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The introduction amount of renewable energy has increased, but control of power system is getting harder. As a solution of this problem, the use of an off-grid smart house that is not connected to the electric power system. The off-grid smart house can freely introduce renewable energy generation equipments. Also, it can operate without emission of carbon dioxide. In recent years, attention has been focused on electric vehicles (EVs). The EVs do not emit carbon dioxide during driving. Moreover, carbon dioxide reduction effect is enhanced by charging EVs using renewable energy generation. In addition, EVs become a way of carrying electricity, and it is possible to supply electricity to the off-grid smart house. EVs can compensate for the electricity and support operation of the off-grid smart house. This paper discusses the introduced capacity of equipments in the off-grid smart house having EV by performing optimization simulation. Moreover, it is possible to operate highly practical by classifying electric appliances load. The simulation results are compared with existing electricity fees, and the validity of the off-grid smart house has been confirmed.
Yasuaki Miyazato; Shota Tobaru; Tomonobu Senjyu; Abdul Motin Howlader; Toshihisa Funabashi. Optimum operation and capacity for off-grid smart house. 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia) 2017, 1137 -1142.
AMA StyleYasuaki Miyazato, Shota Tobaru, Tomonobu Senjyu, Abdul Motin Howlader, Toshihisa Funabashi. Optimum operation and capacity for off-grid smart house. 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia). 2017; ():1137-1142.
Chicago/Turabian StyleYasuaki Miyazato; Shota Tobaru; Tomonobu Senjyu; Abdul Motin Howlader; Toshihisa Funabashi. 2017. "Optimum operation and capacity for off-grid smart house." 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia) , no. : 1137-1142.
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.
Yasuaki Miyazato; Hayato Tahara; Kosuke Uchida; Cirio Celestino Muarapaz; Abdul Motin Howlader; Tomonobu Senjyu. Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems. Sustainability 2016, 8, 1273 .
AMA StyleYasuaki Miyazato, Hayato Tahara, Kosuke Uchida, Cirio Celestino Muarapaz, Abdul Motin Howlader, Tomonobu Senjyu. Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems. Sustainability. 2016; 8 (12):1273.
Chicago/Turabian StyleYasuaki Miyazato; Hayato Tahara; Kosuke Uchida; Cirio Celestino Muarapaz; Abdul Motin Howlader; Tomonobu Senjyu. 2016. "Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems." Sustainability 8, no. 12: 1273.