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Total optimization of smart city by global-best brain storm optimization

Published: 06 July 2018 Publication History

Abstract

This paper proposes a total optimization method of a smart city (SC) by Global-best brain storm optimization (GBSO). The SC model includes natural gas utilities, electric power utilities, drinking and waste water treatment plants, industries, buildings, residences, and railroads. The proposed method minimizes energy cost, shifts actual electric power loads, and minimizes C02 emission using the model. Particle Swarm Optimization (PSO), Differential Evolution (DE), and Differential evolutionary particle swarm optimization (DEEPSO) have been applied to the optimization problem. However, there is room for improving solution quality. The proposed GBSO based method is applied to a model which considers a moderately-sized city in Japan, such as Toyama city. The proposed method is compared with the conventional DEEPSO and BSO based methods with promising results.

References

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Xcel Energy, SMARTGRDCITY, http://smartgridcity.xcelenergy.com/
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K. Yasuda, "Definition and Modelling of Smart Community," Proc. of IEEJ National Conference, 1-H1--2, March 2015 (in Japanese).
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N. Yamaguchi, T. Ogata, Y. Ogita, and S. Asanuma., "Modelling Energy Supply Systems in Smart Community," Proc. of IEEJ National Conference, 1-H1--3, March 2015 (in Japanese).
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T. Matsui, T. Kosaka, D. Komaki, N. Yamaguchi, and Y. Fukuyama., "Energy Consumption Models in Smart Community," Proc. of IEEJ National Conference, 1-H1--4, March 2015 (in Japanese).
[5]
M. Sato, and Y. Fukuyama. "Total Optimization of Smart Community by Particle Swarm Optimization Considering Reduction of Search Space," Proc. of 2016 IEEE International Conference on Power System Technology (POWERCON), 2016.
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M. Sato, and Y. Fukuyama. "Total Optimization of Smart Community by Differential Evolution Considering Reduction of Search Space," Proc. of IEEE TENCON 2016
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M. Sato, and Y. Fukuyama, "Total Optimization of Smart Community by Differential Evolutionary Particle Swarm Optimization," Proc. of IFAC World Congress 2017
[8]
Shi Y, Tan Y, Shi Y, Chai Y, Wang G (ed.), "Brain Storm Optimization Algorithm," Advances in swarm intelligence lecture notes in computer science Vol.6728 pp.295--303
[9]
M. El-Adb, "Global-best brain storm optimization algorithm," in Swarm and Evolutionary Computation Vol. 37 pp. 27--44.
[10]
T. Kanno, T. Matsui, and Y. Fukuyama., "Various Scenarios and Simulation Examples Using Smart Community Models, " Proc. of IEEJ National Conference, 1-H1--5, March 2015 (in Japanese)

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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2018
1968 pages
ISBN:9781450357647
DOI:10.1145/3205651
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 06 July 2018

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