scholarly journals Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage

2016 ◽  
Vol 7 ◽  
pp. 258-269 ◽  
Author(s):  
A. Mamun ◽  
I. Narayanan ◽  
D. Wang ◽  
A. Sivasubramaniam ◽  
H.K. Fathy
Keyword(s):  
Lithium Ion Battery ◽  
Demand Response ◽  
Lithium Ion ◽  
Multi Objective Optimization ◽  
Battery Storage ◽  
Multi Objective

Multi-Objective Optimization to Minimize Battery Degradation and Electricity Cost for Demand Response in Datacenters

2015 ◽  
Author(s):  
Abdullah-Al Mamun ◽  
Iyswarya Narayanan ◽  
Di Wang ◽  
Anand Sivasubramaniam ◽  
Hosam K. Fathy
Keyword(s):  
Lithium Ion Battery ◽  
Demand Response ◽  
Optimization Problem ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Battery Pack ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Electricity Cost ◽  
De Algorithm

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 multi-objective 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.

Multi-objective optimization of charging patterns for lithium-ion battery management

2018 ◽  
Vol 159 ◽  
pp. 151-162 ◽  
Author(s):  
Kailong Liu ◽  
Kang Li ◽  
Haiping Ma ◽  
Jianhua Zhang ◽  
Qiao Peng
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Management ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi-objective optimization of lithium-ion battery model using genetic algorithm approach

2014 ◽  
Vol 270 ◽  
pp. 367-378 ◽  
Author(s):  
Liqiang Zhang ◽  
Lixin Wang ◽  
Gareth Hinds ◽  
Chao Lyu ◽  
Jun Zheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Battery Model ◽  
Genetic Algorithm Approach
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