Abstract

The smart electric power grid will evolve into a very complex adaptive system under semi-autonomous distributed control. its spatial and temporal complexity, non-convexity, non-linearity, non-stationarity, variability and uncertainties exceed the characteristics found in today's traditional power system. the distributed integration of intermittent sources of energy and plug-in electric vehicles to a smart grid further adds complexity and challenges to its modeling, control and optimization. Innovative technologies are needed to handle the growing complexity of the smart grid and stochastic bidirectional optimal power flows, to maximize the penetration of renewable energy, and to provide maximum utilization of available energy storage, especially plugin electric vehicles. Smart grids will need to be monitored continuously to maintain stability, reliability and efficiency under normal and abnormal operating conditions and disturbances. a combination of capabilities for system state prediction, dynamic stochastic power flow, system optimization, and solution checking will be necessary. the optimization and control systems for a smart-grid environment will require a computational systems thinking machine to handle the uncertainties and variability that exist. the importance and contributions of the computational intelligence field for developing the dynamic, stochastic, computational, and scalable technologies needed for sensemaking, situational awareness, control and optimization in smart grids are presented in this paper. © 2011 IEEE.

Department(s)

Electrical and Computer Engineering

Comments

National Science Foundation, Grant 0348221

International Standard Serial Number (ISSN)

1556-603X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Aug 2011

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