Hardware-in-the-Loop Evaluation for Potential High Limit Estimation-Based PV Plant Active Control: Preprint

Mengmeng Cai, Simon Julien, Jing Wang, Subhankar Ganguly, Weihang Yan, Zachary Jacobs, Tristan Liu, Vahan Gevorgian

Research output: Contribution to conferencePaper


This paper validates the effectiveness of an Artificial Intelligence (AI)-driven PV plant control and optimization approach, namely, the Automated Learner for Intermittency Control by Extrapolation (ALICE), in empowering PV plant as a dependable grid reliability service provider. The validation is performed in a realistic laboratory controller-hardware-in-the-loop (CHIL) environment, leveraging accurate PV plant modeling and standard industrial communication protocol. Simulation results, considering both varying weather conditions and active control scenarios, demonstrate the superior performance of ALICE in improving the grid service delivery precision and reducing the over-curtailment compared to a state-of-the-art approach, i.e., reference-control grouping based approach. Such a work could help mitigate risks and provide practical guidance during the field deployment of ALICE, while establishing a standardized testing framework for evaluating various PV active control strategies.
Original languageAmerican English
Number of pages8
StatePublished - 2024
EventIEEE PES General Meeting 2024 - Seattle, Washington
Duration: 21 Jul 202425 Jul 2024


ConferenceIEEE PES General Meeting 2024
CitySeattle, Washington

NREL Publication Number

  • NREL/CP-5D00-87998


  • hardware-in-the-loop
  • potential high limit
  • PV active control


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