Characterizing capacity of flexible loads for providing grid support

Coffman, Austin R.; Zhang, Guo; Barooah, Prabir

doi:10.48550/arxiv.2005.01591

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The constraints ( 3)-( 6) model various classes of flexible loads, e.g. batteries, HVAC systems in commercial buildings, thermostatically controlled loads (TCLs) [21], and pool pumps [9]. However, while the QoS set specifies maximum limits it does not mean that it is desirable to operate at these limits.…”

Section: B Ba's Needs: Reference Tracking (Global Goal)mentioning

confidence: 99%

Predictive resource allocation for flexible loads with local QoS

Coffman,

Hale,

Barooah

2020

Preprint

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Loads that can vary their power consumption without violating their Quality of service (QoS), that is flexible loads, are an invaluable resource for grid operators. Utilizing flexible loads as a resource requires the grid operator to incorporate them into a resource allocation problem. Since flexible loads are often consumers, for concerns of privacy it is desirable for this problem to have a distributed implementation. Technically, this distributed implementation manifests itself as a time varying convex optimization problem constrained by the QoS of each load. In the literature, a time invariant form of this problem without all of the necessary QoS metrics for the flexible loads is often considered. Moving to a more realistic setup introduces additional technical challenges, due to the problems' time-varying nature. In this work, we develop an algorithm to account for the challenges introduced when considering a time varying setup with appropriate QoS metrics.

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Section: B Ba's Needs: Reference Tracking (Global Goal)mentioning

confidence: 99%

Predictive resource allocation for flexible loads with local QoS

Coffman,

Hale,

Barooah

2020

Preprint

Self Cite

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“…In our prior work [22], we had developed a methodology that characterizes the capacity of a flexible load in the frequency domain. We briefly discuss this prior work here.…”

Section: B Mathematical Preliminariesmentioning

confidence: 99%

A model-free method for learning flexibility capacity of loads providing grid support

Coffman¹,

Barooah²

2020

Preprint

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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 case of an air conditioning system. However, in many cases the model parameters are not known or 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 linear time invariant (LTI) dynamics of loads. The method proposed here is applicable to nonlinear dynamics. Numerical evaluation of the method is provided, including a comparison with the model-based solution for the LTI case.

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Characterizing capacity of flexible loads for providing grid support

Cited by 2 publications

References 18 publications

Predictive resource allocation for flexible loads with local QoS

Predictive resource allocation for flexible loads with local QoS

A model-free method for learning flexibility capacity of loads providing grid support

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