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 - Funding Information:
This work is supported by National Science Foundation grant CNS-1302225, CSR: medium: Provisioning and Harnessing Energy Storage for Datacenter Demand Response. The authors gratefully acknowledge this support. The authors would also like to thank Kushagra Vaid from Cloud Server Infrastructure team in Microsoft for providing datacenter power traces
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.
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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 -
