Abstract
We consider a system where inelastic demand for electric power is met from three sources: 1) the grid; 2) in-house renewables such as solar panels; and 3) an in-house energy storage device. In our setting, energy demand, renewable power supply, and cost for grid power are all time-varying and stochastic. Furthermore, there are limits and inefficiency associated with charging and discharging the energy storage device. We formulate the storage operation problem as a dynamic program with parameters estimated from real-world demand, supply, and cost data. As the dynamic program is computationally intensive for large-scale problems, we explore algorithms based on approximate dynamic programming (ADP) and apply them to a test data set. Using the real-world test data, we numerically compare the performance of two ADP-based algorithms against Lyapunov optimization-based algorithms that require no statistical knowledge. Our results ascertain the value of storage and the value of installing a renewable source.
Original language | English |
---|---|
Pages (from-to) | 1619-1629 |
Number of pages | 11 |
Journal | IEEE Transactions on Smart Grid |
Volume | 8 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2017 Jul |
Bibliographical note
Funding Information:Manuscript received March 24, 2015; revised July 12, 2015 and September 7, 2015; accepted October 9, 2015. Date of publication November 9, 2015; date of current version June 19, 2017. This work was supported in part by the Air Force Office of Scientific Research under Contract FA9550-13-1-0008, and in part by the Massachusetts Institute of Technology-Singapore University of Technology and Design International Design Center under Grant IDG21400103. A preliminary version of this paper appeared in the Proceedings of the 5th IEEE International Conference on Smart Grid Communications (SmartGridComm 2014). Paper no. TSG-00340-2015. (Corresponding author: Yunjian Xu.) S. Kwon and N. Gautam are with the Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843-3131 USA (e-mail: soongeol@email.tamu.edu; gautam@tamu.edu).
Publisher Copyright:
© 2010-2012 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science(all)