Intelligent scheduling of smart home appliances based on demand response considering the cost and peak-to-average ratio in residential homes

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dc.contributor.authorTutkun, Nedim
dc.contributor.authorBurgio, Alessandro
dc.contributor.authorJasinski, Michal
dc.contributor.authorLeonowicz, Zbigniew
dc.contributor.authorJasinska, Elzbieta
dc.date.accessioned2022-01-31T08:19:19Z
dc.date.available2022-01-31T08:19:19Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractAbstract: With recent developments, smart grids assured for residential customers the opportunity to schedule smart home appliances’ operation times to simultaneously reduce both the electricity bill and the PAR based on demand response, as well as increasing user comfort. It is clear that the multiobjective combinatorial optimization problem involves constraints and the consumer’s preferences, and the solution to the problem is a difficult task. There have been a limited number of investigations carried out so far to solve the indicated problems using metaheuristic techniques like particle swarm optimization, mixed-integer linear programming, and the grey wolf and crow search optimization algorithms, etc. Due to the on/off control of smart home appliances, binary-coded genetic algorithms seem to be a well-fitted approach to obtain an optimal solution. It can be said that the novelty of this work is to represent the on/off state of the smart home appliance with a binary string which undergoes crossover and mutation operations during the genetic process. Because special binary numbers represent interruptible and uninterruptible smart home appliances, new types of crossover and mutation were developed to find the most convenient solutions to the problem. Although there are a few works which were carried out using the genetic algorithms, the proposed approach is rather distinct from those employed in their work. The designed genetic software runs at least ten times, and the most fitting result is taken as the optimal solution to the indicated problem; in order to ensure the optimal result, the fitness against the generation is plotted in each run, whether it is converged or not. The simulation results are significantly encouraging and meaningful to residential customers and utilities for the achievement of the goal, and they are feasible for a wide-range applications of home energy management systems.en_US
dc.identifier.doi10.3390/en14248510en_US
dc.identifier.scopus2-s2.0-85121560907en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/5174
dc.identifier.urihttps://doi.org/10.3390/en14248510
dc.identifier.wosWOS:000737557800001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofEnergiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjecthome energy managementen_US
dc.subjectBinary-coded genetic algorithmsen_US
dc.subjectOptimal power schedulingen_US
dc.subjectDemand responseen_US
dc.titleIntelligent scheduling of smart home appliances based on demand response considering the cost and peak-to-average ratio in residential homesen_US
dc.typeArticleen_US

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