@misc{60899a2b08dc45dc9c506c2f7651c8d4,
title = "A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services",
abstract = "Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.",
keywords = "CHIL, closed-loop, controller-hardware-in-the-loop, grid services, hardware-in-the-loop, HIL, machine learning, ML, PHL, potential high limit, PV, real-time",
author = "Emanuel Mendiola and Mengmeng Cai and Weihang Yan and Jing Wang and Simon Julien and Tristan Liu and Zachary Jacobs and Vahan Gevorgian",
year = "2024",
language = "American English",
series = "Presented at the 52nd IEEE Photovoltaic Specialists Conference (PVSC52), 9-14 June 2024, Seattle, Washington",
publisher = "National Renewable Energy Laboratory (NREL)",
address = "United States",
type = "Other",
}