Abstract
This paper studies the wind power regulation problem by controlling a population of thermostatically controlled loads (TCLs) in smart grids. With each TCL endowed with a cost function related to its room temperature, we formulate the wind power regulation problem into a quadratic optimization problem, for which we present a decentralized bisection algorithm relying on an aggregator. The algorithm converges fast and is decentralized in the sense that the TCLs conduct local computation and keep the parameters’ privacy from the aggregator. The proposed algorithm also includes a Kalman filter based parameter identification technique to deal with the time-varying thermal characteristics of TCLs. Simulations are given to show the performance of our algorithm.
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