Visible to the public Architecture and Distributed Management for Reliable Mega-scale Smart Grids


The objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications, similar to the way users connect to the Internet. The approach is to view the overall grid management as an adaptive optimizer to iteratively solve a system-wide optimization problem, where networked sensing, control and verification carry out distributed computation tasks to achieve reliability at all levels, particularly component-level, system-level, and application level.

Under the common theme of reliability guarantees, distributed monitoring and inference algorithms are being developed to perform fault diagnosis and operate resiliently against all hazards. PMU measurements from multiple locations, are used for learning, characterizing and classifying event-specific spatial signatures, and probabilistic models are developed to subsume measurement data. To attain high reliability, a trustworthy middleware tailored towards smart grid design, is being studied to shield the grid design from the complexities of the underlying software world, using automatic generation of invariants for software validation. Further, the PIs are investigating realistic/tractable models for wind generation forecast, and leveraging them to devise efficient algorithms for demand response and adaptive reserve requirements.

Another major effort of this project is on Internet-scale data centers (IDCs) which have rapidly proliferated to such an extent that their energy consumption and GreenHouse Gas (GHG) emissions have become an important concern to society. As a result, many IDC operators have started using renewable energy, e.g., wind power, to power their data centers. Unfortunately, the utilization of wind energy has stayed at a low ratio due to the intermittent nature of wind. We make the case that it is in fact possible for a distributed IDC system to exploit multiple uncorrelated wind energy sources to significantly reduce the effect of intermittency and nearly achieve "entirely green" cloud- scale services. This result is obtained based on the analysis of real-world wind power traces from 69 wind farms. The idea is to leverage the front-end load dispatching server to send work to the location where wind power is available. We propose a wind-power- aware (WPA) policy that routes jobs based only on the current states of workloads and wind power availabilities in the data centers. We show that with the WPA policy more than 95% of energy consumption in IDCs can in fact be satisfied by wind power, and, secondly, that achieving this does not require the delaying processing of jobs due to wind availability. We also show that the locations where data centers are placed play an important role in achieving high wind power utilization. Our analysis shows that wind power utilization can generally lie in a range from 44% to 96%, depending on how locations of wind farms are selected. We propose a method for location selection that uses the coefficient of variation (CV) instead of the correlation coefficient, and show that with this method the utilization can lie in the high end of the above range. Finally, we verify these results by simulations that are based on real-world traces for both workloads and wind power generations.

Award Number: 1035906

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