations of renewable generation. Using model-based approaches to determine the optimal PV headroom reserve, however, requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV 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. Furthermore, the proposed reserve determination strategy is applied on a realistic 1-day operation profile of the WECC system and demonstrates a savings of more than 40% PV headroom compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - Feb 2020
Event2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 - Washington, United States
Duration: 17 Feb 202020 Feb 2020

Conference

Conference2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020
Country/TerritoryUnited States
CityWashington
Period17/02/2020/02/20

Bibliographical note

See NREL/CP-5D00-74829 for preprint

NREL Publication Number

  • NREL/CP-5D00-77369

Keywords

  • Frequency control
  • Machine learning
  • Neural network
  • Photovoltaic (PV)
  • Renewable energy
  • Reserve
  • WECC

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