LAPSE:2018.1156
Published Article
LAPSE:2018.1156
Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost
Yohwan Choi, Hongseok Kim
November 28, 2018
A self-sustainable base station (BS) where renewable resources and energy storage system (ESS) are interoperably utilized as power sources is a promising approach to save energy and operational cost in communication networks. However, high battery price and low utilization of ESS intended for uninterruptible power supply (UPS) necessitates active utilization of ESS. This paper proposes a multi-functional framework of ESS using dynamic programming (DP) for realizing a sustainable BS. We develop an optimal charging and discharging scheduling algorithm considering a detailed battery wear-out model to minimize operational cost as well as to prolong battery lifetime. Our approach significantly reduces total cost compared to the conventional method that does not consider battery wear-out. Extensive experiments for several scenarios exhibit that total cost is reduced by up to 70.6% while battery wear-out is also reduced by 53.6%. The virtue of the proposed framework is its wide applicability beyond sustainable BS and thus can be also used for other types of load in principle.
Keywords
battery wear-out cost, demand response (DR), dynamic programming (DP), energy storage system (ESS), optimal scheduling, peak shift, photovoltaic (PV), sustainable base station (BS)
Suggested Citation
Choi Y, Kim H. Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost. (2018). LAPSE:2018.1156
Author Affiliations
Choi Y: Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 121-742, Korea
Kim H: Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 121-742, Korea [ORCID]
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Journal Name
Energies
Volume
9
Issue
6
Article Number
E462
Year
2016
Publication Date
2016-06-16
Published Version
ISSN
1996-1073
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PII: en9060462, Publication Type: Journal Article
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LAPSE:2018.1156
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doi:10.3390/en9060462
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Nov 28, 2018
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CC BY 4.0
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Nov 28, 2018
 
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Calvin Tsay
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