Autonomous Energy Grids: Preprint

Benjamin Kroposki, Emiliano Dall-Anese, Andrey Bernstein, Yingchen Zhang, Brian Hodge

Research output: Contribution to conferencePaper


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.
Original languageAmerican English
Number of pages11
StatePublished - 2017
EventHawaii International Conference on System Sciences - Waikoloa, Hawaii
Duration: 3 Jan 20186 Jan 2018


ConferenceHawaii International Conference on System Sciences
CityWaikoloa, Hawaii

Bibliographical note

See NREL/CP-5D00-80521 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5D00-68712


  • autonomous energy grids
  • big data analytics
  • complex system theory
  • control theory
  • microgrid
  • optimization
  • renewable integration
  • smart grids


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