Citation
Salman Nazir, Ian Hiskens. "Noise and Parameter Heterogeneity in Aggregate Models of Thermostatically Controlled Loads". IFAC World Congress, IFAC, July, 2017.

Abstract
Aggregate models are used in the analysis and control of large populations of thermostatically controlled loads (TCLs), such as air-conditioners and water heaters. The fidelity of such models is studied by analyzing the influences of noise and parameter heterogeneity on TCL aggregate dynamics. While TCLs can provide valuable services to the power systems, control may cause their temperatures to synchronize, which may then lead to undesirable power oscillations. Recent works has shown that the aggregate dynamics of TCLs can be modeled by tracking the evolution of probability densities over discrete temperature ranges or bins. To accurately capture oscillations in aggregate power, such bin-based models require a large number of bins. The process of obtaining the Markov state transition matrix that governs the dynamics can be computationally intensive when using Monte Carlo based system identification techniques. Existing analytical techniques are further limited as noise and heterogeneity in several thermal parameters are difficult to incorporate. These challenges are addressed by developing a fast analytical technique that incorporates noise and heterogeneity into bin-based aggregate models. Results show the identified and the analytical models match very closely. Studies consider the influence of model error, noise and parameter heterogeneity on the damping of oscillations. Results demonstrate that for a specific bin width, the model can be invariant to quantifiable levels of noise and parameter heterogeneity. Finally, a discussion is provided of cases where existing bin models may face challenges in capturing the influence of heterogeneity.

Electronic downloads

• Paper2_AnalyzingTCLmodels_vF.pdf · application/pdf · 665 kbytes

• HTML

  Salman Nazir, Ian Hiskens. <a
  href="http://www.cps-forces.org/pubs/230.html"
  >Noise and Parameter Heterogeneity in Aggregate Models of
  Thermostatically Controlled Loads</a>, IFAC World
  Congress, IFAC, July, 2017.

• Plain text

  Salman Nazir, Ian Hiskens. "Noise and Parameter
  Heterogeneity in Aggregate Models of Thermostatically
  Controlled Loads". IFAC World Congress, IFAC, July,
  2017.

• BibTeX

  @inproceedings{NazirHiskens17_NoiseParameterHeterogeneityInAggregateModelsOfThermostatically,
      author = {Salman Nazir and Ian Hiskens},
      title = {Noise and Parameter Heterogeneity in Aggregate
                Models of Thermostatically Controlled Loads},
      booktitle = {IFAC World Congress},
      organization = {IFAC},
      month = {July},
      year = {2017},
      abstract = {Aggregate models are used in the analysis and
                control of large populations of thermostatically
                controlled loads (TCLs), such as air-conditioners
                and water heaters. The fidelity of such models is
                studied by analyzing the influences of noise and
                parameter heterogeneity on TCL aggregate dynamics.
                While TCLs can provide valuable services to the
                power systems, control may cause their
                temperatures to synchronize, which may then lead
                to undesirable power oscillations. Recent works
                has shown that the aggregate dynamics of TCLs can
                be modeled by tracking the evolution of
                probability densities over discrete temperature
                ranges or bins. To accurately capture oscillations
                in aggregate power, such bin-based models require
                a large number of bins. The process of obtaining
                the Markov state transition matrix that governs
                the dynamics can be computationally intensive when
                using Monte Carlo based system identification
                techniques. Existing analytical techniques are
                further limited as noise and heterogeneity in
                several thermal parameters are difficult to
                incorporate. These challenges are addressed by
                developing a fast analytical technique that
                incorporates noise and heterogeneity into
                bin-based aggregate models. Results show the
                identified and the analytical models match very
                closely. Studies consider the influence of model
                error, noise and parameter heterogeneity on the
                damping of oscillations. Results demonstrate that
                for a specific bin width, the model can be
                invariant to quantifiable levels of noise and
                parameter heterogeneity. Finally, a discussion is
                provided of cases where existing bin models may
                face challenges in capturing the influence of
                heterogeneity.},
      URL = {http://cps-forces.org/pubs/230.html}
  }
  

Posted by Ian Hiskens on 28 Feb 2017.
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