Multi-objective optimization to minimize battery degradation and electricity cost for demand response in datacenters

Abdullah Al Mamun, Iyswarya Narayanan, Di Wang, Anand Sivasubramaniam, Hosam Kadry Fathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper presents a Lithium-ion battery control framework to achieve minimum health degradation and electricity cost when batteries are used for datacenter demand response (DR). Demand response in datacenters refers to the adjustment of demand for grid electricity to minimize electricity cost. Utilizing batteries for demand response will reduce the electricity cost but might accelerate health degradation. This tradeoff makes battery control for demand response a multiobjective optimization problem. Current research focuses only on minimizing the cost of demand response and does not capture battery transient and degradation dynamics. We address this multi-objective optimization problem using a second-order equivalent circuit model and an empirical capacity fade model of Lithium-ion batteries. To the best of our knowledge, this is the first study to use a nonlinear Lithium-ion battery and health degradation model for health-aware optimal control in the context of datacenters. The optimization problem is solved using a differential evolution (DE) algorithm and repeated for different battery pack sizes. Simulation results furnish a Pareto front that makes it possible to examine tradeoffs between the two optimization objectives and size the battery pack accordingly.

Original languageEnglish (US)
Title of host publicationDiagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791857250
DOIs
StatePublished - Jan 1 2015
EventASME 2015 Dynamic Systems and Control Conference, DSCC 2015 - Columbus, United States
Duration: Oct 28 2015Oct 30 2015

Publication series

NameASME 2015 Dynamic Systems and Control Conference, DSCC 2015
Volume2

Other

OtherASME 2015 Dynamic Systems and Control Conference, DSCC 2015
CountryUnited States
CityColumbus
Period10/28/1510/30/15

Fingerprint

Multiobjective optimization
Electricity
Health
Degradation
Costs
Equivalent circuits
Lithium-ion batteries

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Control and Systems Engineering

Cite this

Mamun, A. A., Narayanan, I., Wang, D., Sivasubramaniam, A., & Fathy, H. K. (2015). Multi-objective optimization to minimize battery degradation and electricity cost for demand response in datacenters. In Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2015-9812
Mamun, Abdullah Al ; Narayanan, Iyswarya ; Wang, Di ; Sivasubramaniam, Anand ; Fathy, Hosam Kadry. / Multi-objective optimization to minimize battery degradation and electricity cost for demand response in datacenters. Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. American Society of Mechanical Engineers, 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015).
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abstract = "This paper presents a Lithium-ion battery control framework to achieve minimum health degradation and electricity cost when batteries are used for datacenter demand response (DR). Demand response in datacenters refers to the adjustment of demand for grid electricity to minimize electricity cost. Utilizing batteries for demand response will reduce the electricity cost but might accelerate health degradation. This tradeoff makes battery control for demand response a multiobjective optimization problem. Current research focuses only on minimizing the cost of demand response and does not capture battery transient and degradation dynamics. We address this multi-objective optimization problem using a second-order equivalent circuit model and an empirical capacity fade model of Lithium-ion batteries. To the best of our knowledge, this is the first study to use a nonlinear Lithium-ion battery and health degradation model for health-aware optimal control in the context of datacenters. The optimization problem is solved using a differential evolution (DE) algorithm and repeated for different battery pack sizes. Simulation results furnish a Pareto front that makes it possible to examine tradeoffs between the two optimization objectives and size the battery pack accordingly.",
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Mamun, AA, Narayanan, I, Wang, D, Sivasubramaniam, A & Fathy, HK 2015, Multi-objective optimization to minimize battery degradation and electricity cost for demand response in datacenters. in Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. ASME 2015 Dynamic Systems and Control Conference, DSCC 2015, vol. 2, American Society of Mechanical Engineers, ASME 2015 Dynamic Systems and Control Conference, DSCC 2015, Columbus, United States, 10/28/15. https://doi.org/10.1115/DSCC2015-9812

Multi-objective optimization to minimize battery degradation and electricity cost for demand response in datacenters. / Mamun, Abdullah Al; Narayanan, Iyswarya; Wang, Di; Sivasubramaniam, Anand; Fathy, Hosam Kadry.

Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. American Society of Mechanical Engineers, 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Multi-objective optimization to minimize battery degradation and electricity cost for demand response in datacenters

AU - Mamun, Abdullah Al

AU - Narayanan, Iyswarya

AU - Wang, Di

AU - Sivasubramaniam, Anand

AU - Fathy, Hosam Kadry

PY - 2015/1/1

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N2 - This paper presents a Lithium-ion battery control framework to achieve minimum health degradation and electricity cost when batteries are used for datacenter demand response (DR). Demand response in datacenters refers to the adjustment of demand for grid electricity to minimize electricity cost. Utilizing batteries for demand response will reduce the electricity cost but might accelerate health degradation. This tradeoff makes battery control for demand response a multiobjective optimization problem. Current research focuses only on minimizing the cost of demand response and does not capture battery transient and degradation dynamics. We address this multi-objective optimization problem using a second-order equivalent circuit model and an empirical capacity fade model of Lithium-ion batteries. To the best of our knowledge, this is the first study to use a nonlinear Lithium-ion battery and health degradation model for health-aware optimal control in the context of datacenters. The optimization problem is solved using a differential evolution (DE) algorithm and repeated for different battery pack sizes. Simulation results furnish a Pareto front that makes it possible to examine tradeoffs between the two optimization objectives and size the battery pack accordingly.

AB - This paper presents a Lithium-ion battery control framework to achieve minimum health degradation and electricity cost when batteries are used for datacenter demand response (DR). Demand response in datacenters refers to the adjustment of demand for grid electricity to minimize electricity cost. Utilizing batteries for demand response will reduce the electricity cost but might accelerate health degradation. This tradeoff makes battery control for demand response a multiobjective optimization problem. Current research focuses only on minimizing the cost of demand response and does not capture battery transient and degradation dynamics. We address this multi-objective optimization problem using a second-order equivalent circuit model and an empirical capacity fade model of Lithium-ion batteries. To the best of our knowledge, this is the first study to use a nonlinear Lithium-ion battery and health degradation model for health-aware optimal control in the context of datacenters. The optimization problem is solved using a differential evolution (DE) algorithm and repeated for different battery pack sizes. Simulation results furnish a Pareto front that makes it possible to examine tradeoffs between the two optimization objectives and size the battery pack accordingly.

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BT - Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications

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Mamun AA, Narayanan I, Wang D, Sivasubramaniam A, Fathy HK. Multi-objective optimization to minimize battery degradation and electricity cost for demand response in datacenters. In Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. American Society of Mechanical Engineers. 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015). https://doi.org/10.1115/DSCC2015-9812