Control and Automation for Sustainability, Energy, and Environment (CASE2)

A sustainable future requires intelligent use of resources. Main sources of energy consumption, such as buildings and transportation, need to be made more efficient without making them more expensive. Green but volatile sources of energy generation, such as solar and wind, have to be coordinated with flexible demand, batteries, and conventional generators. This complex networked system has to be reliable, secure, and resilient to failures. We are developing the decision making algorithms, design tools, and IoT systems needed to transform this vision into reality.

Prof. Prabir Barooah

Prabir Barooah, 2022

Electronics and Electrical Engineering
Indian Institute of Technology, Guwahati

Prabir Barooah joined the Indian Institute of Technology, Guwahati, in July 2022. Previously he was a Professor of Mechanical and Aerospace Engineering at the University of Florida, Gainesville, Florida, USA. He received his Ph.D. in 2007 from the University of California, Santa Barbara. From 1999 to 2002 he was a research engineer at United Technologies Research Center, East Hartford, CT. He received the M. S. degree in Mechanical Engineering from the University of Delaware in 1999 and the B.Tech degree in Mechanical Engineering from the Indian Institute of Technology, Kanpur, in 1996. Dr. Barooah has won the Outstanding Researcher Award (2013) from American Society of Engineering Education (SE Section), CAREER award (2010) from the National Science Foudation (USA), General Chairs' Recognition Award for Interactive papers at the 48th IEEE Conference on Decision and Control (2009), the best paper award at the 2nd Int. Conf. on Intelligent Sensing and Information Processing (2005), and a NASA group achievement award (2003).

Research interests

  • Applications: energy sustainability, efficiency and resiliency:

    Methods for operation and planning to support renewable energy integration in power grids, demand side management, energy resiliency to disasters, energy efficiency, smart power grids with distributed energy resources, renewable energy economics and policy.
  • Theory: Data-driven learning and control, decentralized coordination:

    Algorithms for autonomous control using model-free (Artificial Intelligence) methods and model-based learning techniques, coordinated control of networked systems consisting of large number of sub-systems, such as the smart power grid.

Distributed control in smart grids

The power grid is a large interconnected system, and is increasing in complexity every day. Apart from the thousands of conventional generators, millions of smaller energy resources - solar panels, batteries, EVs, smart consumer loads - have to be controlled to ensure stable and reliable operation. The control action for each of these agents must be locally computed. How do you design control algorithms for a large networked systems with provable performance guarantee?

Energy efficiency: reducing buildings' energy use through algorithms

Buildings and their air conditioning systems consume enormous amount of energy, especially electricity, and thus are a major source of CO2 emissions. We are developing control algorithms to fix this problem: the algorithms will continuously change setpoints of heating, ventilation and air conditioning (HVAC) systems to reduce energy use while improving indoor climate.

Renewable energy: smart buildings as "virtual batteries" for absorbing solar and wind volatility

Due to the thermal inertia of buildings, the electricity demand of their HVAC systems can be varied within limits without affecting their indoor climate. From the point of view of the power grid, this variation is the same as the charging and discharging of a battery. The same game can be played with almost every electric load. The resulting VES (virtual energy storage) potential of all these loads is huge. It is also a lot cheaper than a real battery of the same size.

Youtube video on virtual energy storage from smart loads and the role of distributed control. (created by my former students at the University of Florida)