Meeting Inelastic Demand in Systems with Storage and Renewable Sources

Soongeol Kwon, Yunjian Xu, Natarajan Gautam

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29 Citations (Scopus)


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 languageEnglish
Pages (from-to)1619-1629
Number of pages11
JournalIEEE Transactions on Smart Grid
Issue number4
Publication statusPublished - 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:;

Publisher Copyright:
© 2010-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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