AI Intelligence for the Grid 16 Years Later: Progress, Challenges and Lessons for Other Sectors

How could the “new AI” based on neural networks and deep learning be applied to the electric power grid, so as to get maximum benefit from the new technology, and serve as a model for how to organize the new Internet of Things (IOT) in general? The first of these questions was already assessed in great detail in workshops organized jointly by NSF and the Electric Power Research Institute (EPRI) in 2002 [1], drawing on new technologies which included today's deep learning but also more advanced technologies in the same family [2]. The NSTC (White House) Smart Grid policy of June 2011 cited [1] in stating: “NSF is currently supporting research to develop a '4th generation intelligent grid' that would use intelligent system-wide optimization to allow up to 80% of electricity to come from renewable sources and 80% of cars to be pluggable electric vehicles (PEV) without compromising reliability, and at minimum cost to the Nation.” This paper gives some highlights of the progress made, the open challenges, and important connections to the larger needs of humanity, in that order. The synergy between new intelligence, new technology for cybersecurity [3] and new physical hardware [4] is essential to maximum success, and even to the very survival of our endangered species. Lessons from the power grid are essential to better understanding of urgent challenges central to the IOT in general [5].

[1]  K. Deutsch.  The nerves of government : models of political communication and control , 1965 .

[2]  Gregory B. White,et al.  Secure Computers and Networks: Analysis, Design, and Implementation , 2000 .

[3]  Brian Stuart.  Principles of Operating Systems: Design and Applications , 2008 .

[4]  Brian L. Stuart.  Principles of Operating Systems , 2008 .

[5]  Paul J. Werbos,et al.  2009 Special Issue: Intelligence in the brain: A theory of how it works and how to build it , 2009 .

[6]  Paul J. Werbos,et al.  Putting more brain-like intelligence into the electric power grid: What we need and how to do it , 2009, 2009 International Joint Conference on Neural Networks.

[7]  Paul J. Werbos,et al.  Computational Intelligence for the Smart Grid-History, Challenges, and Opportunities , 2011, IEEE Computational Intelligence Magazine.

[8]  Paul J. Werbos,et al.  Neural networks and the experience and cultivation of mind , 2012, Neural Networks.

[9]  L. J. Camp,et al.  Protecting the Internet from Dictators: Technical and Policy Solutions to Ensure Online Freedoms , 2012 .

[10]  Frank L. Lewis,et al.  Reinforcement Learning and Approximate Dynamic Programming for Feedback Control , 2012 .

[11]  Shuhui Li,et al.  Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Paul J. Werbos,et al.  Example of lumped parameter modeling of a quantum optics circuit , 2013, Sensing Technologies + Applications.

[13]  Paul J. Werbos,et al.  Analog quantum computing (AQC) and the need for time-symmetric physics , 2015, Quantum Information Processing.

[14]  Haibo He,et al.  Cyber-physical attacks and defences in the smart grid: a survey , 2016, IET Cyper-Phys. Syst.: Theory & Appl..

[15]  J Voas,et al.  Whatever Happened to Formal Methods for Security? , 2016, Computer.

[16]  Jian-Wei Pan,et al.  10-Qubit Entanglement and Parallel Logic Operations with a Superconducting Circuit. , 2017, Physical review letters.

[17]  Paul J. Werbos,et al.  The New AI , 2019, Artificial Intelligence in the Age of Neural Networks and Brain Computing.