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Sustainability 2016, 8(12), 1273; doi:10.3390/su8121273

Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

1
Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan
2
Department of Electrical Engineering, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan
3
Hawaii Natural Energy Institute, University of Hawaii, 1680 East-West Rd, Honolulu, HI 96822, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Umberto Berardi
Received: 16 August 2016 / Revised: 15 November 2016 / Accepted: 29 November 2016 / Published: 7 December 2016
(This article belongs to the Special Issue Sustainable Electric Power Systems Research)

Abstract

A smart house generally has a Photovoltaic panel (PV), a Heat Pump (HP), a Solar Collector (SC) and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions. View Full-Text
Keywords: smart house; fixed battery capacity; shiftable load; real-time pricing; NSGA2 smart house; fixed battery capacity; shiftable load; real-time pricing; NSGA2
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Miyazato, Y.; Tahara, H.; Uchida, K.; Celestino Muarapaz, C.; Motin Howlader, A.; Senjyu, T. Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems. Sustainability 2016, 8, 1273.

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