Effect of mobility of smart meters on performance of advanced metering infrastructure

Shunsuke Matsuzawa; Satoru Harada; Kazuya Monden; Yukihiro Takatani; Yutaka Takahashi

doi:10.3934/jimo.2017004

Article Contents

2017, Volume 13, Issue 3: 1495-1510. Doi: 10.3934/jimo.2017004

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Effect of mobility of smart meters on performance of advanced metering infrastructure

1.
Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
2.
Center for Technology Innovation -System Research & Development Group, Hitachi, Ltd Yoshida-Cho, Totsuka-ku, Yokohama-shi, Kanagawa 244-0817, Japan
3.
Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan

^* Corresponding author: Shunsuke Matsuzawa
^* Corresponding author: Shunsuke Matsuzawa

The reviewing process of the paper was handled by Wuyi Yue as Guest Editor

Received: October 2015

Published: September 2017

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Abstract

In order to realize a smart grid, Advanced Metering Infrastructure (AMI) has started to be deployed. AMI automates meter reading operations and enables real-time monitoring of power usage. Monitoring power consumption data will be useful for power generation planning, power demand control, and peak shift. In addition to monitoring power consumption, in AMI networks, other types of communication (e.g., gas and water consumption, demand response for electricity, and inquiries to electric power companies) can be accommodated by using surplus bandwidth. An essential part of AMI is a set of electricity meters with communication functions, called smart meters, which transmit power consumption data to electric power companies periodically with fixed intervals. They have been installed in houses, factories, or buildings, and are expected to be equipped with electric vehicles in a future. We can also save energy by turning off smart meters when it is not necessary to communicate. In this paper, we present an analytical model to evaluate the performance of AMI taking the randomness of the number of smart meters into consideration, caused by the turn on/off of meters and mobility of meters across the AMI network coverage.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. Transmission schedule for AMI

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Figure 2. State transition diagram of smart meters

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Figure 3. Throughput vs. number of smart meters. $(L=5)$

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Figure 4. Throughput vs. number of smart meters. $(L=20)$

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Figure 5. Maximum throughput vs. number of time slots

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Figure 6. Throughput vs. number of smart meters. ($T/P_{\rm off}=18000$)

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Figure 7. Successful transmission probability vs. number of smart meters. ($T/P_{\rm off}=18000$)

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Figure 8. Mean transmission delay vs. number of smart meters. ($T/P_{\rm off}=18000$)

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Figure 9. Coefficient of variation of transmission delay vs. number of smart meters. ($T/P_{\rm off}=18000$)

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Figure 10. Throughput vs. number of smart meters. ($T/P_{\rm off}:T/P_{\rm on}=1:4$)

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Figure 11. Successful transmission probability vs. number of smart meters. ($T/P_{\rm off}:T/P_{\rm on}=1:4$)

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Figure 12. Mean transmission delay vs. number of smart meters. ($T/P_{\rm off}:T/P_{\rm on}=1:4$)

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Figure 13. Coefficient of variation of transmission delay vs. number of smart meters. ($T/P_{\rm off}:T/P_{\rm on}=1:4$)

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Table 1. Parameter setting

Number of smart meters $N_{\rm RA}$	5-1000
Length of a check cycle $T$ (sec)	1800
Duration of a polling period $T_{\rm P}$ (sec)	1500-1792.5
Duration of a random access period $T_{\rm RA}$ (sec)	7.5-300
Number of time slots of a random access period $L$	5-200
Length of a time slot of a random access period $t_{\rm RA}$ (sec)	1.5
Mean duration of on-state periods $T/P_{\rm off}$ (sec)	18000-180000
Mean duration of off-state periods $T/P_{\rm on}$ (sec)	4500-72000
Mean generation interval of transmission requests $T/\lambda$ (sec)	3600

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