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Title: Autonomous Energy Grids: Preprint

Abstract

With much higher levels of distributed energy resources - variable generation, energy storage, and controllable loads just to mention a few - being deployed into power systems, the data deluge from pervasive metering of energy grids, and the shaping of multi-level ancillary-service markets, current frameworks to monitoring, controlling, and optimizing large-scale energy systems are becoming increasingly inadequate. This position paper outlines the concept of 'Autonomous Energy Grids' (AEGs) - systems that are supported by a scalable, reconfigurable, and self-organizing information and control infrastructure, can be extremely secure and resilient (self-healing), and self-optimize themselves in real-time for economic and reliable performance while systematically integrating energy in all forms. AEGs rely on scalable, self-configuring cellular building blocks that ensure that each 'cell' can self-optimize when isolated from a larger grid as well as partaking in the optimal operation of a larger grid when interconnected. To realize this vision, this paper describes the concepts and key research directions in the broad domains of optimization theory, control theory, big-data analytics, and complex system modeling that will be necessary to realize the AEG vision.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1399662
Report Number(s):
NREL/CP-5D00-68712
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: To be presented at the Hawaii International Conference on System Sciences, 3-6 January 2018, Waikoloa, Hawaii
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; autonomous energy grids; smart grids; microgrid; control theory; optimization; big data analytics; complex system theory; renewable integration

Citation Formats

Kroposki, Benjamin D, Dall-Anese, Emiliano, Bernstein, Andrey, Zhang, Yingchen, and Hodge, Brian S. Autonomous Energy Grids: Preprint. United States: N. p., 2017. Web.
Kroposki, Benjamin D, Dall-Anese, Emiliano, Bernstein, Andrey, Zhang, Yingchen, & Hodge, Brian S. Autonomous Energy Grids: Preprint. United States.
Kroposki, Benjamin D, Dall-Anese, Emiliano, Bernstein, Andrey, Zhang, Yingchen, and Hodge, Brian S. Wed . "Autonomous Energy Grids: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1399662.
@article{osti_1399662,
title = {Autonomous Energy Grids: Preprint},
author = {Kroposki, Benjamin D and Dall-Anese, Emiliano and Bernstein, Andrey and Zhang, Yingchen and Hodge, Brian S},
abstractNote = {With much higher levels of distributed energy resources - variable generation, energy storage, and controllable loads just to mention a few - being deployed into power systems, the data deluge from pervasive metering of energy grids, and the shaping of multi-level ancillary-service markets, current frameworks to monitoring, controlling, and optimizing large-scale energy systems are becoming increasingly inadequate. This position paper outlines the concept of 'Autonomous Energy Grids' (AEGs) - systems that are supported by a scalable, reconfigurable, and self-organizing information and control infrastructure, can be extremely secure and resilient (self-healing), and self-optimize themselves in real-time for economic and reliable performance while systematically integrating energy in all forms. AEGs rely on scalable, self-configuring cellular building blocks that ensure that each 'cell' can self-optimize when isolated from a larger grid as well as partaking in the optimal operation of a larger grid when interconnected. To realize this vision, this paper describes the concepts and key research directions in the broad domains of optimization theory, control theory, big-data analytics, and complex system modeling that will be necessary to realize the AEG vision.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Oct 04 00:00:00 EDT 2017},
month = {Wed Oct 04 00:00:00 EDT 2017}
}

Conference:
Other availability
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