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July  2017, 13(3): 1495-1510. doi: 10.3934/jimo.2017004

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

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

Received  October 2015 Published  December 2016

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.

Citation: Shunsuke Matsuzawa, Satoru Harada, Kazuya Monden, Yukihiro Takatani, Yutaka Takahashi. Effect of mobility of smart meters on performance of advanced metering infrastructure. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1495-1510. doi: 10.3934/jimo.2017004
References:
[1]

D. H. Davis and S. A. Gronemeyer, Performance of slotted ALOHA random access with delay capture and randomized time of arrival, IEEE Transactions on Communications, 28 (1980), 703-710.  doi: 10.1109/TCOM.1980.1094718.  Google Scholar

[2]

O. Fatemieh, R. Chandra and C. A. Gunter, Low cost and secure smart meter communications using the tv white spaces Resilient Control Systems (ISRCS), 2010 3rd International Symposium on (2010). doi: 10.1109/ISRCS.2010.5602162.  Google Scholar

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C. LiJ. Li and X. Cai, Performance evaluation of IEEE 802.11 WLAN-high speed packet wireless data network for supporting voice service, IEEE Wireless Communications and Networking Conference, 3 (2004), 1463-1468.   Google Scholar

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R. Natsugari and T. Hirano, NEC's Approach towards Advanced Metering Infrastructure (AMI), NEC Technical Journal, 7 (2012), 92-96.   Google Scholar

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J. Oba, What is a smart meter? -Why is it essential to smart grid?, http://sangyo.jp/ri/sg/na/article/20110408.html. (in Japanese) Google Scholar

[6]

S. OgasawaraS. HaradaK. MondenY. Takatani and Y. Takahashi, Mathematical Modeling and Theoretical Analysis of Advanced Metering Infrastructure with Hybrid Communication System (in Japanese), IEICE Transactions on Information and Systems, J99-D (2016), 652-661.   Google Scholar

[7]

F. Romano and L. Zoppi, A combined reservation random access polling protocol for voice-data transmissions in a wireless packet network, IEEE Transactions on Vehicular Technology, 48 (1999), 652-662.   Google Scholar

[8]

I. Rubin and M. Y. Louie, A hybrid TDMA/random-access scheme for multiple-access communication networks, Computers & Electrical Engineering, 10 (1983), 159-181.  doi: 10.1016/0045-7906(83)90005-8.  Google Scholar

[9]

S. SenD. J. DorseyR. Guerin and M. Chiang, Analysis of Slotted ALOHA with multipacket messages in clustered surveillance networks, IEEE Military Communications Conference, (2012), 1-6.  doi: 10.1109/MILCOM.2012.6415679.  Google Scholar

[10]

Tokyo Gas and Hitachi, Toward integration of wireless metering systems for water and gas consumption, http://www.hitachi.co.jp/New/cnews/month/2014/12/1219.html. (in Japanese) Google Scholar

[11]

Y. Yang and T. P. Yum, Delay distributions of Slotted ALOHA and CSMA, IEEE Transactions on Communications, 51 (2003), 1846-1857.  doi: 10.1109/TCOMM.2003.819201.  Google Scholar

show all references

References:
[1]

D. H. Davis and S. A. Gronemeyer, Performance of slotted ALOHA random access with delay capture and randomized time of arrival, IEEE Transactions on Communications, 28 (1980), 703-710.  doi: 10.1109/TCOM.1980.1094718.  Google Scholar

[2]

O. Fatemieh, R. Chandra and C. A. Gunter, Low cost and secure smart meter communications using the tv white spaces Resilient Control Systems (ISRCS), 2010 3rd International Symposium on (2010). doi: 10.1109/ISRCS.2010.5602162.  Google Scholar

[3]

C. LiJ. Li and X. Cai, Performance evaluation of IEEE 802.11 WLAN-high speed packet wireless data network for supporting voice service, IEEE Wireless Communications and Networking Conference, 3 (2004), 1463-1468.   Google Scholar

[4]

R. Natsugari and T. Hirano, NEC's Approach towards Advanced Metering Infrastructure (AMI), NEC Technical Journal, 7 (2012), 92-96.   Google Scholar

[5]

J. Oba, What is a smart meter? -Why is it essential to smart grid?, http://sangyo.jp/ri/sg/na/article/20110408.html. (in Japanese) Google Scholar

[6]

S. OgasawaraS. HaradaK. MondenY. Takatani and Y. Takahashi, Mathematical Modeling and Theoretical Analysis of Advanced Metering Infrastructure with Hybrid Communication System (in Japanese), IEICE Transactions on Information and Systems, J99-D (2016), 652-661.   Google Scholar

[7]

F. Romano and L. Zoppi, A combined reservation random access polling protocol for voice-data transmissions in a wireless packet network, IEEE Transactions on Vehicular Technology, 48 (1999), 652-662.   Google Scholar

[8]

I. Rubin and M. Y. Louie, A hybrid TDMA/random-access scheme for multiple-access communication networks, Computers & Electrical Engineering, 10 (1983), 159-181.  doi: 10.1016/0045-7906(83)90005-8.  Google Scholar

[9]

S. SenD. J. DorseyR. Guerin and M. Chiang, Analysis of Slotted ALOHA with multipacket messages in clustered surveillance networks, IEEE Military Communications Conference, (2012), 1-6.  doi: 10.1109/MILCOM.2012.6415679.  Google Scholar

[10]

Tokyo Gas and Hitachi, Toward integration of wireless metering systems for water and gas consumption, http://www.hitachi.co.jp/New/cnews/month/2014/12/1219.html. (in Japanese) Google Scholar

[11]

Y. Yang and T. P. Yum, Delay distributions of Slotted ALOHA and CSMA, IEEE Transactions on Communications, 51 (2003), 1846-1857.  doi: 10.1109/TCOMM.2003.819201.  Google Scholar

Figure 1.  Transmission schedule for AMI
Figure 2.  State transition diagram of smart meters
Figure 3.  Throughput vs. number of smart meters. $(L=5)$
Figure 4.  Throughput vs. number of smart meters. $(L=20)$
Figure 5.  Maximum throughput vs. number of time slots
Figure 6.  Throughput vs. number of smart meters. ($T/P_{\rm off}=18000$)
Figure 7.  Successful transmission probability vs. number of smart meters. ($T/P_{\rm off}=18000$)
Figure 8.  Mean transmission delay vs. number of smart meters. ($T/P_{\rm off}=18000$)
Figure 9.  Coefficient of variation of transmission delay vs. number of smart meters. ($T/P_{\rm off}=18000$)
Figure 10.  Throughput vs. number of smart meters. ($T/P_{\rm off}:T/P_{\rm on}=1:4$)
Figure 11.  Successful transmission probability vs. number of smart meters. ($T/P_{\rm off}:T/P_{\rm on}=1:4$)
Figure 12.  Mean transmission delay vs. number of smart meters. ($T/P_{\rm off}:T/P_{\rm on}=1:4$)
Figure 13.  Coefficient of variation of transmission delay vs. number of smart meters. ($T/P_{\rm off}:T/P_{\rm on}=1:4$)
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
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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