Jianming Lian

Aggregated Modeling and Control of Air Conditioning Loads for Demand Response

Demand response is playing an increasingly important role in the efficient and reliable operation of the electric grid. Modeling the dynamic behavior of a large population of responsive loads is especially important to evaluate the effectiveness of various demand response strategies. In this paper, a highly accurate aggregated model is developed for a population of air conditioning loads. The model effectively includes statistical information of the load population, systematically deals with load heterogeneity, and accounts for second-order dynamics necessary to accurately capture the transient dynamics in the collective response. Based on the model, a novel aggregated control strategy is designed for the load population under realistic conditions. The proposed controller is fully responsive and achieves the control objective without sacrificing end-use performance. The proposed aggregated modeling and control strategy is validated through realistic simulations using GridLAB-D. Extensive simulation results indicate that the proposed approach can effectively manage a large number of air conditioning systems to provide various demand response services, such as frequency regulation and peak load reduction.

Brief paper: Sliding-mode observers for systems with unknown inputs: A high-gain approach

Sliding-mode observers can be constructed for systems with unknown inputs if the so-called observer matching condition is satisfied. However, most systems do not satisfy this condition. To construct sliding-mode observers for systems that do not satisfy the observer matching condition, auxiliary outputs are generated using high-gain approximate differentiators and then employed in the design of sliding-mode observers. The state estimation error of the proposed high-gain approximate differentiator based sliding-mode observer is shown to be uniformly ultimately bounded with respect to a ball whose radius is a function of design parameters. Finally, the unknown input reconstruction using the proposed observer is analyzed and then illustrated with a numerical example.

Self-Organizing Radial Basis Function Network for Real-Time Approximation of Continuous-Time Dynamical Systems

Real-time approximators for continuous-time dynamical systems with many inputs are presented. These approximators employ a novel self-organizing radial basis function (RBF) network, which varies its structure dynamically to keep the prescribed approximation accuracy. The RBFs can be added or removed online in order to achieve the appropriate network complexity for the real-time approximation of the dynamical systems and to maintain the overall computational efficiency. The performance of this variable structure RBF network approximator with both Gaussian RBF (GRBF) and raised-cosine RBF (RCRBF) is analyzed. The compact support of RCRBF enables faster training and easier output evaluation of the network than that of the network with GRBF. The proposed real-time self-organizing RBF network approximator is then employed to approximate both linear and nonlinear dynamical systems to illustrate the effectiveness of our proposed approximation scheme, especially for higher order dynamical systems. The uniform ultimate boundedness of the approximation error is proved using the second method of Lyapunov.

Market-Based Coordination of Thermostatically Controlled Loads—Part I: A Mechanism Design Formulation

This paper focuses on the coordination of a population of thermostatically controlled loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the device bidding and market clearing strategies to motivate self-interested users to realize efficient energy allocation subject to a peak energy constraint. This coordination problem is formulated as a mechanism design problem, and we propose a mechanism to implement the social choice function in dominant strategy equilibrium. The proposed mechanism consists of a novel bidding and clearing strategy that incorporates the internal dynamics of TCLs in the market mechanism design, and we show it can realize the team optimal solution. This paper is divided into two parts. Part I presents a mathematical formulation of the problem and develops a coordination framework using the mechanism design approach. Part II presents a learning scheme to account for the unknown load model parameters, and evaluates the proposed framework through realistic simulations.

Quadratic optimal control of switched linear stochastic systems

This paper studies a quadratic optimal control problem for discrete-time switched linear stochastic systems with nonautonomous subsystems perturbed by Gaussian random noises. The goal is to jointly design a deterministic switching sequence and a continuous feedback law to minimize the expectation of a finite-horizon quadratic cost function. Both the value function and the optimal control strategy are characterized analytically. A numerical relaxation framework is developed to efficiently compute a control strategy with a guaranteed performance upper bound. It is also proved that by choosing the relaxation parameter sufficiently small, the performance of the resulting control strategy can be made arbitrarily close to the optimal one.

Variable Neural Direct Adaptive Robust Control of Uncertain Systems

Direct adaptive robust state and output feedback controllers are proposed for the output tracking control of a class of uncertain systems. The proposed controllers incorporate a variable structure radial basis function (RBF) network to approximate unknown system dynamics, where the RBF network can determine its structure online dynamically. Radial basis functions can be added or removed to ensure the desired tracking accuracy and to prevent the network redundancy simultaneously. The closed-loop systems driven by the direct adaptive robust controllers are characterized by the guaranteed transient and steady-state tracking performance. The performance of the proposed output feedback controller is illustrated with numerical simulations.

Minimum-Time Consensus-Based Approach for Power System Applications

This paper presents minimum-time consensus-based distributed algorithms for power system applications, such as load shedding and economic dispatch. The proposed algorithms are capable of solving these problems in a minimum number of time steps instead of asymptotically as in most of the existing studies. Moreover, these algorithms are applicable to both undirected and directed communication networks. Simulation results are used to validate the proposed algorithms.

Modeling of Electric Water Heaters for Demand Response: A Baseline PDE Model

Demand response (DR) control can effectively relieve balancing and frequency regulation burdens on conventional generators, facilitate integrating more renewable energy, and reduce generation and transmission investments needed to meet peak demands. Electric water heaters (EWHs) have a great potential in implementing DR control strategies because: a) the EWH power consumption has a high correlation with daily load patterns; b) they constitute a significant percentage of domestic electrical load; c) the heating element is a resistor, without reactive power consumption; and d) they can be used as energy storage devices when needed. Accurately modeling the dynamic behavior of EWHs is essential for successfully designing DR controls. In this paper, a new partial differential equation (PDE) physics-based model is developed to capture the detailed temperature profiles at different tank locations, which is validated against field test data for more than 10 days. The developed PDE model is compared with the one-mass and two-mass models, and shows better performance in capturing water thermal dynamics for benchmark testing purposes.

Decentralized control of multimachine power systems

A novel decentralized dynamic output feedback controller is presented to deal with the transient stability of a class of multimachine power systems. The proposed decentralized control strategy employs local sliding mode observers to estimate the states of each machine, and the feedback gain matrix of each local controller is obtained by solving two linear matrix inequalities. In addition, local sliding mode observers are capable of reconstructing unknown interconnections between machines. The effectiveness of the proposed control strategy is illustrated by simulation of a three-machine power system.

Distributed Hierarchical Control Architecture for Transient Dynamics Improvement in Power Systems

In this paper, a novel distributed hierarchical control architecture is proposed for large-scale power systems. The newly proposed architecture facilitates faster and more accurate frequency restoration during primary frequency control, by providing decentralized robust control to several selected pilot generators in each area. At the local level, these decentralized robust controllers are designed to quickly damp oscillations and restore frequency after large faults and disturbances in the system. Incorporating this supplementary governor control helps the system reach the nominal frequency without necessarily requiring secondary frequency control. Thus, at the area level, automatic generation control (AGC) actions are alleviated in terms of conducting frequency restoration. Moreover, at the area level, AGC coordinates with the decentralized robust controllers to successfully perform tie-line power balancing, while efficiently damping low-frequency inter-area oscillations. The interaction of local and area controllers is validated through detailed simulations.