Identification of Virtual Battery Models for Flexible Loads

Hughes, Justin T.; Domínguez-García, Alejandro D.; Poolla, Kameshwar

doi:10.1109/tpwrs.2015.2505645

Cited by 114 publications

(62 citation statements)

References 16 publications

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“…We provide a single model-a generalization of the virtual battery model [17]-which can describe the behavior of any of these resources.…”

Section: Der Power Delivery Modelmentioning

confidence: 99%

“…We utilize "Normalized Dynamic and Traditional Regulation Signals" from PJM for the period January [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]2013. This data is available at [23], and includes two regulation signals-RegD, a fast response signal, and RegA, a filtered version of RegD for slower ramping generators; both signals are updated every 2 s. Figure 1 shows a representative segment of the aforementioned data.…”

Section: Datasetmentioning

confidence: 99%

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Coordinating Heterogeneous Distributed Energy Resources for Provision of Frequency Regulation Services

Hughes

Domínguez-García

Poolla

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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We discuss a framework for coordinating the response of distributed energy resources (DERs) connected to electric power distribution networks to provide frequency regulation services. These resources include plug-in electric vehicles, thermostatically controlled loads, and microturbines. In this framework, we consider an aggregator that participates in the real-time market by submitting an offer to provide frequency regulation services. If the offer is accepted, the aggregator needs to coordinate the response of a set of DERs. The DERs are compensated through bilateral contracts, the terms of which are negotiated in advance. The DER coordination problem the aggregator is faced with is cast as an optimal control problem, and we propose a bilayer framework to obtain a sub-optimal solution. In the first layer, we utilize model-predictive control techniques driven by regulation signal forecasts and parameter estimates to obtain a reference control signal for the DERs. A second control layer provides closed-loop regulation around the reference computed by the top layer, which minimizes the error that arises due to forecast error, plant-model mismatch, and the slower speed of the optimal control.

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“…We provide a single model-a generalization of the virtual battery model [17]-which can describe the behavior of any of these resources.…”

Section: Der Power Delivery Modelmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Coordinating Heterogeneous Distributed Energy Resources for Provision of Frequency Regulation Services

Hughes

Domínguez-García

Poolla

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

Self Cite

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“…Further, authors in [10] have extended the geometric method by incorporating the uncertainties into characterizing the stochastic aggregate flexibility of the ensemble. Another way of characterizing the aggregate flexibility of the ensemble is by representing it with an equivalent possibly a first-order linear system, VB [4,11,12]. The state of charge (soc) of the VB denotes the aggregate flexibility of the ensemble and is captured simply in terms of battery parameters such as power limits, initial soc, self dissipation rate and capacity.…”

Section: Introductionmentioning

confidence: 99%

“…There exists several characterizations of aggregate flexibility using VB models in the literature [4,[11][12][13]. These works include characterizing a VB model for a wide range of systems from small residential TCLs to complex systems such as commercial building heating, ventilating, and air-conditioning (HVAC) loads.…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of Virtual Battery Model for Heterogeneous Devices

Nandanoori

Chakraborty

Ramachandran

et al. 2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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The potential of distributed energy resources in providing grid services can be maximized with the recent advancements in demand side control. Effective utilization of this control strategy requires the knowledge of aggregate flexibility of the distributed energy resources (DERs). Recent works have shown that the aggregate flexibility of DERs can be modeled as a virtual battery (VB) whose state evolution is governed by a first order system including self dissipation. The VB parameters (self dissipation rate, energy capacity) are obtained by solving an optimization problem which minimizes the tracking performance of the ensemble and the proposed first order model. For the identified first order model, time varying power limits are calculated using binary search algorithms. Finally, this proposed framework is demonstrated for different homogeneous and heterogeneous ensembles consisting of air conditioners (ACs) and electric water heaters (EWHs).

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“…In [27], the authors utilized the building's thermostat setpoint to control the fan speed and adjust the power consumption. Reference [28] formulated a virtual battery model for commercial buildings. In our work, we use distributed OCO for real-time DR of large aggregations of commercial buildings.…”

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confidence: 99%

Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings

Lesage‐Landry

Callaway

2020

IEEE Control Syst. Lett.

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We extend the regret analysis of the online distributed weighted dual averaging (DWDA) algorithm [1] to the dynamic setting and provide the tightest dynamic regret bound known to date with respect to the time horizon for a distributed online convex optimization (OCO) algorithm. Our bound is linear in the cumulative difference between consecutive optima and does not depend explicitly on the time horizon. We use dynamic-online DWDA (D-ODWDA) and formulate a performance-guaranteed distributed online demand response approach for heating, ventilation, and air-conditioning (HVAC) systems of commercial buildings. We show the performance of our approach for fast timescale demand response in numerical simulations and obtain demand response decisions that closely reproduce the centralized optimal ones.

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Identification of Virtual Battery Models for Flexible Loads

Cited by 114 publications

References 16 publications

Coordinating Heterogeneous Distributed Energy Resources for Provision of Frequency Regulation Services

Coordinating Heterogeneous Distributed Energy Resources for Provision of Frequency Regulation Services

Identification and Validation of Virtual Battery Model for Heterogeneous Devices

Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings

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