Browsing by Author "Faradji, Mohamed"

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    A hybrid APSO–ANFIS optimization based load shifting technique for demand side management in smart grids
    (IAES, 2025) Faradji, Mohamed; Madani Layadi, Toufik; Rouabah, Khaled
    Cost and performance are considered important parameters to obtain an optimized configuration for smart grids. In this paper, a new optimization approach, based on a hybrid adaptive particle swarm with an adaptive neuro- fuzzy inference system (ANFIS) algorithm, has been proposed. This approach allows optimizing demand side management (DSM) using the load shifting technique. The impact of the latter on consumer profile, electricity pricing mechanisms, and overall grid performance are illustrated. In this simulation, the focus lies on modeling DSM using a day-ahead load shifting approach as a minimization problem. Simulation experiments have been tested separately on three different demand zones, namely, residential, commercial, and industrial zones. A comparative study of solutions was performed, focusing on both reduced peak demand and operational costs. The obtained results demonstrate that the optimization presented in this article approach outperforms the other approaches by achieving greater savings in the residential and commercial sectors. The study proved a significant reduction in peak demand. In fact, values of 23.76%, 17.61% and 16.5% in peak demand reduction are achieved in the case of residential, commercial, and industrial sectors, respectively. Furthermore, operational cost reductions of 7.52%, 9.6%, and 16.5% are obtained for the three different cases.
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    Contribution to the optimization of smart grids
    (Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Faradji, Mohamed; Layadi, Toufik Madani(Directeur de thèse)
    The transition toward smart grids demands sophisticated optimization techniques to enhance efficiency, stability, and demand responsiveness. This thesis contributes to smart grid optimization by leveraging advanced optimization and computational intelligence methods, including Particle Swarm Optimization (PSO), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The study focuses on two critical areas: demand-side management (DSM) for peak load reduction and load frequency control (LFC) for grid stability. A hybrid PSO- ANN framework is proposed for DSM to predict and optimize energy consumption patterns, while ANFIS-based controllers are designed for robust LFC under dynamic load conditions. Simulation results demonstrate superior performance compared to conventional methods, with significant improvements in demand response accuracy and frequency regulation. The findings underscore the potential of AI-driven optimization in advancing smart grid resilience and operational efficiency
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    Load Frequency Control in Two Area Power Systems in A Smart Grid Environment
    (IEEE, 2024) Faradji, Mohamed; Madani Layadi, Toufik; Ilhami, Colak
    Load Frequency Control (LFC) is a critical aspect of power system stability, ensuring that the frequency and tieline power flow remains within acceptable limits. In this paper, we investigate LFC in a two-area system with the integration of demand response (DR) loops. The DR loops allow for dynamic adjustments of load demand based on real-time system conditions. Our study focuses on optimizing the proportional-integral-derivative (PID) controller used in the LFC system. To achieve this, we perform a comparative analysis of three optimization algorithms: Artificial bee colony (ABC), particle swarm optimization (PSO), and Aquila Optimization (AO). These algorithms are applied to tune the PID controller parameters, aiming to enhance system performance, reduce frequency deviations, and minimize control efforts. Simulation results demonstrate the effectiveness of the proposed approach. The optimized PID controller, combined with DR, significantly improves system response during load disturbances. Furthermore, the comparative study sheds light on the strengths and weaknesses of each optimization algorithm, providing valuable insights for future LFC implementations. Overall, our work contributes to the advancement of LFC strategies in interconnected power systems, emphasizing the role of demand response and optimization techniques in achieving robust and efficient control