Modernized electrical grid automated to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity.
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CPS: Breakthrough: Securing Smart Grid by Understanding Communications Infrastructure Dependencies
Smart grid includes two interdependent infrastructures: power transmission and distribution network, and the supporting telecommunications network. Complex interactions among these infrastructures lead to new pathways for attack and failure propagation that are currently not well understood. This innovative project takes a holistic multilevel approach to understand and characterize the interdependencies between these two infrastructures, and devise mechanisms to enhance their robustness.
Submitted by Simone Silvestri on November 29th, 2017
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Distributed Asynchronous Algorithms & Software Systems For Wide-Area Monitoring of Power Systems
Submitted by Aranya Chakrabortty on November 29th, 2017
Our results concern using program‐slicing to model software components in a CPS, with applications to verification of aviation software.
Submitted by Jason Rife on November 29th, 2017
The confluence of two powerful global trends -- the rapid growth of cloud computing and data centers
with skyrocketing energy consumption, and the accelerating penetration of renewable energy sources --
Submitted by Junshan Zhang on November 29th, 2017
Submitted by Zhenhua Liu on November 29th, 2017
The electric power grid is a complex cyber-physical system (CPS) that forms the lifeline of modern society. Cybersecurity and resiliency of the power grid is of paramount importance to national security and economic well-being. CPS security testbeds are enabling technologies that provide realistic experimental platforms for the evaluation and validation of security technologies within controlled environments, and they also enable the exploration of robust security solutions.
Submitted by Manimaran Govindarasu on November 29th, 2017
Submitted by Justin Pearson on November 29th, 2017
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CPS: Synergy: Certifiable, Attack-resilient Submodular Control Framework for Smart Grid Stability
ABSTRACT
Submitted by Linda Bushnell on November 28th, 2017
Submitted by Husheng Li on November 28th, 2017
The confluence of two powerful global trends, (1) the rapid growth of cloud computing and data centers with skyrocketing energy consumption, and (2) the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. The fast growing renewable generation puts forth great operational challenges since they will cause large, frequent, and random fluctuations in supply. Data centers, on the other hand, offer large flexible loads in the grid. Leveraging this flexibility, this project will develop fundamental theories and algorithms for sustainable data centers with a dual goal of improving data center energy efficiency and accelerating the integration of renewables in the grid via data center demand response (DR) and workload management. Specifically, the research findings will shed light on data center demand response while maintaining their performance, which will help data centers to decide how to participate in power market programs. Further, the success of data center demand response will help increase renewable energy integration and reduce the carbon footprint of data centers, contributing to global sustainability. The PIs will leverage fruitful collaboration to eventually bring the research to bear on ongoing industry standardization and development efforts. The PIs teach courses spanning networks, games, smart grid and optimization, and are strongly committed to promoting diversity by providing research opportunities to underrepresented students.
Built on the PIs expertise on data centers and the smart grid, this project takes an interdisciplinary approach to develop fundamental theories and algorithms for sustainable data centers. The research tasks are organized under two well-coordinated thrusts, namely agile data center DR and adaptive workload management. The strategies and decisions of data center DR will be made based on the workload management algorithms that balance quality of service and energy efficiency and determine the supply functions. The workload management algorithms will optimize quality of service under the electric load constraints imposed by DR accordingly. This project will make three unique contributions: (1) new market programs with strategic participation of data centers in DR, instead of passive price takers, (2) fundamental understanding of the impacts of power network constraints on data center DR and new distributed algorithms for solving optimal power flow with stochastic renewable supplies, and (3) high-performance dynamic server provisioning and load balancing algorithms for large scale data centers under time-varying and stochastic electric load constraints and on-site renewable generation.
Award Number
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Institution
California Institute of Technology
Lead PI
Steven Low
Performance Period
-
Sponsor
National Science Foundation
Sponsor Award URL
Submitted by Steven Low on October 3rd, 2017