PhD Defense by Paritosh Ramanan

Event Details
  • Date/Time:
    • Thursday October 8, 2020
      8:30 am - 10:30 am
  • Location: REMOTE: BLUE JEANS
  • Phone:
  • URL: BlueJeans Link
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  • Fee(s):
    N/A
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Summaries

Summary Sentence: Decentralized Optimization and Analytics for Large Scale Power System Problems

Full Summary: No summary paragraph submitted.

Thesis Title: 

Decentralized Optimization and Analytics for Large Scale Power System Problems

 

Advisors:

Dr. Nagi Gebraeel, School of Industrial and Systems Engineering,

Georgia Tech

Dr. Edmond Chow, School of Computational Science and Engineering,

Georgia Tech

 

Committee members:

Dr. Santanu Dey, School of Industrial and Systems Engineering, Georgia

Tech

Dr. Andy Sun, School of Industrial and Systems Engineering, Georgia

Tech

Dr. Michael Rabbat, Facebook AI Research, Montreal

 

Date: Thursday, October 8th, 2020

 

Time: 830am - 10am, EST

 

Meeting URL (for BlueJeans):https://bluejeans.com/520087903

 

Meeting ID (for BlueJeans):520

087 903

 

Abstract:

Large scale power networks form the backbone of the global energy infrastructure. Power system optimization problems aim at better utilization

of system resources and form a significant part of power systems research. However, large scale planning and optimization problems demand efficient computational schemes that respect the data privacy of asset owners and operators as well. As a result, decentralized

computational paradigms for large scale planning problems in power systems are gaining popularity. 

 

In this thesis, we explore novel ways to solve computationally challenging planning and analytics problems in a decentralized manner using

synchronous as well as asynchronous computational models. We specifically focus on decentralized formulations of unit commitment, joint operations and maintenance, differential privacy based unit commitment and maintenance as well as a blockchain based, decentralized

analytics methodology for replay attack detection. 

 

In Chapter 2, we establish a synchronous solution mechanism to the decentralized sensor driven optimization problem that provides a comparable

solution to the centralized method. In Chapter 3 we tackle the more fundamental decentralized UC (DUC) problem and explore an asynchronous solution for its computational benefit. We show that the asynchronous approach presented in Chapter 3 potentially leads

to faster solution times showing good computational promise.

 

In Chapter 4, we strengthen our solution to the DUC problem with the help of privacy preserving valid inequalities that are computed asynchronously

leading to a significant improvement in solution quality. We introduce an interleaved binary approach to our asynchronous method that improves convergence times for the asynchronous solution drastically. 

 

In Chapter 5, we present our work that is driven by a subgradient based solution to the integrated sensor driven, decentralized maintenance

and UC problem for planning horizons spanning many months. In this approach, by dualizing the maintenance limit constraint that is coupled across weeks, we parallelize weekly operations locally with the help of the subgradient method. We orchestrate multithreading

for every subproblem while employing the distributed memory paradigm to communicate among regions.

 

In Chapter 6 we propose a differential privacy driven approach geared towards decentralized formulations of mixed integer operations and maintenance

optimization problems that protects network flow estimates. We prove strong privacy guarantees by leveraging the linear relationship between the phase angles and the flow. To address the challenges associated with the mixed integer and dynamic nature of the

problem, we introduce an exponential moving average based consensus mechanism to enhance convergence, coupled with a control chart based convergence criteria to improve stability. 

 

In Chapter 7, we propose a blockchain based decentralized framework for detecting coordinated replay attacks with full data privacy. We develop

a Bayesian inference mechanism employing locally reported attack probabilities that is tailor made for a blockchain framework. We compare our framework to a traditional decentralized algorithm based on the broadcast gossip framework both theoretically as well

as empirically. With the help of experiments on a private Ethereum blockchain, we show that our approach achieves good detection quality and significantly outperforms gossip driven approaches in terms of accuracy, timeliness and scalability

Additional Information

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Graduate Studies

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Public, Graduate students
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Phd Defense
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Sep 30, 2020 - 12:52pm
  • Last Updated: Sep 30, 2020 - 12:52pm