Open AccessResidential load control system based analytical optimization method for real residential data consumption
Author(s) -
Mousa J. Sultan,
Mohammed Ali Tawfeeq,
Haider T. Haider
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1973/1/012018
Subject(s) - particle swarm optimization , demand response , load shifting , consumption (sociology) , energy consumption , computer science , smart grid , peak demand , real time computing , simulation , automotive engineering , electricity , engineering , electrical engineering , social science , sociology , machine learning
Peak load periods have a great impact for energy demand in smart grid. These times is directly related to the consumption of residential sector, thus utility need to add additional generation capacity during peak time to support the demand required. This paper proposes a demand response system for residential household. Analytical Method (AM) is used to optimize the load consumption based real data of typical residential home. The consumption data are measured using smart plugs that have been designed and implemented to communicate with household’s smart devices. The simulation results show the peak load was reduced by 37.64% and the energy consumption cost bill was reduced by 29.52%. The proposed method is compared with other optimization methods such as Bacterial Foraging Optimization (BFO), and Particle Swarm Optimization (PSO) to highlight the finding. The proposed approach indicated a greater saving period to produce the final results.