Adaptive Scheduling in a Distributed Cyber-Physical System: A case study on Future Power Grids

Author

Ashish Choudhari, Southern Illinois University CarbondaleFollow

Date of Award

12-1-2015

Degree Name

Doctor of Philosophy

Department

Electrical and Computer Engineering

First Advisor

Weng, Ning

Abstract

Cyber-physical systems (CPS) are systems that are composed of physical and computational components. CPS components are typically interconnected through a communication network that allows components to interact and take automated actions that are beneficial for the overall CPS. Future Power-Grid is one of the major example of Cyber-physical systems. Traditionally, Power-Grids use a centralized approach to manage the energy produced at power sources or large power plants. Due to the advancement and availability of renewable energy sources such as wind farms and solar systems, there are more number of energy sources connecting to the power grid. Managing these large number of energy sources using a centralized technique is not practical and is computationally very expensive. Therefore, a decentralized way of monitoring and scheduling of energy across the power grid is preferred. In a decentralized approach, computational load is distributed among the grid entities that are interconnected through a readily available communication network like internet. The communication network allows the grid entities to coordinate and exchange their power state information with each other and take automated actions that lead to efficient consumption of energy as well as the network bandwidth. Thus, the future power grid is appropriately called a "Smart-Grid". While Smart-Grids provide efficient energy operations, they also impose several challenges in the design, verification and monitoring phases. The computer network serves as a backbone for scheduling messages between the Smart-Grid entities. Therefore, network delays experienced by messages play a vital role in grid stability and overall system performance. In this work, we study the effects of network delays on Smart-Grid performance and propose adaptive algorithms to efficiently schedule messages between the grid entities. Algorithms proposed in this work also ensure the grid stability and perform network congestion control. Through this work, we derive useful conclusions regarding the Smart-Grid performance and find new challenges that can serve as future research directions in this domain.