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Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo
by
Yamada, Satoshi
, Takanokura, Masato
, Matsui, Masayuki
, Yamada, Tetsuo
, Ijuin, Hiromasa
in
Alternative energy sources
/ Biogas
/ Carbon
/ Climate change
/ Collaboration
/ Consumption
/ Cooling
/ Costs
/ dynamic inventory control
/ Electricity
/ Electricity distribution
/ electricity storage management
/ Emissions
/ Energy industry
/ Energy management
/ Greenhouse gases
/ initial charge
/ Integer programming
/ Manufacturing
/ Methods
/ power demand and supply forecast
/ Power supply
/ Production planning
/ renewable energy
/ Renewable resources
/ Scheduling
/ Solar energy
/ Stochastic models
/ Supply & demand
/ Wave power
2022
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Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo
by
Yamada, Satoshi
, Takanokura, Masato
, Matsui, Masayuki
, Yamada, Tetsuo
, Ijuin, Hiromasa
in
Alternative energy sources
/ Biogas
/ Carbon
/ Climate change
/ Collaboration
/ Consumption
/ Cooling
/ Costs
/ dynamic inventory control
/ Electricity
/ Electricity distribution
/ electricity storage management
/ Emissions
/ Energy industry
/ Energy management
/ Greenhouse gases
/ initial charge
/ Integer programming
/ Manufacturing
/ Methods
/ power demand and supply forecast
/ Power supply
/ Production planning
/ renewable energy
/ Renewable resources
/ Scheduling
/ Solar energy
/ Stochastic models
/ Supply & demand
/ Wave power
2022
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Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo
by
Yamada, Satoshi
, Takanokura, Masato
, Matsui, Masayuki
, Yamada, Tetsuo
, Ijuin, Hiromasa
in
Alternative energy sources
/ Biogas
/ Carbon
/ Climate change
/ Collaboration
/ Consumption
/ Cooling
/ Costs
/ dynamic inventory control
/ Electricity
/ Electricity distribution
/ electricity storage management
/ Emissions
/ Energy industry
/ Energy management
/ Greenhouse gases
/ initial charge
/ Integer programming
/ Manufacturing
/ Methods
/ power demand and supply forecast
/ Power supply
/ Production planning
/ renewable energy
/ Renewable resources
/ Scheduling
/ Solar energy
/ Stochastic models
/ Supply & demand
/ Wave power
2022
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Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo
Journal Article
Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo
2022
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Overview
In recent years, environmental and energy issues relating to global warming have become more serious, and there is a need to shift from conventional power generation, which emits an abundance of carbon dioxide, to renewable energy sources without emissions, such as solar and wind. However, solar power generation, which is one of the renewable energies, changes dynamically, depending on real time weather conditions. Thus, power supplied mainly by solar power generation is often unstable, and an appropriate on-demand energy management for demand-to-supply is required to ensure a stable power supply. Demand-to-supply management methods include inventory management analysis and on-demand inventory management analysis. The cumulative-control method has been used as one of the production management methods to visually manage inventory status in factories and warehouses, while the on-demand cumulative-control method is an extension of inventory management analysis. This study models a demand-to-supply management method for a solar power generation system by using the on-demand cumulative-control method in an actual case. First, a demand-to-supply management method is modeled by an on-demand cumulative-control method, using actual power data from a childcare facility in Tokyo. Next, the on-demand cumulative-control method is adopted to the case without batteries, and the amount of electricity to be purchased is estimated. Finally, the effectiveness of the maximum battery capacity and the amount of the initial charge are examined and discussed by sensitivity analysis.
Publisher
MDPI AG
Subject
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