Abstract
Flexible loads have great potential to improve the electric grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. This 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 environmental 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.
Original language | American English |
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Pages | 2363-2369 |
Number of pages | 7 |
DOIs | |
State | Published - Jul 2020 |
Event | 2020 American Control Conference, ACC 2020 - Denver, United States Duration: 1 Jul 2020 → 3 Jul 2020 |
Conference
Conference | 2020 American Control Conference, ACC 2020 |
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Country/Territory | United States |
City | Denver |
Period | 1/07/20 → 3/07/20 |
Bibliographical note
See NREL/CP-5D00-76334 for preprintNREL Publication Number
- NREL/CP-5D00-77796
Keywords
- aggregates
- autoregressive processes
- data models
- load modeling
- power system stability
- quality of service
- temperature control