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We propose a control architecture for distributed coordination of a collection of on/off TCLs (thermostatically con- trolled loads), such as residential air conditioners, to provide the same service to the power grid as a large battery. A key constraint is to ensure that consumers’ quality of service (QoS) is maintained. Our proposal involves replac- ing the thermostats at the loads by a randomized controller, following recent proposals in this direction. The new local controller has a tunable parameter that serves as the control command from the balancing authority (BA). Com- pared to prior work in this area, our proposed architecture can handle large disturbances from the outside temperature. Weather-induced disturbance also imposes an algorithm-independent limit on the capacity of the virtual energy storage the loads can provide. This key limitation, which was ignored in prior work, is incorporated in our formulation in a principled manner.
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A model-free method for learning flexibility capacity of loads providing grid support
Flexible loads are a resource for the Balancing Authority (BA) of the future to aid in the balance of power supply and demand. In order to be used as a resource, the BA must know the capacity of the flexible loads to vary their power demand over a baseline without violating consumers' quality of service (QoS). Existing work on capacity characterization is model-based: They need models relating power consumption to variables that dictate QoS, such as temperature in the case of an air conditioning system. However, in many cases the model parameters are not known or are difficult to obtain. In this work, we pose a data driven capacity characterization method that does not require model information, it only needs access to a simulator. The capacity is characterized as the set of feasible spectral densities (SDs) of the demand deviation. The proposed method is an extension of our recent work on SD-based capacity characterization that was limited to the case where the loads dynamic model is completely known. Numerical evaluation of the method is provided, which compares our approach to the model-based solution of our past work.
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- PAR ID:
- 10281468
- Date Published:
- Journal Name:
- American Control Conference
- Page Range / eLocation ID:
- 2881 to 2886
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC50511.2021.9483207
