Battery aging-aware energy management of green small cells powered by the smart grid

Mobile operators are deploying energy-harvesting heterogeneous networks due to their foreseen advantages such as self-sustainable capability and reduced operating expenditure, which cannot be offered by conventional grid powered communications. However, the used energy storage is subject to irreversible aging mechanisms, requiring intelligent management that considers both the energy cost and battery life cycle. In this paper, we propose a cognitive energy management strategy for small cell base stations powered by local renewable energy, a battery, and the smart grid to simultaneously minimize electricity expenditures of the mobile operators and enhance the life span of the storage device. Non-linear battery models and aging processes are considered to formulate the energy cost optimization problem. Simulation results in different configurations show that a degradation-aware policy significantly improves the battery lifetime, while achieving considerable cost savings.

[1]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[2]  Antonio Capone,et al.  Enabling Green cellular networks: A survey and outlook , 2014, Comput. Commun..

[3]  Y. Jading,et al.  INFSO-ICT-247733 EARTH Deliverable D 2 . 3 Energy efficiency analysis of the reference systems , areas of improvements and target breakdown , 2012 .

[4]  Simona Onori,et al.  A control-oriented lithium-ion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management , 2015 .

[5]  M. Broussely,et al.  Main aging mechanisms in Li ion batteries , 2005 .

[6]  Nouredine Hadjsaid,et al.  Energy Management of Green Small Cells Powered by the Smart Grid , 2016, CrownCom.

[7]  Anders Forsgren,et al.  Interior Methods for Nonlinear Optimization , 2002, SIAM Rev..

[8]  Amit Gupta,et al.  Effect of Relaxation Periods over Cycling Performance of a Li-Ion Battery , 2015 .

[9]  P. Malbranche,et al.  Assessment of Storage Ageing in Different Types of PV Systems: Technical and Economical Aspects , 2008 .

[10]  James Marco,et al.  An Acausal Li-Ion Battery Pack Model for Automotive Applications , 2014 .

[11]  Wen-Yeau Chang,et al.  The State of Charge Estimating Methods for Battery: A Review , 2013 .

[12]  Huei Peng,et al.  An Open-Circuit-Voltage Model of Lithium-Ion Batteries for Effective Incremental Capacity Analysis , 2013 .

[13]  Johann Leithon,et al.  Online energy management strategies for base stations powered by the smart grid , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[14]  Jacques Palicot.  Cognitive radio: an enabling technology for the green radio communications concept , 2009, IWCMC.

[15]  Michalis E. Zervakis,et al.  Solar Radiation Time-Series Prediction Based on Empirical Mode Decomposition and Artificial Neural Networks , 2014, AIAI.

[16]  Maxime Montaru,et al.  Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use , 2014 .

[17]  Dusit Niyato,et al.  Decomposition of Stochastic Power Management for Wireless Base Station in Smart Grid , 2012, IEEE Wireless Communications Letters.

[18]  Xuemin Shen,et al.  Energy-Aware Traffic Offloading for Green Heterogeneous Networks , 2016, IEEE Journal on Selected Areas in Communications.

[19]  Hongjia Li,et al.  Modeling, metrics, and optimal design for solar energy-powered base station system , 2015, EURASIP Journal on Wireless Communications and Networking.

[20]  N. Hadjsaid,et al.  Energy supply for remote base transceiver stations of telecommunication , 2004, IEEE Power Engineering Society General Meeting, 2004..

[21]  Dirk Uwe Sauer,et al.  Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data , 2012 .

[22]  Delphine Riu,et al.  A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .

[23]  A. Eddahech,et al.  Lithium-ion battery performance improvement based on capacity recovery exploitation , 2013 .

[24]  Khaled Ben Letaief,et al.  Grid Energy Consumption and QoS Tradeoff in Hybrid Energy Supply Wireless Networks , 2016, IEEE Transactions on Wireless Communications.

[25]  Balasubramaniam Natarajan,et al.  Power management in heterogeneous networks with energy harvesting base stations , 2015, Phys. Commun..

[26]  R. Restaino,et al.  Comparing particle filter and extended kalman filter for battery State-Of-Charge estimation , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[27]  Loutfi Nuaymi,et al.  Renewable energy in cellular networks: A survey , 2013, 2013 IEEE Online Conference on Green Communications (OnlineGreenComm).

[28]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[29]  Giuseppe Piro,et al.  HetNets Powered by Renewable Energy Sources: Sustainable Next-Generation Cellular Networks , 2013, IEEE Internet Computing.

[30]  Leonardo Badia,et al.  Energy Management Policies for Harvesting-Based Wireless Sensor Devices with Battery Degradation , 2013, IEEE Transactions on Communications.

[31]  Muhammad Ali Imran,et al.  How much energy is needed to run a wireless network? , 2011, IEEE Wireless Communications.

[32]  Wuyi Yue,et al.  Energy-saving strategy for green cognitive radio networks with an LTE-advanced structure , 2016, Journal of Communications and Networks.

[33]  Y Riffonneau,et al.  Optimal Power Flow Management for Grid Connected PV Systems With Batteries , 2011, IEEE Transactions on Sustainable Energy.

[34]  Nouredine Hadjsaid,et al.  Fuzzy Q-Learning based energy management of small cells powered by the smart grid , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).