State Estimation for the Individual and the Population in Mean Field Control With Application to Demand Dispatch

Yue Chen 1 Ana Bušic 2, 3 Sean P. Meyn 1
3 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique : UMR 8548, Inria de Paris
Abstract : This paper concerns state estimation problems in a mean field control setting. In a finite population model, the goal is to estimate the joint distribution of the population state and the state of a typical individual. The observation equations are a noisy measurement of the population. The general results are applied to demand dispatch for regulation of the power grid, based on randomized local control algorithms. In prior work by the authors it is shown that local control can be designed so that the aggregate of loads behaves as a controllable resource, with accuracy matching or exceeding traditional sources of frequency regulation. The operational cost is nearly zero in many cases. The information exchange between grid and load is minimal, but it is assumed in the overall control architecture that the aggregate power consumption of loads is available to the grid operator. It is shown that the Kalman filter can be constructed to reduce these communication requirements, and to provide the grid operator with accurate estimates of the mean and variance of quality of service (QoS) for an individual load.
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Submitted on : Thursday, April 13, 2017 - 6:01:01 PM
Last modification on : Thursday, October 17, 2019 - 12:36:05 PM

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Yue Chen, Ana Bušic, Sean P. Meyn. State Estimation for the Individual and the Population in Mean Field Control With Application to Demand Dispatch. IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2017, 62 (3), pp.1138 - 1149. ⟨10.1109/TAC.2016.2572880⟩. ⟨hal-01508107⟩

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