Browsing by Author "Saoud, Afaf"

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    Application of optimization to data communication in smart grids
    (Université M'Hamed Bougara : Institut de génie électrique et électronique, 2021) Saoud, Afaf; Recioui, Abdelmadjid(Directeur de thèse)
    Smart grid has been introduced as a new generation of power systems that ensures reliable, secure, low cost, and intelligent energy distribution and consumption. In smart grids, a complex two-way communication infrastructure is involved generating huge amount of data from the different parts of the grid which generates delay and accuracy problems that affect the performance of the smart grid. In this thesis, optimization is applied to data communication at different levels of the smart grid. Three significant issues are investigated: data transfer improvement in wide area monitoring (WAMS), load balancing in cloud-fog computing and load energy forecasting based on smart metering system data. First, we propose an optimization of the WAMS data transfer through PMU reporting rate. The objective of this work is based on the variation of the reporting rate to prove its relation with the PMU location and compare the results to those of the fixed reporting rate as specified in the standards. The Search Group Algorithm is used for the reporting rate optimization. We consider the PMU data latency and completeness as performance metrics. The simulation is performed on MATLAB/SIMULINK. Second, load balancing in smart grid to overcome the delay issue is proposed. In this work, we introduce a cloud-fog computing system and hybrid optimization based on WOA-BAT to enhance the task scheduling in the virtual machines. The performance measures for this study are the processing and response times. The simulation is carried out on Java platform in Net beans and cloud analyst tool. Finally, optimization applied to short term forecasting as an application on smart metering data is presented. In this part, we optimize a long short-term memory autoencoder (LSTM-AE) model parameters using Particle Swarm Optimization (PSO) to give better results in terms of forecasting and then compare to state of art forecasting models. The evaluation metrics used for the comparison are mean absolute error (MAE) and root mean square error (RMSE). The simulation is done on PYTHON
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    Hybrid algorithm for cloud-fog system based load balancing in smart grids
    (Institute of Advanced Engineering and Science, 2022) Saoud, Afaf; Recioui, Abdelmadjid
    Energy management is among the key components of smart metering. Its role is to balance energy consumption and distribution. Smart devices integration results in a huge data exchange between different parts of the smart grid causing a delay in the response and processing time. To overcome this latency issue, the cloud computing has been proposed. However, cloud computing does not perform well when there are large distances from the cloud to the consumers. Fog computing solves this issue. In this paper, a cloud-fog computing system is presented to achieve an accurate load balancing. The hybridization of whale optimization algorithm with bat algorithm (WOA-BAT) is proposed for load balancing. The model performance is compared to state of art load balancing techniques as throttled, round robin, whale and particle swarm optimization algorithms in terms of processing and the response time. The results reveal that the proposed WOA-BAT has better results in terms of response time than the three algorithms with 4.3% improvement compared to RR and TH. It also outperforms all the algorithms in terms of processing time by at least 22.3%