Statistical-Physics inspired approaches for combinatorial optimization; and enabling the grid of the future

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Combinatorial optimization problems arise in many applications in various
forms in seemingly unrelated areas such as data compression, pattern
recognition, image segmentation, resource allocation, routing, and
scheduling, graph aggregation, and graph partition problems. These
optimization problems are largely non convex, computationally complex and
suffer from multiple local minima that riddle the cost surface. In our
work, we are motivated by solutions that are employed by nature to similar
combinatorial optimization problems; well described in terms of laws such
as minimum free energy principle in statistical physics literature. The
resulting approach is independent of initialization, fast and results in
high-quality solutions.
In the second half of my talk, I'll discuss innovative hardware and
software solutions to integrate and coordinate generation, transmission,
and end-use energy systems at various points on the electric grid. In this
context, microgrids are hypothesized as viable alternatives to the
traditional electric grid. Microgrids support a flexible and efficient
electric grid by enabling the integration of growing deployments of
distributed energy resources such as renewables like solar and wind.  We
have devised a novel robust and optimal control architecture to address
challenges in decentralized control of microgrids. These control systems
will enable real-time coordination between distributed generation, such as
rooftop and community solar assets and bulk power generation, while
proactively shaping electric load.



  Date and Time

  Location

  Hosts

  Registration



  • Date: 15 Jan 2018
  • Time: 02:30 PM to 03:30 PM
  • All times are Asia/Kolkata
  • Add_To_Calendar_icon Add Event to Calendar
  • DA 229
  • IIT KANPUR
  • KANPUR, Uttar Pradesh
  • India 208016
  • Building: ACES


  Speakers

Mr. Mayank Baranwal

Topic:

Statistical-Physics inspired approaches for combinatorial optimization; and enabling the grid of the future

Combinatorial optimization problems arise in many applications in various
forms in seemingly unrelated areas such as data compression, pattern
recognition, image segmentation, resource allocation, routing, and
scheduling, graph aggregation, and graph partition problems. These
optimization problems are largely non convex, computationally complex and
suffer from multiple local minima that riddle the cost surface. In our
work, we are motivated by solutions that are employed by nature to similar
combinatorial optimization problems; well described in terms of laws such
as minimum free energy principle in statistical physics literature. The
resulting approach is independent of initialization, fast and results in
high-quality solutions.
In the second half of my talk, I'll discuss innovative hardware and
software solutions to integrate and coordinate generation, transmission,
and end-use energy systems at various points on the electric grid. In this
context, microgrids are hypothesized as viable alternatives to the
traditional electric grid. Microgrids support a flexible and efficient
electric grid by enabling the integration of growing deployments of
distributed energy resources such as renewables like solar and wind.  We
have devised a novel robust and optimal control architecture to address
challenges in decentralized control of microgrids. These control systems
will enable real-time coordination between distributed generation, such as
rooftop and community solar assets and bulk power generation, while
proactively shaping electric load.

Biography:

Mayank Baranwal is a final year PhD student in the Department of
Mechanical Science and Engineering at the University of Illinois at
Urbana-Champaign (UIUC). He obtained his Bachelors in Mechanical
Engineering in 2011 from Indian Institute of Technology, Kanpur, and MS in
Mechanical Science and Engineering in Summer 2014 and MS in Mathematics in
Spring 2015, both from UIUC. He works on topics related to control of
power electronic systems and microgrids; and on analysis of metaheuristic
clustering techniques with applications to grid reduction, and pick-up and
delivery problem with time-windows. He was awarded the General Proficiency
Medal during his bachelors, for securing highest rank in his department.
He is also a recipient of the University of Tokyo-IIT Undergraduate
scholarship in the year 2010 and the Outstanding Teaching Assistant award
in the year 2015.

Address: University of Illinois at Urbana-Champaign (UIUC),