Prabir Barooah

DistressNet: a wireless ad hoc and sensor network architecture for situation management in disaster response

Situational awareness in a disaster is critical to effective response. Disaster responders require timely delivery of high volumes of accurate data to make correct decisions. To meet these needs, we present DistressNet, an ad hoc wireless architecture that supports disaster response with distributed collaborative sensing, topology-aware routing using a multichannel protocol, and accurate resource localization. Sensing suites use collaborative and distributed mechanisms to optimize data collection and minimize total energy use. Message delivery is aided by novel topology management, while congestion is minimized through the use of mediated multichannel radio protocols. Estimation techniques improve localization accuracy in difficult environments.

Error Amplification and Disturbance Propagation in Vehicle Strings with Decentralized Linear Control

We consider the problem of controlling a string of vehicles moving in one dimension so that they all follow a lead vehicle with a constant spacing between successive vehicles. We examine the symmetric bidirectional control architecture, where the control action on a vehicle depends equally on the spacing errors with respect to its adjacent vehicles. Performance of this decentralized scheme in terms of spacing error amplification and disturbance propagation is investigated. The results established in this paper show that a symmetric bidirectional architecture with a linear controller suffers from fundamental limitations on closed loop performance that cannot be mitigated by appropriate control design.

Ancillary Service to the Grid Through Control of Fans in Commercial Building HVAC Systems

The thermal storage potential in commercial buildings is an enormous resource for providing various ancillary services to the grid. In this paper, we show how fans in Heating, Ventilation, and Air Conditioning (HVAC) systems of commercial buildings alone can provide substantial frequency regulation service, with little change in their indoor environments. A feedforward architecture is proposed to control the fan power consumption to track a regulation signal. The proposed control scheme is then tested through simulations based on a calibrated high fidelity non-linear model of a building. Model parameters are identified from data collected in Pugh Hall, a commercial building located on the University of Florida campus. For the HVAC system under consideration, numerical experiments demonstrate how up to 15% of the rated fan power can be deployed for regulation purpose while having little effect on the building indoor temperature. The regulation signal that can be successfully tracked is constrained in the frequency band [1/τ0,1/τ1], where τ0 ≈ 3 minutes and τ1 ≈ 8 seconds. Our results indicate that fans in existing commercial buildings in the U.S. can provide about 70% of the current national regulation reserve requirements in the aforementioned frequency band. A unique advantage of the proposed control scheme is that assessing the value of the ancillary service provided is trivial, which is in stark contrast to many demand-response programs.

Agent-based and graphical modelling of building occupancy

We propose a novel stochastic agent-based model of occupancy dynamics in a building with an arbitrary number of zones and occupants. Simulation of the model yields time-series of the location of each agent (a software representation of an occupant). The model is meant to provide realistic simulation of occupancy dynamics in non-emergency situations. Comparison of the models prediction of distributions of random variables such as first arrival time of a building is provided against those estimated from measurements in commercial buildings. We also propose a lower complexity graphical model of occupancy evolution in multi-zone buildings. The graphical model captures information on mean occupancy and correlation among occupancy at various zones in the building. The agent-based model can be used in conjunction with building performance simulation tools, while the graphical model is more suitable for real-time applications, such as occupancy estimation with noisy sensor measurements.

Building thermal model reduction via aggregation of states

This paper proposes an aggregation-based model reduction method for thermal models of large buildings. Using an electric analogy, the baseline thermal model is represented as an RC-network. The proposed model reduction methodology is used to obtain a simpler (with fewer states) multi-scale representation of this network. The methodology preserves the electrical analogy and retains the physical intuition during the model reduction process. The theoretical results are illustrated with the aid of examples.

An integrated approach to occupancy modeling and estimation in commercial buildings

The problem of real-time estimation of occupancy in a commercial building (number of people in various zones at every time instant) is relevant to a number of emerging applications, such as green buildings that achieve high energy efficiency through feedback control. Due to the high deployment cost and large errors that people counting sensors suffer from, measuring occupancy throughout the building accurately from sensors alone is not feasible. Fusing sensor data with model predictions is essential. Due to the highly uncertain nature of occupancy dynamics, modeling and estimation of occupancy is a challenging problem. This paper makes two contributions toward addressing these challenges. We develop an agent-based model to simulate the behavior of all the occupants of a building, and extract reduced-order graphical models from Monte-Carlo simulations of the agent-based model. The agent-based model is validated with sensor data for the special case of one room and one occupant. Noisy measurements from a few sensors are fused with the graphical model predictions using the classical LMV estimator to estimate room-level occupancy in the building. Simulations illustrate the effectiveness of the proposed method.

Experimental Evaluation of Frequency Regulation From Commercial Building HVAC Systems

Automated demand response can be a valuable resource for ancillary services in the power grid. This paper illustrates this value with the first experimental demonstration of frequency regulation from commercial building heating ventilation and air conditioning (HVAC) systems. The experiments were conducted in Pugh Hall, a 40000 sq. ft. commercial building located at the University of Florida. Detailed are the steps required to make this possible, including control architecture, system identification, and control design. Experiments demonstrate: that satisfactory frequency regulation service can be provided by the HVAC system without noticeable effect on the indoor climate, and the ancillary service provided by this system passes the qualification criteria for participating in the Pennsylvania-New Jersey-Maryland (PJM) interconnections frequency regulation market.

Ancillary Service to the Grid Using Intelligent Deferrable Loads

Renewable energy sources such as wind and solar power have a high degree of unpredictability and time-variation, which makes balancing demand and supply challenging. One possible way to address this challenge is to harness the inherent flexibility in demand of many types of loads. Introduced in this paper is a technique for decentralized control for automated demand response that can be used by grid operators as ancillary service for maintaining demand-supply balance. A randomized control architecture is proposed, motivated by the need for decentralized decision making, and the need to avoid synchronization that can lead to large and detrimental spikes in demand. An aggregate model for a large number of loads is then developed by examining the mean field limit. A key innovation is a linear time-invariant (LTI) system approximation of the aggregate nonlinear model, with a scalar signal as the input and a measure of the aggregate demand as the output. This makes the approximation particularly convenient for control design at the grid level.

Estimating DoA From Radio-Frequency RSSI Measurements Using an Actuated Reflector

We describe a proof-of-concept device and method to estimate the direction-of-arrival (DoA) of a radio signal by a receiver that is suitable for wireless sensor network (WSN) applications. The device estimates the DoA by identifying the peak of received signal strength indicator (RSSI) measurements using an actuated parabolic reflector. Automatic localization is an important requirement for deployment of wireless sensor networks. Localization from distance measurement alone is a challenging problem, but becomes easier if relative angular position measurements between pairs of radio nodes are available. Methods of DoA estimation such as phased arrays are unsuitable for wireless sensor networks due to size, cost and complexity limitations. The device and the algorithm we describe is compact and simple enough to be suitable for WSNs. Experimental results show that the error in the measured DoA has a mean smaller than 4° and standard deviation of smaller than 8°, in both indoor and outdoor environments in line of sight situations. Presence of moving objects in the vicinity of the transceivers seem to have an adverse effect of the measurement accuracy.

Ancillary service to the grid from deferrable loads: The case for intelligent pool pumps in Florida

Renewable energy sources such as wind and solar power have a high degree of unpredictability and time-variation, which makes balancing demand and supply challenging. One possible way to address this challenge is to harness the inherent flexibility in demand of many types of loads. We focus on pool pumps, and how they can be used to provide ancillary service to the grid for maintaining demand-supply balance. A Markovian Decision Process (MDP) model is introduced for an individual pool pump. A randomized control architecture is proposed, motivated by the need for decentralized decision making, and the need to avoid synchronization that can lead to large and detrimental spikes in demand. An aggregate model for a large number of pools is then developed by examining the mean field limit. A key innovation is an LTI-system approximation of the aggregate nonlinear model, with a scalar signal as the input and a measure of the aggregate demand as the output. This makes the approximation particularly convenient for control design at the grid level. Simulations are provided to illustrate the accuracy of the approximations and effectiveness of the proposed control approach.