DECENTRALIZED OPTIMIZATION AND ANALYTICS FOR LARGE SCALE POWER SYSTEM PROBLEMS

Abstract

Large scale power networks form the backbone of the global energy infrastructure. Power system optimization problems are geared towards large scale planning problems in power systems. The solutions to these problems offer better utilization of system resources and therefore such problems 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.

Decentralized methods have lately emerged as a means to tackle the different operational issues like data privacy and computational efficiency. Decentralized methods localize the problem and data component in a multi agent system like the power grid. Therefore, decentralized approaches towards planning problems in power systems could be an attractive way for utilities to derive globally optimal solutions without divulging their local data in a computationally efficient fashion. 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.