Modeling and control of thermostatically controlled loads

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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    ABSTRACT: We explore methods to use thermostatically controlled loads (TCLs), such as water heaters and air conditioners, to provide ancillary services by assisting in balancing generation and load. We show that by adding simple imbedded instructions and a small amount of memory to temperature controllers of TCLs, it is possible to design open-loop control algorithms capable of creating short-term pulses of demand response without unwanted power oscillations associated with temporary synchronization of the TCL dynamics. By moving a small amount of intelligence to each of the end point TCL devices, we are able to leverage our knowledge of the time dynamics of TCLs to shape the demand response pulses for different power system applications. A significant benefit of our open-loop method is the reduction from two-way to one-way broadcast communication which also eliminates many basic consumer privacy issues. In this work, we focus on developing the algorithms to generate a set of fundamental pulse shapes that can subsequently be used to create demand response with arbitrary profiles. Demand response control methods, such as the one developed here, open the door to fast, nonperturbative control of large aggregations of TCLs.
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    ABSTRACT: This paper explores the flexibility of thermostatically controlled loads (TCLs) as a way of absorbing the intermittent and variable renewable energy resources. We propose an innovative mechanism, which allows load serving entities (LSEs) to pay incentive rates to customers for the control of thermostats. We apply a game-theoretic framework to model the interactive behavior between LSEs and customers in the contract design and subscription process. As a result, LSEs are able to manipulate subscribers' thermostat set-points in a certain way that the aggregated TCLs are managed to follow the renewable energy output as close as possible. This is formulated as a linear-quadratic (LQ) tracking problem with inequality constraints and is solved by model predictive control approach. We also study the sensitivity of contract design parameters to the maximum tracking error and total cost.
    2013 Industrial and Systems Engineering Research Conference; 05/2013
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    ABSTRACT: Recently it has been shown that an aggregation of Thermostatically Controlled Loads (TCLs) can be utilized to provide fast regulating reserve service for power grids and the behavior of the aggregation can be captured by a stochastic battery with dissipation. In this paper, we address two practical issues associated with the proposed battery model. First, we address clustering of a heterogeneous collection and show that by finding the optimal dissipation parameter for a given collection, one can divide these units into few clusters and improve the overall battery model. Second, we analytically characterize the impact of imposing a no-short-cycling requirement on TCLs as constraints on the ramping rate of the regulation signal. We support our theorems by providing simulation results.

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