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Virtual energy storage from TCLs using QoS preserving local randomized control

Published: 07 November 2018 Publication History
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  • Abstract

    We propose a control architecture for distributed coordination of a collection of on/off TCLs (thermostatically controlled loads), such as residential air conditioners, to provide the same service to the power grid as a large battery. This involves a collection of loads to coordinate their on/off decisions so that the aggregate power consumption profile tracks a grid-supplied reference. A key constraint is to maintain each consumer's quality of service (QoS). Recent works have proposed randomization at the loads. Thermostats at the loads are replaced by a randomized controller, and the grid broadcasts a scalar to all loads, which tunes the probability of turning on or off at each load depending on its state. In this paper we propose a modification of a previous design by Meyn and Bušić. The previous design by Meyn and Bušić ensures that the indoor temperature remains within a pre-specified bound, but other QoS metrics, especially the frequency of turning on and off was not limited. The controller we propose can be tuned to reduce the cycling rate of a TCL to any desired degree. The proposed design is compared against the design by Meyn and Bušić and another well cited design in the literature on control of TCL populations, by Mathieu et al. We show through simulations that the proposed controller is able to reduce the cycling of individual ACs compared to the previous designs with little loss in tracking of the grid-supplied reference signal.

    References

    [1]
    Prabir Barooah, Ana Bušić, and Sean Meyn. 2015. Spectral decomposition of demand side flexibility for reliable ancillary service in a smart grid. In 48th Hawaii International Conference on Systems Science (HICSS). invited paper.
    [2]
    S. Bashash and H. K. Fathy. 2011. Modeling and control insights into demand-side energy management through setpoint control of thermostatic loads. In Proceedings of the 2011 American Control Conference. 4546--4553.
    [3]
    Ana Bušić, Md Umar Hashmi, and Sean Meyn. 2017. Distributed control of a fleet of batteries. In American Control Conference (ACC). 3406--3411.
    [4]
    Ana Bušić and Sean Meyn. 2016. Distributed randomized control for demand dispatch. In IEEE conference on decision and control. 6964--6971.
    [5]
    Duncan S Callaway. 2009. Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy. Energy Conversion and Management 50, 5 (2009), 1389--1400.
    [6]
    Yue Chen. 2016. Markovian Demand Dispatch Design for Virtual Energy Storage to Support Renewable Energy Integration. Ph.D. Dissertation. Gainesville, FL, USA. Advisor(s) Meyn, Sean P.
    [7]
    Yue Chen, Md Umar Hashmi, Joel Mathias, Ana Busic, and Sean Meyn. 2017. Distributed Control Design for Balancing the Grid Using Flexible Loads. In IMA Volume on the Control of Energy Markets and Grids. 1--26. https://hal.archives-ouvertes.fr/hal-01656726
    [8]
    Austin R. Coffman, Ana Busic, and Prabir Barooah. 2018. A Study of Virtual Energy Storage From Thermostatically Controlled Loads Under Time-Varying Weather Conditions. In 5th International High Performance Buildings Conference at Purdue. 10.
    [9]
    Alexander W. Dowling and Victor M. Zavala. 2017. Economic opportunities for industrial systems from frequency regulation markets. Computers & Chemical Engineering (2017).
    [10]
    S. Kundu, N. Sinitsyn, S. Backhaus, and I. Hiskens. 2011. Modelling and control of thermostatically controlled loads. In Power systems computation conference.
    [11]
    Yashen Lin, Prabir Barooah, and Johanna Mathieu. 2017. Ancillary services through demand scheduling and control of commercial buildings. IEEE Transactions on Power Systems 32 (January 2017), 186 -- 197. Issue 1.
    [12]
    Yashen Lin, Prabir Barooah, Sean Meyn, and Timothy Middelkoop. 2015. Experimental evaluation of frequency regulation from commercial building HVAC systems. IEEE Transactions on Smart Grid 6 (2015), 776 -- 783. Issue 2.
    [13]
    M. Liu and Y. Shi. 2016. Model Predictive Control of Aggregated Heterogeneous Second-Order Thermostatically Controlled Loads for Ancillary Services. IEEE Transactions on Power Systems 31, 3 (May 2016), 1963--1971.
    [14]
    Y.V. Makarov, J. Ma, S. Lu, and T.B. Nguyen. 2008. Assessing the value of regulation resources based on their time response characteristics. Pacific Northwest National Laboratory (2008).
    [15]
    Johanna L. Mathieu, Mark Dyson, and Duncan S. Callaway. 2012. Using residential electric loads for fast demand response: The potential resource and revenues, the costs, and policy recommendations. In In Proceedings of the ACEEE Summer Study on Buildings.
    [16]
    Johanna L Mathieu, Stephan Koch, and Duncan S Callaway. 2013. State estimation and control of electric loads to manage real-time energy imbalance. IEEE Transactions on Power Systems 28, 1 (2013), 430--440.
    [17]
    Sean Meyn, Prabir Barooah, Ana Bušić, Yue Chen, and Jordan Ehren. 2015. Ancillary service to the grid from intelligent deferrable loads. IEEE Trans. Automat. Control 60 (March 2015), 2847 -- 2862. Issue 1.
    [18]
    Cristian Perfumo, Ernesto Kofman, Julio H. Braslavsky, and John K. Ward. 2012. Load management: Model-based control of aggregate power for populations of thermostatically controlled loads. Energy Conversion and Management 55 (2012), 36 -- 48.
    [19]
    Ana Radovanovic, William D. Heavlin, and Sila Kiliccote. 2016. Optimized Risk-Aware Nomination Strategy in Demand Response Markets. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys '16). ACM, New York, NY, USA, 99--108.
    [20]
    W. Zhang, J. Lian, C. Y. Chang, and K. Kalsi. 2013. Aggregated Modeling and Control of Air Conditioning Loads for Demand Response. IEEE Transactions on Power Systems 28, 4 (Nov 2013), 4655--4664.

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    1. Virtual energy storage from TCLs using QoS preserving local randomized control

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        cover image ACM Conferences
        BuildSys '18: Proceedings of the 5th Conference on Systems for Built Environments
        November 2018
        211 pages
        ISBN:9781450359511
        DOI:10.1145/3276774
        • General Chair:
        • Rajesh Gupta,
        • Program Chairs:
        • Polly Huang,
        • Marta Gonzalez
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        Published: 07 November 2018

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        Author Tags

        1. demand response
        2. distributed control
        3. randomized control
        4. virtual energy storage

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        • (2023)Aggregate Flexibility Capacity of TCLs With Cycling ConstraintsIEEE Transactions on Power Systems10.1109/TPWRS.2022.316007138:1(52-62)Online publication date: Jan-2023
        • (2023)A unified framework for coordination of thermostatically controlled loadsAutomatica (Journal of IFAC)10.1016/j.automatica.2023.111002152:COnline publication date: 10-May-2023
        • (2021)A model-free method for learning flexibility capacity of loads providing grid support2021 American Control Conference (ACC)10.23919/ACC50511.2021.9483207(2881-2886)Online publication date: 25-May-2021
        • (2021)Control-oriented modeling of TCLs2021 American Control Conference (ACC)10.23919/ACC50511.2021.9482922(4148-4154)Online publication date: 25-May-2021
        • (2020)Resource allocation with local QoS: Flexible loads in the power grid2020 IEEE Conference on Control Technology and Applications (CCTA)10.1109/CCTA41146.2020.9206313(1060-1065)Online publication date: Aug-2020

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