TY - GEN
T1 - Battery health-conscious online power management for stochastic datacenter demand response
AU - Mamun, Abdullah Al
AU - Narayanan, Iyswarya
AU - Wang, Di
AU - Sivasubramaniam, Anand
AU - Fathy, Hosam K.
N1 - Publisher Copyright:
© 2016 American Automatic Control Council (AACC).
PY - 2016/7/28
Y1 - 2016/7/28
N2 - This paper presents a stochastic control framework for optimizing datacenter power management. The paper focuses on datacenters employing lithium-ion batteries for demand response. The use of batteries for demand response can reduce electricity costs, at the expense of battery degradation. We minimize this degradation using a control policy that takes into account uncertainties in power demand. We perform this optimization using a second-order model of battery charge dynamics, coupled with a physics-based model of battery aging via solid-electrolyte interphase (SEI) growth. To the best of our knowledge, this is the first study that uses battery models capturing diffusion dynamics and nonlinear aging effects, together with a model of demand uncertainty, for datacenter energy management. We formulate this as a stochastic dynamic programming (SDP) problem, where uncertain power demand is modeled as a Markov chain. The resulting control policy keeps grid power within a predefined range while minimizing battery degradation.
AB - This paper presents a stochastic control framework for optimizing datacenter power management. The paper focuses on datacenters employing lithium-ion batteries for demand response. The use of batteries for demand response can reduce electricity costs, at the expense of battery degradation. We minimize this degradation using a control policy that takes into account uncertainties in power demand. We perform this optimization using a second-order model of battery charge dynamics, coupled with a physics-based model of battery aging via solid-electrolyte interphase (SEI) growth. To the best of our knowledge, this is the first study that uses battery models capturing diffusion dynamics and nonlinear aging effects, together with a model of demand uncertainty, for datacenter energy management. We formulate this as a stochastic dynamic programming (SDP) problem, where uncertain power demand is modeled as a Markov chain. The resulting control policy keeps grid power within a predefined range while minimizing battery degradation.
UR - http://www.scopus.com/inward/record.url?scp=84992152225&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992152225&partnerID=8YFLogxK
U2 - 10.1109/ACC.2016.7525411
DO - 10.1109/ACC.2016.7525411
M3 - Conference contribution
AN - SCOPUS:84992152225
T3 - Proceedings of the American Control Conference
SP - 3206
EP - 3211
BT - 2016 American Control Conference, ACC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 American Control Conference, ACC 2016
Y2 - 6 July 2016 through 8 July 2016
ER -
