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.
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.
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