My research group studies the foundations of Computer Networks and Cyber-Physical Systems. We apply tools and techniques from optimization, control theory, queueing theory, probability theory and stochastic processes to design, operate, and analyze performance of these systems.
Distributed energy resources are traditionally operated by simple rule-based controllers. These controllers are myopic and suboptimal. We explore how learning-based control of these resources can improve their performance, increase the revenue generated, and minimize detrimental impacts on the power grid. We develop adaptive and distributed control algorithms that can run on edge devices and compare their performance with various model-based and model-free control algorithms.
Power distribution networks have a sparse topology which is usually unknown to system operators. These networks are currently monitored only at a small number of points beyond the substation for cost reasons. In this project we leverage the sparsity of the distribution networks along with the available sensor measurements and pseudo-measurements to (a) identify the network topology and model parameters, (b) estimate the state, (c) identify and pinpoint events to a small part of the network, and (d) monitor harmonic generation and propagation.
Energy systems are becoming increasingly complex with dynamics that span multiple timescales. Designing and validating new optimization and control algorithms for the smart grid require simulation tools that accurately model the dynamics of the physical system and its interactions with various other domains, such as communication networks, building subsystems, transport systems, and electricity markets. In this project, we build a co-simulation platform that meets these requirements. We develop several distributed control and state estimation algorithms in addition to cyber security applications on top of this simulator. We use concepts from operating systems and software engineering to design a modular, efficient, and secure platform.
Commercial buildings are large-scale distributed systems containing hundreds of sensors and actuators, yet they are largely unaware of the presence and actions of their occupants. For example, commercial building HVAC systems traditionally run on a static heating/cooling schedule that does not take occupancy into account, wasting a lot of energy in conditioning empty or partially-occupied spaces. We investigate how data from various wired and wireless sensors (including the sensors that communicate their measurements to the Building Management System) can be fused to estimate the number of occupants and localize them within the building. Additionally, we study how this approximate knowledge of occupancy at the level of individual rooms can be incorporated in the energy-efficient operation of building subsystems.
IoT systems are typically equipped with a myriad of sensors to monitor the environment and understand user activity. The sheer amount of data collected by these sensors poses serious threats to our privacy. For example, building occupancy can be inferred from data collected by smart thermostats, people's health and wellness can be inferred from conversations recorded by smart home personal assistants, and their age and gender can be determined by a fitness tracking application running on their smartphone. To address these privacy threats, we develop algorithms to remove private and sensitive information (discrete or continuous) from sensor data to prevent unwanted inferences while minimizing distortion of useful data. These techniques enable publication and sharing of sensor data with third party applications without compromising our privacy.
We designed a multi-resolution search algorithm for time series data analytics. Leveraging statistical summaries of data offered by BTrDB for a desired temporal resolution, this algorithm is capable of efficiently identifying rare, critical events across a wide range of temporal scales (micro-seconds to years) in vast amounts of telemetry data. This algorithm has been used to detect tap operations, voltage sags, and switching events as a part of the ARPA-E micro-synchrophasors for distribution systems project.
We designed an efficient algorithm for solving the inverse Power Flow (iPF) problem in a three phase distribution network equipped with phasor measurement units. This algorithm is capable of inferring network topology, i.e., the admittance matrix, and identifying switching operations from voltage and current phasor measurements in cases with and without hidden states.
Variable-power distributed energy resources, such as solar photovoltaic and storage systems, and high-power elastic loads, such as electric vehicle chargers, are being installed at a phenomenal rate in power distribution systems. Such active end-nodes can affect the reliable operation of the grid if they are not controlled properly. The joint control of active end-nodes in quasi real-time based on fast timescale measurements could enable operators to meet their efficiency and fairness requirements, and enhance service reliability by preventing network overloads, reverse flows, and voltage deviations beyond operating limits.
Uncoordinated charging of EVs may overload distribution transformers and branches. Assuming that EV chargers obey control signals sent from the grid, and support variable-rate charging, we propose a distributed control algorithm which adapts the charging rate of EVs to the available capacity of the distribution network in real-time. This algorithm is stable and yields a proportionally fair charging rate allocation. This algorithm is similar to the congestion control algorithms used in packet-switched networks.
We investigate the problem of identifying energy consumption profiles for residential consumers so that they can be used to produce accurate, personalized and thus effective feedback. Since residential energy consumption is driven by a wide range of factors such as routine, occupancy, activity and weather, we pursue a modelling approach to formulate energy profiles that capture these factors. Categorizing consumers based on these profiles can provide valuable insight into their relative energy consumption and forms the foundation for comparative feedback applications.
We developed an application for Microsoft HomeOS which detects abnormal household electricity consumption patterns and subsequently informs homeowners by sending an email or a text message. This application communicates with a Current Cost Envi device to monitor the aggregate power consumption of a home.
We used teletraffic theory to size power distribution networks just as it is used to size telecommunication access networks. Specifically, we proved the equivalence between a model of a distribution branch comprised of a transformer and storage that we want to size for a given underflow probability ε, and a queuing model that we want to size for a given overflow probability ε. Based on this equivalence, we showed how existing teletraffic analysis can be applied to jointly size transformers and storage.
We developed a testbed to obtain fine-grained measurements of active power consumption of homes. This testbed is comprised of 30 measurement nodes; each node has an Envi device to measure the instantaneous power consumption and a netbook to collect and send the measurements to our server.
We developed a testbed to monitor temperature, humidity, sound, light, and air pressure at a fast timescale (every six seconds). We deployed 24 wired Weatherduck sensors in graduate students' offices and labs in Davis Centre at the University of Waterloo. These measurements are used to infer room occupancy and reduce energy consumption of AC and electric heater when the room is unoccupied.
I am grateful for the support of the following funding agencies and corporate sponsors: