Finding optimal schedules in a home energy management system

•We model the scheduling problem for home energy management systems.•The system includes appliances, storage devices, energy generators and ACs.•Stochastic appliance usage, energy prices and weather conditions are considered.•We show that the energy bill can be decreased by one fourth with the usage...

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Bibliographic Details
Published in:Electric power systems research 2020-05, Vol.182, p.106229, Article 106229
Main Authors: Merdanoğlu, Hakan, Yakıcı, Ertan, Doğan, O. Tufan, Duran, Serhan, Karatas, Mumtaz
Format: Article
Language:English
Subjects:
Battery cycles
Computer simulation
Decision analysis
Decision support systems
Electric appliances
Electric vehicles
Electricity consumption
Electricity pricing
Energy consumption
Energy costs
Energy management
Energy storage
Home energy management system
Household appliances
Mathematical models
Optimization
Residential energy
Schedules
Scheduling
Stochastic optimization
Storage capacity
Studies
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Summary:•We model the scheduling problem for home energy management systems.•The system includes appliances, storage devices, energy generators and ACs.•Stochastic appliance usage, energy prices and weather conditions are considered.•We show that the energy bill can be decreased by one fourth with the usage of HEMS. In this study, we model and solve the scheduling problem embedded in a home energy management system (HEMS), which enables users to overcome the major obstacles in implementing demand response programs. The problem aims to find the minimum energy cost while taking into account the time-varying prices, generation from renewable sources, usage demands for each appliance in household, battery storage capacity and grid constraints. Due to the uncertainties in supply, demand and electricity price, a stochastic optimization approach is utilized. A solution to the problem determines schedules of the operating periods of household appliances, charging cycles of battery storage and plug-in electric vehicles (EVs) and electricity purchase and sale periods for the following days in the decision horizon. We analyze both effects of different price tariffs on HEMS and conduct simulations in order to compare two cases for a green house in terms of energy consumption, where first case is when the house is supported by HEMS and the second one is when the house has no decision support system. Experimental results support the benefit of the usage of the proposed model in a HEMS.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2020.106229