Article

Modeling and control of thermostatically controlled loads

01/2011;
Source: arXiv

ABSTRACT As the penetration of intermittent energy sources grows substantially, loads
will be required to play an increasingly important role in compensating the
fast time-scale fluctuations in generated power. Recent numerical modeling of
thermostatically controlled loads (TCLs) has demonstrated that such load
following is feasible, but analytical models that satisfactorily quantify the
aggregate power consumption of a group of TCLs are desired to enable controller
design. We develop such a model for the aggregate power response of a
homogeneous population of TCLs to uniform variation of all TCL setpoints. A
linearized model of the response is derived, and a linear quadratic regulator
(LQR) has been designed. Using the TCL setpoint as the control input, the LQR
enables aggregate power to track reference signals that exhibit step, ramp and
sinusoidal variations. Although much of the work assumes a homogeneous
population of TCLs with deterministic dynamics, we also propose a method for
probing the dynamics of systems where load characteristics are not well known.

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    • "In this paper, we show that Thermostatically Controlled Load (TCLs) have a great potential for providing fast regulation service, due to their large population size and the ability of being turned ON/OFF simultaneously. The proof of concept of using TCLs to provide regulation reserve and load following has been reported in Lu (2012), Mathieu et al. (2013a), Kundu et al. (2011), Zhang et al. (2013), and Bashash and Fathy (2013). Other related work include study of commercial HVAC (Heating, Ventilation, and Air-Conditioning ) systems, residential pool pumps, and electric vehicles to provide ancillary services to the grid (Lin et al., 2013; Hao et al., 2014a; Oldewurtel et al., 2013; Meyn et al., 2013; Hao and Chen, 2015; Barooah et al., 2015; Kempton et al., 2008; Nayyar et al., 2013). "
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    ABSTRACT: Residential Thermostatically Controlled Loads (TCLs) such as Air Conditioners (ACs), heat pumps, water heaters and refrigerators have an enormous thermal storage potential for providing regulation reserve to the grid. In this paper, we study the potential resource, regulatory requirements, and economic analysis for TCLs providing frequency regulation service. In particular, we show that the potential resource of TCLs in California is more than enough for both current and predicted near-future regulation requirements for the California power system. Moreover, we estimate the cost and revenue of TCLs, discuss the qualification requirements, recommended policy changes, and participation incentive methods, and compare TCLs with other energy storage technologies. We show that TCLs are potentially more cost-effective than other energy storage technologies such as flywheels, Li-ion, advanced lead acid and Zinc Bromide batteries.
    Energy Policy 09/2014; 79. DOI:10.1016/j.enpol.2015.01.013 · 2.70 Impact Factor
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    • "The proof of concept of using TCLs to provide regulation reserve and load following were reported in [11]–[14]. Timebased and temperature-based priority control methods that are similar to our work were developed in [12] and [13]. The work of [15], [16] are closely related to the present paper. "
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    ABSTRACT: Thermostatically Controlled Loads (TCLs) such as air conditioners, heat pumps, water heaters and refrigerators have a great potential for providing regulation reserve to the grid. This paper aims to provide a foundation for a practical method of enabling TCLs to provide regulation service. We study the economic, regulatory, and practical aspects to realize such a vision. We show that the potential of TCLs in California is more than enough for both current and predicted near-future regulation requirements. Moreover, we estimate the cost and revenue of TCLs, discuss the qualification requirements and participation incentive methods, and present a practical control framework for TCLs to provide regulation service. Numerical experiments are provided to illustrate the efficacy of our methods in addressing practical issues such as short cycling of units, communication latency, and dynamics modeling errors.
    American Control Conference; 06/2014
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    • "These appliances, in our case, consist of thermostatic loads (e.g., space/water heating, refrigeration). These kinds of loads are a promising category for engaging in short-term ancillary services as they are typically characterized by slow-evolving states (e.g., temperature with hourly time dynamics) that allow for control flexibility (e.g., [11], [12], [13]). Contrary to classic DR approaches, GECN acts on a fast time scale (in the order of few seconds) without significantly impacting the end customers. "
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    ABSTRACT: Demand response (DR) has traditionally targeted peak shaving for the optimal allocation of electricity consumption on a time scale that ranges from minutes to hours. However, with the availability of advanced monitoring and communication infrastructure, the potential of real-time DR for providing ancillary services to the grid has not yet been adequately explored. In this work, we propose a low-overhead decentralized DR control mechanism, henceforth called Grid Explicit Congestion Notification (GECN), intended for deployment by distribution network operators (DNOs) to provide ancillary services to the grid by a seamless control of a large population of elastic appliances. Contrary to classic DR approaches, the proposed scheme aims to continuously support the grid needs in terms of voltage control by broadcasting low-bit rate control signals on a fast time scale (i.e., every few seconds). Overall, the proposed DR mechanism is designed to i) indirectly reveal storage capabilities of end-customers and ii) have a negligible impact on the end-customer. In order to estimate the benefits of the proposed mechanism, the evaluation of the algorithm is carried out by using the IEEE 13 nodes test feeder in combination with realistic load profiles mixed with non-controllable demand and non-dispatchable generation from photovoltaic distributed generation.
    IEEE Transactions on Smart Grid 03/2014; 5(2):622-631. DOI:10.1109/TSG.2013.2275004 · 4.33 Impact Factor
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