Leaky storage model for optimal multi‐service allocation of thermostatic loads

Trovato, Vincenzo; Tindemans, Simon H.; Štrbac, Goran

doi:10.1049/iet-gtd.2015.0168

Cited by 61 publications

(89 citation statements)

References 14 publications

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“…Recently it has been demonstrated in [10] that the aggregate flexibility of ≫ 1 heterogeneous TCLs is summarized by the (1-4).…”

Section: Aggregate Model and Control Of Tclsmentioning

confidence: 99%

“…Equation (1) [10] for the mathematical definition of . The dynamics of the leaky-storage unit are governed by a decentralized hybrid control strategy [11] and its extensions in [10]; under the considered strategy, each appliance individually targets the desired relative power curve ( ), so that the total power consumption equals (2).…”

Section: Aggregate Model and Control Of Tclsmentioning

confidence: 99%

“…The dynamics of the leaky-storage unit are governed by a decentralized hybrid control strategy [11] and its extensions in [10]; under the considered strategy, each appliance individually targets the desired relative power curve ( ), so that the total power consumption equals (2). ̂0 [MW] is the average steady state population consumption and the superscript is used for single device parameters.…”

Section: Aggregate Model and Control Of Tclsmentioning

confidence: 99%

“…These units take action several minutes after the generator loss and replace the support from previous units and TCL, completing the frequency restoration. As reserve represents a long-term commitment, it is not suitable for the TCL storage capacity of several classes of appliances [10].…”

Section: Demand Side Response Modelmentioning

confidence: 99%

“…The TCL power levels are constrained by (2) and (4). The response services provision and the consequent payback are constrained by (7)(8)(9)(10)(11). In Sec.…”

Section: A Incorporating the Dsrm Into The Sucmentioning

confidence: 99%

See 4 more Smart Citations

Value of thermostatic loads in future low-carbon Great Britain system

Trovato

Teng

Štrbac

2016

2016 Power Systems Computation Conference (PSCC)

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“…Recently it has been demonstrated in [10] that the aggregate flexibility of ≫ 1 heterogeneous TCLs is summarized by the (1-4).…”

Section: Aggregate Model and Control Of Tclsmentioning

confidence: 99%

Section: Aggregate Model and Control Of Tclsmentioning

confidence: 99%

Section: Aggregate Model and Control Of Tclsmentioning

confidence: 99%

Section: Demand Side Response Modelmentioning

confidence: 99%

“…The TCL power levels are constrained by (2) and (4). The response services provision and the consequent payback are constrained by (7)(8)(9)(10)(11). In Sec.…”

Section: A Incorporating the Dsrm Into The Sucmentioning

confidence: 99%

See 3 more Smart Citations

Value of thermostatic loads in future low-carbon Great Britain system

Trovato

Teng

Štrbac

2016

2016 Power Systems Computation Conference (PSCC)

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Characterization of Aggregated Building Heating, Ventilation, and Air Conditioning Load as a Flexibility Service Using Gray‐Box Modeling

2021

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Integrating large amounts of volatile renewable power into the electricity grid requires ancillary services (ASs) from multiple providers including flexible demand. These should be comparable by uniform and efficiently evaluable performance criteria. The objective is to characterize the technical flexibility of aggregated building heating, ventilation, and air conditioning (HVAC) under different operating conditions. New bounds of flexible power and holding durations, accordingly pay‐back power and recovery times, and ramping rates are derived, using a new gray‐box model of stochastically actuated aggregations of thermostatically controlled loads (TCLs) that can serve as well for load control. New closed formulas of the expected switching temperatures are derived using survival processes and hazard functions. This ex‐ante characterization enables fast decision tools for AS feasibility testing and planning by demand aggregators, as it neither relies on simulation or optimization, nor on the identification and clustering of unit‐level parameters. The estimates are explored in a sensitivity study of urban‐level heat pump heating with respect to six key input factors. A case study using dynamic regulation signals from Pennsylvania–New Jersey–Maryland (PJM) demonstrates the benefit, in terms of tracking precision, of the refined energy measures over pure energy or power capacity bounds.

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Overview of TCLs in Smart Grids

Meng

Gao

2021

Integration of Distributed Resources in Smart Grids for Demand Response and Transactive Energy

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Leaky storage model for optimal multi‐service allocation of thermostatic loads

Cited by 61 publications

References 14 publications

Value of thermostatic loads in future low-carbon Great Britain system

Value of thermostatic loads in future low-carbon Great Britain system

Characterization of Aggregated Building Heating, Ventilation, and Air Conditioning Load as a Flexibility Service Using Gray‐Box Modeling

Overview of TCLs in Smart Grids

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