Scheduling of smart home appliances for optimal energy management in smart grid using Harris-hawks optimization algorithm

• Published in: Optimization and engineering 2021-09, Vol.22 (3), p.1625-1652
• Main Authors: Mouassa, Souhil, Bouktir, Tarek, Jurado, Francisco
• Format: Article
• Language: English
• Subjects: Algorithms; Brand loyalty; Control; Distributed generation; Electric power demand; Electric utilities; Electricity; Energy management; Engineering; Environmental Management; Financial Engineering; Household appliances; Mathematics; Mathematics and Statistics; Operations Research/Decision Theory; Optimization; Optimization techniques; Peak load; Peak periods; Research Article; Scheduling; Smart buildings; Smart grid; Systems Theory; Time of use electricity pricing
• ISSN: 1389-4420; 1573-2924
• DOI: 10.1007/s11081-020-09572-1

With arrival of advanced technologies, automated appliances in residential sector are still in unlimited growth. Therefore, the design of new management schemes becomes necessary to be achieved for the electricity demand in an effort to ensure safety of domestic installations. To this end, the Demand Side Management (DSM) is one of suggested solution which played a significant role in micro-grid and Smart Grid systems. DSM program allows end-users to communicate with the grid operator so they can contribute in making decisions and assist the utilities to reduce the peak power demand through peak periods. This can be done by managing loads in a smart way, while keeping up customer loyalty. Nowadays, several DSM programs are proposed in the literature, almost all of them are focused on the domestic sector energy management system. In this original work, four heuristics optimization algorithms are proposed for energy scheduling in smart home, which are: bat algorithm, grey wolf optimizer, moth flam optimization, algorithm, and Harris hawks optimization (HHO) algorithm. The proposed model used in this experiment is based on two different electricity pricing schemes: Critical-Peak-Price and Real-Time-Price. In addition, two operational time intervals (60 min and 12 min) were considered to evaluate the consumer’s demand and behavior of the suggested scheme. Simulation results show that the suggested model schedules the appliances in an optimal way, resulting in electricity-cost and peaks reductions without compromising users’ comfort. Hence, results confirm the superiority of HHO algorithm in comparison with other optimization techniques.