Capacity of flexible loads for grid support: statistical characterization for long term planning

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

doi:10.23919/acc45564.2020.9147252

Cited by 5 publications

(9 citation statements)

References 17 publications

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“…That is, instead of relying on model knowledge it rather relies on access to a simulator to determine the capacity. This framework builds off of our past work [20], where we characterize the capacity of flexible load(s) as constraints on the SD of their power deviation. In the past work, to obtain a SD we set up an optimization problem: the BA projects its needs (roughly, the SD of net demand, e.g., shown in Figure 1) onto the constraint set of feasible of SD's.…”

Section: Data?mentioning

confidence: 99%

“…In the past work, to obtain a SD we set up an optimization problem: the BA projects its needs (roughly, the SD of net demand, e.g., shown in Figure 1) onto the constraint set of feasible of SD's. In [20], the models relating demand deviation to QoS variables were assumed LTI, and it was shown how to solve the optimization problem for that case.…”

Section: Data?mentioning

confidence: 99%

“…The key insight that allows for this model free construction is the choice of approximation architecture when discretizing the infinite dimensional optimization problem. This data driven framework also has the ability to accommodate non-linear flexible load models, whereas our past work [20] was for linear models only.…”

Section: Data?mentioning

confidence: 99%

“…In addition to the previous references, there is an emerging methodology of characterizing the capacity as constraints on the statistics of the reference signal [18]- [20], rather than the reference signal itself. Most commonly, as constraints on the spectral density (SD) of the reference signal [19], [20]. Elaborating, these methods aim to precisely quantify the regions shown in Figure 1 based on the QoS of the loads considered.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Data?mentioning

confidence: 99%

Section: Data?mentioning

confidence: 99%

Section: Data?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Coffman¹,

Barooah²

2020

Preprint

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“…Virtual battery models have therefore been developed that can accurately capture the essential dynamics of the fleet [3]- [9]. Flexibility is then defined as a set of constraints on SoC, aggregate power consumption and other related quantities in time domain [6] and frequency domain [7]. The authors in [3] developed a time-varying virtual battery model for energy arbitrage.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Quantifying Flexibility in a fleet of Diverse DERs

Khurram¹,

Espinosa²,

Almassalkhi³

2020

Preprint

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This paper addresses the question: how many distributed energy resources (DERs) are needed to provide ±1MW of flexibility over a number of hours? For this purpose, a metric based on an ISO's own performance score is proposed. Then, a systematic procedure is presented and validated that makes use of either a simulator or the solution to an optimization problem based on a nominal analytical formulation to get flexibility in terms of kW-per-device. Furthermore, simulation-based analysis indicates that flexibility from different DER fleets adds linearly, that is, the total flexibility provided by a mixture of different DER types can be obtained as a convex combination of their individual kW-per-device flexibility. The proposed methodology is validated on (i) a centralized coordinator and (ii) a device driven DER coordination scheme called packetized energy management (PEM). Furthermore, the effect of heterogeneity as well as PEM specific parameters such as packet length and mean time-torequest on flexibility is also quantified.

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