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.
Original language | American English |
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Number of pages | 11 |
State | Published - 2017 |
Event | Hawaii International Conference on System Sciences - Waikoloa, Hawaii Duration: 3 Jan 2018 → 6 Jan 2018 |
Conference
Conference | Hawaii International Conference on System Sciences |
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City | Waikoloa, Hawaii |
Period | 3/01/18 → 6/01/18 |
Bibliographical note
See NREL/CP-5D00-80521 for paper as published in proceedingsNREL Publication Number
- NREL/CP-5D00-68712
Keywords
- autonomous energy grids
- big data analytics
- complex system theory
- control theory
- microgrid
- optimization
- renewable integration
- smart grids