Open AccessResidential Load Scheduling Based Analytical Optimization Method
Author(s) -
Mousa J. Sultan,
Mohammed Ali Tawfeeq,
Hilde Haider
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1076/1/012005
Subject(s) - particle swarm optimization , mathematical optimization , computer science , scheduling (production processes) , energy consumption , energy (signal processing) , genetic algorithm , demand response , peak demand , load shifting , engineering , algorithm , electricity , mathematics , electrical engineering , statistics
Peak load periods in smart grids significantly affect the energy stability produced by energy suppliers. One of the important factors that distinctly affects the load during these periods is the household energy consumption. Thus, managing and improving energy demand for smart home appliances can effectively reduce the peak loads which represents a major challenge. This paper introduces a dynamic Analytical optimization Method (AM) to find the optimum managing for residential energy load. The results showed that the maximum load of total demand is decreased by 35%, as well as, the energy consumption cost bill is decreased by 44%. The results of proposed method are compared with two widely used optimization methods; Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Although the results of the proposed method showed a superior time saving for achieving the final results.