Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes

Souhil Mouassa; Marcos Tostado-Véliz; Francisco Jurado

doi:10.1515/ijeeps-2021-0104

Article

Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes

Souhil Mouassa , Marcos Tostado-Véliz and Francisco Jurado

Published/Copyright: August 10, 2021

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From the journal International Journal of Emerging Electric Power Systems Volume 22 Issue 6

Abstract

With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.

Keywords: artificial ecosystem optimization algorithm; demand side management; modern optimization algorithms; optimal power scheduling of home appliances

Corresponding author: Souhil Mouassa, Department of Electrical Engineering, University of Bouira, Bouira, Algeria; and Department of Electrical Engineering, University of Jaén, 23700 EPS Linares, Jaén, Spain, E-mail: souhil.mouassa@yahoo.fr

Acknowledgments

Many thanks to the Electrical Engineering Department, Universities of Bouira and Jaén for financing this work. This work was conducted in Department of Electrical Engineering, University of Jaen-Linares; Spain. This work dedicate to the memory of my dear Professor Tarek Bouktir may Allah forgive him, raise his ranks and grant him the highest degrees in Jannah.

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2021-02-15

Accepted: 2021-07-16

Published Online: 2021-08-10

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Articles in the same Issue

https://doi.org/10.1515/ijeeps-2021-0104

Keywords for this article

artificial ecosystem optimization algorithm; demand side management; modern optimization algorithms; optimal power scheduling of home appliances