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DOE OSTI · 1669450

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services: Preprint

Chen, Yue·Bernstein, Andrey
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Abstract

Flexible loads have great potential to improve the power grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. The paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environment impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

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controldataelectric gridflexible loadslinear parameter-varying modelingPOWER TRANSMISSION AND DISTRIBUTION

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Chen, Yue, Bernstein, Andrey. 2020-07-14. Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services: Preprint. https://www.osti.gov/biblio/1669450

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