Application Of Optimization To Data Communication In Smart Grids
Résumé: 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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