Borhan Molazem Sanandaji

Aggregate Flexibility of Thermostatically Controlled Loads

It is widely accepted that thermostatically controlled loads (TCLs) can be used to provide regulation reserve to the grid. We first argue that the aggregate flexibility offered by a collection of TCLs can be succinctly modeled as a stochastic battery with dissipation. We next characterize the power limits and energy capacity of this battery model in terms of TCL parameters and random exogenous variables such as ambient temperature and user-specified set-points. We then describe a direct load control architecture for regulation service provision. Here, we use a priority-stack-based control framework to select which TCLs to control at any time. The control objective is for the aggregate power deviation from baseline to track an automatic generation control signal supplied by the system operator. Simulation studies suggest the practical promise of our methods.

A generalized battery model of a collection of Thermostatically Controlled Loads for providing ancillary service

The thermal storage potential of Thermostatically Controlled Loads (TCLs) is a tremendous flexible resource for providing various ancillary services to the grid. In this work, we study aggregate modeling, characterization, and control of TCLs for frequency regulation service provision. We propose a generalized battery model for aggregating flexibility of a collection of TCLs. A theoretical characterization of the aggregate power limits and energy capacity of TCLs is provided. Moreover, we propose a priority-stack-based control strategy to manipulate the power consumption of TCLs for frequency regulation, while preventing short cycling on the units. Numerical experiments are provided to show the accuracy of the proposed model and the efficacy of the developed control method.

Exact topology identification of large-scale interconnected dynamical systems from compressive observations

In this paper, we consider the problem of identifying the exact topology of an interconnected dynamical network from a limited number of measurements of the individual nodes. Within the network graph, we assume that interconnected nodes are coupled by a discrete-time convolution process, and we explain how, given observations of the node outputs, the problem of topology identification can be cast as solving a linear inverse problem. We use the term compressive observations in the case when there is a limited number of measurements available and thus the resulting inverse problem is highly underdetermined. Inspired by the emerging field of Compressive Sensing (CS), we then show that in cases where network interconnections are suitably sparse (i.e., the network contains sufficiently few links), it is possible to perfectly identify the topology from small numbers of node observations, even though this leaves a highly underdetermined set of linear equations. This can dramatically reduce the burden of data acquisition for problems involving network identification. The main technical novelty of our approach is in casting the identification problem as the recovery of a block-sparse signal x ∈ RN from the measurements b = Ax ∈ RM with M <; N, where the measurement matrix A is a block-concatenation of Toeplitz matrices. We discuss identification guarantees, introduce the notion of network coherence for the analysis of interconnected networks, and support our discussions with illustrative simulations.

Model Predictive Control of regulation services from commercial buildings to the smart grid

We first demonstrate that the demand-side flexibility of the Heating Ventilation and Air Conditioning (HVAC) system of a typical commercial building can be exploited for providing frequency regulation service to the power grid using at-scale experiments. We then show how this flexibility in power consumption of building HVAC system can be leveraged for providing regulation service. To this end, we consider a simplified model of the power grid with uncertain demand and generation. We present a Model Predictive Control (MPC) scheme to direct the ancillary service power flow from buildings to improve upon the classical Automatic Generation Control (AGC) practice. We show how constraints such as slow and fast ramping rates for various ancillary service providers, and short-term load forecast information can be integrated into the proposed MPC framework. Finally, we provide extensive simulation results to illustrate the effectiveness of the proposed methodology for enhancing grid frequency regulation.

On the observability of linear systems from random, compressive measurements

Recovering or estimating the initial state of a high-dimensional system can require a potentially large number of measurements. In this paper, we explain how this burden can be significantly reduced for certain linear systems when randomized measurement operators are employed. Our work builds upon recent results from the field of Compressive Sensing (CS), in which a high-dimensional signal containing few nonzero entries can be efficiently recovered from a small number of random measurements. In particular, we develop concentration of measure bounds for the observability matrix and explain circumstances under which this matrix can satisfy the Restricted Isometry Property (RIP), which is central to much analysis in CS. We also illustrate our results with a simple case study of a diffusion system. Aside from permitting recovery of sparse initial states, our analysis has potential applications in solving inference problems such as detection and classification of more general initial states.

Fast Regulation Service Provision via Aggregation of Thermostatically Controlled Loads

Federal Energy Regulatory Commission (FERC) Order 755 requires scheduling coordinators to procure and compensate more for regulation resources with faster ramping rates. Thermostatically Controlled Loads (TCLs) are a tremendous demand-side resource for providing fast regulation service due to their population size and their ability of being turned ON or OFF simultaneously. In this paper, we consider modeling and control of a collection of TCLs to provide such regulation service. We first develop a non-uniform bin state transition model for aggregate modeling of a collection of TCLs. The non-uniform model presents a potential for more accurate prediction while requiring fewer number of bins (reducing the complexity of the model) than the existing uniform bin models. We also propose a randomized priority control strategy to manipulate the power consumption of TCLs to track a regulation signal, while preventing short cycling, and reducing wear and tear on the equipment. The proposed control strategy is decentralized in the sense that each TCL makes its own decision solely based on a common broadcast command signal. This framework reduces the communication and computational efforts required for implementing the control strategy. We provide illustrative simulations to show the accuracy of the developed non-uniform model and efficacy of the proposed control strategy.

Frequency regulation from flexible loads: Potential, economics, and implementation

Thermostatically Controlled Loads (TCLs) such as air conditioners, heat pumps, water heaters and refrigerators have a great potential for providing regulation reserve to the grid. This paper aims to provide a foundation for a practical method of enabling TCLs to provide regulation service. We study the economic, regulatory, and practical aspects to realize such a vision. We show that the potential of TCLs in California is more than enough for both current and predicted near-future regulation requirements. Moreover, we estimate the cost and revenue of TCLs, discuss the qualification requirements and participation incentive methods, and present a practical control framework for TCLs to provide regulation service. Numerical experiments are provided to illustrate the efficacy of our methods in addressing practical issues such as short cycling of units, communication latency, and dynamics modeling errors.

Compressive System Identification in the Linear Time-Invariant framework

Selection of an efficient model parametrization (model order, delay, etc.) has crucial importance in parametric system identification. It navigates a trade-off between representation capabilities of the model (structural bias) and effects of over-parametrization (variance increase of the estimates). There exists many approaches to this widely studied problem in terms of statistical regularization methods and information criteria. In this paper, an alternative ℓ1 regularization scheme is proposed for estimation of sparse linear-regression models based on recent results in compressive sensing. It is shown that the proposed scheme provides consistent estimation of sparse models in terms of the so-called oracle property, it is computationally attractive for large-scale over-parameterized models and it is applicable in case of small data sets, i.e., underdetermined estimation problems. The performance of the approach w.r.t. other regularization schemes is demonstrated in an extensive Monte Carlo study.

Compressive topology identification of interconnected dynamic systems via Clustered Orthogonal Matching Pursuit

In this paper, we consider topology identification of large-scale interconnected dynamical systems. The system topology under study has the structure of a directed graph. Each edge of the directed network graph represents a Finite Impulse Response (FIR) filter with a possible transport delay. Each node is a summer, whose inputs are the signals from the incoming edges, while the output of the summer is sent to outgoing edges. Edges of the graph can be of different unknown orders and delays. Both the graph topology and the FIR filters and delays that make up the edges are unknown. We aim to do the topology identification from the smallest possible number of node observations when there is limited data available and for this reason, we call this problem Compressive Topology Identification (CTI).

Compressive System Identification of LTI and LTV ARX models

In this paper, we consider identifying Auto Regressive with eXternal input (ARX) models for both Linear Time-Invariant (LTI) and Linear Time-Variant (LTV) systems. We aim at doing the identification from the smallest possible number of observations. This is inspired by the field of Compressive Sensing (CS), and for this reason, we call this problem Compressive System Identification (CSI).