@article{5ca08f11bbbf453a8d9e780840bd52f2,
title = "Adaptive stochastic control for the smart grid",
abstract = "Approximate dynamic programming (ADP) driven adaptive stochastic control (ASC) for the Smart Grid holds the promise of providing the autonomous intelligence required to elevate the electric grid to efficiency and self-healing capabilities more comparable to the internet. To that end, we demonstrate the load and source control necessary to optimize management of distributed generation and storage within the Smart Grid.",
keywords = "Adaptive stochastic control (ASC), Smart Grid, approximate dynamic programming (ADP), control systems",
author = "Anderson, {Roger N.} and Albert Boulanger and Powell, {Warren Buckler} and Warren Scott",
note = "Funding Information: Ph.D. and M.S. degrees in civil engineering (summa cum laude) from the Massachusetts Institute of Technology (MIT), Cambridge, and received the B.S.E. degree from Princeton University. He has been a faculty member at Princeton University, Princeton, NJ, since 1981. He is the Founder and Director of CASTLE Laboratory, which was created in 1990 to reflect an expanding research program into dynamic resource manage- ment. He has been funded by the Air Force Office of Scientific Research, the National Science Foundation, the Department of Homeland Security, Lawrence Livermore National Laboratory and numerous industrial companies in freight transportation and logistics, including United Parcel Service, Schneider National and Norfolk Southern Railroad. He pioneered the first interactive optimization model for network design in freight transportation, and he developed the first real-time optimization model for the truckload industry using approximate dynamic programming. His research focuses on stochastic optimization problems arising in energy, transportation, health, and finance. He pioneered a new class of approximate dynamic programming algorithms for solving very high-dimensional stochastic dynamic programs. He coined the term Bthree curses of dimensionality,[ and introduced the concept of the postdecision state variable to eliminate the imbedded expectation. He is also working in the area of optimal learning for the efficient collection of information. He has founded Transport Dynamics, Inc., and the Princeton Transpor- tation Consulting Group. He is the author of Approximate Dynamic Programming: Solving the Curses of Dimensionality, and co-editor of Learning and Approximate Dynamic Programming: Scaling up to the Real World. He is the author/coauthor of over 160 publications. Funding Information: Manuscript received November 29, 2010; accepted January 12, 2011. Date of current version May 17, 2011. The work of R. N. Anderson and A. Boulanger is supported in part by Consolidated Edison of New York, Inc. and the Department of Energy through American Recovery and Reinvestment Act of 2009 contract E-OE0000197 by way of subaward agreement SA-SG003. The work of W. B. Powell and W. Scott is supported in part by the Air Force Office of Scientific Research, grant number FA9550-08-1-0195 and the National Science Foundation, grant CMMI-0856153. R. N. Anderson and A. Boulanger are with the Center for Computational Learning Systems, Columbia University, New York, NY 10027 USA. W. B. Powell and W. Scott are with the Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544 USA.",
year = "2011",
month = jun,
doi = "10.1109/JPROC.2011.2109671",
language = "English (US)",
volume = "99",
pages = "1098--1115",
journal = "Proceedings of the IEEE",
issn = "0018-9219",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",
}