Modeling,metrics, and optimal design for solar energy-powered base station system

Authors

Heng Wang, Chongqing University
Hongjia Li, Chinese Academy of Sciences
Chaowei Tang, Chongqing UniversityFollow
Lingbao Ye, Chinese Academy of Sciences
Xin Chen, Chinese Academy of Sciences
Hui Tang, Chongqing University
Song Ci, University of Nebraska-LincolnFollow

Date of this Version

2015

Citation

Wang et al. EURASIP Journal on Wireless Communications and Networking (2015) 2015:39 DOI 10.1186/s13638-015-0270-0.

Comments

© 2015 Wang et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.

Abstract

Using renewable energy system in powering cellular base stations (BSs) has been widely accepted as a promising avenue to reduce and optimize energy consumption and corresponding carbon footprints and operational expenditures for 4G and beyond cellular communications. However, how to design a reliable and economical renewable energy powering (REPing), while guaranteeing communication reliability, renewable energy utilization, and system durability, is still a great challenge. Motivated by this challenge, we firstly model the dynamic energy flow behavior of solar energy-powered BS by using stochastic queue model, jointly considering instability of solar energy generation, non-linear effects of energy storage, and time varies of traffic load. On the basis of the model, three key performance metrics, including service outage probability (SoP), solar energy utilization efficiency (SEuE), and mean depth of discharge (MDoD), are defined, and close-form expressions of them are derived. Finally, under the guidelines of defined metrics, the sizing optimization problem is formulated, and then we propose the capital expenditure (CAPEX) minimization algorithm to resolve it with considerations of communication reliability, efficiency, and durability. Numerical results conducted to demonstrate the effectiveness of our proposed metrics vividly showed the close relationship between design metrics and system parameters. Simulation results also showed that our proposed algorithm can reduce at least 12.1% CAPEX compared with the classic algorithms and guarantee SoP below 0.82%, SEuE above 97%, and MDoD ranging from 7.2% to 9.1%, which means that the optimal design was achieved in terms of system reliability, efficiency, durability, and investment. The proposed modeling, design metrics, and sizing method provide a theoretical basis for actual designs of REPing BS system, which also can be further applied to the scenario of other forms of renewable energy powered system.