Flexibility capacity of thermostatically controlled loads with cycling/lock-out constraints

Coffman, Austin R.; Cammardella, Neil; Barooah, Prabir; Meyn, Sean

doi:10.23919/acc45564.2020.9147817

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…Furthermore, the tracking RMSE increases as the packet length increases as expected since due to down-ramp limited response in case of longer packet lengths. Cycling can be included as a constraint in demand dispatch, which is especially important in the case of coordinating air-conditioners and HVAC thermostats [28], [32]. The next subsection shows that introducing randomization on packet lengths surprisingly reduces cycling.…”

Section: A Effect Of Packet Lengthmentioning

confidence: 97%

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Optimal Frequency Regulation using Packetized Energy Management

Brahma¹,

Khurram²,

Ossareh³

et al. 2021

Preprint

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Packetized energy management (PEM) is a demand dispatch scheme that can be used to provide ancillary services such as frequency regulation. In PEM, distributed energy resources (DERs) are granted uninterruptible access to the grid for a pre-specified time interval called the packet length. This results in a down ramp-limited response in PEM for DERs that can only consume power from the grid. In this work, a linearized virtual battery model of PEM is provided that is capable of predicting the down-ramp limited output of PEM and is used in a model predictive control (MPC) framework to improve the performance of PEM in tracking an automatic generation control (AGC) signal. By performing statistical analysis on the AGC regulation signal, PJM Reg-D, an ARMA model is derived as a predictor for the MPC-based precompensator. Finally, as an alternative to MPC, it is shown that by varying the packet length as a function of time, for example through packet randomization, frequency regulation can be improved under PEM.

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Section: A Effect Of Packet Lengthmentioning

confidence: 97%

“…Packet randomization engenders improved tracking during down-ramps by the availability of packets with shorter duration. Simulation results indicate that packet randomization has an added benefit of (slightly) reducing the number of times a TCL cycles between ON/OFF states, which can decrease wear-and-tear [28].…”

Section: Introductionmentioning

confidence: 99%

Optimal Frequency Regulation using Packetized Energy Management

Brahma¹,

Khurram²,

Ossareh³

et al. 2021

Preprint

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show abstract

“…Coffman et al [ 41,42 ] study lockout constraints as well but pursue a different goal named reference planning. A grid balancing authority successively extracts and assigns suitably shaped portions from a residual load signal to those flexible resources that can best handle these according to their capacities.…”

Section: Problem Definition Related Work and Contributionsmentioning

confidence: 99%

“…By supplying capacity bounds as static constraints, TBs support planning and optimization tasks such as the economic dispatch of load and generation units. [ 35,41 ] To control and characterize load aggregations dynamically, TCL in fact lend themselves as a model and need no detour via TB or polytope descriptions, because the Fokker–Planck partial differential equation (FPE–PDE) raises their descriptive power to aggregations. [ 45–48 ] The FPE governs the evolution of temperature densities in time.…”

Section: Problem Definition Related Work and Contributionsmentioning

confidence: 99%

“…Roughly speaking, two avenues exist to characterize the flexibility of TCL load aggregates: TBs using geometric aggregation (TB [ 15,33–41 ] ), and dynamic analysis of temperature densities or histograms based on the Fokker–Planck equations (FPE, [ 8,9,24–27,45–52,56 ] ). While the TB approach captures only few criteria, the more versatile FPE requires a costly ex post analysis through simulation or optimization.…”

Section: Problem Definition Related Work and Contributionsmentioning

confidence: 99%

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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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Multi-period optimal scheduling of building loads based on accurate virtual battery model

Zhu,

Wang,

et al. 2025

Energy and Buildings

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Flexibility capacity of thermostatically controlled loads with cycling/lock-out constraints

Cited by 11 publications

References 15 publications

Optimal Frequency Regulation using Packetized Energy Management

Optimal Frequency Regulation using Packetized Energy Management

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

Multi-period optimal scheduling of building loads based on accurate virtual battery model

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