TY - JOUR
T1 - A Stochastic Optimal Control Approach for Exploring Tradeoffs between Cost Savings and Battery Aging in Datacenter Demand Response
AU - Mamun, Abdullah Al
AU - Narayanan, Iyswarya
AU - Wang, Di
AU - Sivasubramaniam, Anand
AU - Fathy, Hosam K.
N1 - Funding Information:
This work was supported by the National Science Foundation through the Project CSR: medium: Provisioning and Harnessing Energy Storage for Datacenter Demand Response under Grant CNS-1302225.
Funding Information:
Manuscript received June 22, 2016; revised September 26, 2016; accepted December 3, 2016. Date of publication January 10, 2017; date of current version December 14, 2017. Manuscript received in final form December 14, 2016. This work was supported by the National Science Foundation through the Project CSR: medium: Provisioning and Harnessing Energy Storage for Datacenter Demand Response under Grant CNS-1302225. Recommended by Associate Editor T. Parisini. (Corresponding author: Hosam K. Fathy.) A. Mamun and H. K. Fathy are with the Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802 USA (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/1
Y1 - 2018/1
N2 - This brief paper optimizes power management for datacenters employing lithium-ion battery storage, with the specific goal of addressing the tradeoff between: 1) the cost saving achievable through the peak demand shaving and 2) the corresponding battery aging. To the best of the authors' knowledge, this tradeoff has never been addressed using physics-based models of battery performance and degradation combined with stochastic models of datacenter demand. We build: 1) a Markov chain model of datacenter power demand; 2) a second-order model of battery diffusion/reaction dynamics; and 3) a physics-based model of battery aging via solid electrolyte interphase growth. Together, these models enable the solution of the battery health-conscious demand response problem via stochastic dynamic programming (SDP). A penalty function is used for enforcing a datacenter "power cap" within this SDP problem. By varying this power cap, we traverse the Pareto tradeoff between the cost savings due to demand response and battery health degradation.
AB - This brief paper optimizes power management for datacenters employing lithium-ion battery storage, with the specific goal of addressing the tradeoff between: 1) the cost saving achievable through the peak demand shaving and 2) the corresponding battery aging. To the best of the authors' knowledge, this tradeoff has never been addressed using physics-based models of battery performance and degradation combined with stochastic models of datacenter demand. We build: 1) a Markov chain model of datacenter power demand; 2) a second-order model of battery diffusion/reaction dynamics; and 3) a physics-based model of battery aging via solid electrolyte interphase growth. Together, these models enable the solution of the battery health-conscious demand response problem via stochastic dynamic programming (SDP). A penalty function is used for enforcing a datacenter "power cap" within this SDP problem. By varying this power cap, we traverse the Pareto tradeoff between the cost savings due to demand response and battery health degradation.
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U2 - 10.1109/TCST.2016.2643569
DO - 10.1109/TCST.2016.2643569
M3 - Article
AN - SCOPUS:85009882067
SN - 1063-6536
VL - 26
SP - 360
EP - 367
JO - IEEE Transactions on Control Systems Technology
JF - IEEE Transactions on Control Systems Technology
IS - 1
M1 - 7812654
ER -
