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Advanced Energy Management in a Sustainable Integrated Hybrid Power Network Using a Computational Intelligence Control Strategy

Author

Listed:
  • Muhammad Usman Riaz

    (Department of Electrical and Computer Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Suheel Abdullah Malik

    (Department of Electrical and Computer Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Amil Daraz

    (College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
    MEU Research Unit, Middle East University, Amman 11831, Jordan)

  • Hasan Alrajhi

    (Department of Electrical Engineering, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

  • Ahmed N. M. Alahmadi

    (Department of Electrical Engineering, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

  • Abdul Rahman Afzal

    (Department of Industrial Engineering, University of Business and Technology (UBT), Jeddah 23847, Saudi Arabia)

Abstract

The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods.

Suggested Citation

  • Muhammad Usman Riaz & Suheel Abdullah Malik & Amil Daraz & Hasan Alrajhi & Ahmed N. M. Alahmadi & Abdul Rahman Afzal, 2024. "Advanced Energy Management in a Sustainable Integrated Hybrid Power Network Using a Computational Intelligence Control Strategy," Energies, MDPI, vol. 17(20), pages 1-53, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5040-:d:1495976
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    References listed on IDEAS

    as
    1. Ramdhan Halid Siregar & Yuwaldi Away & Tarmizi & Akhyar, 2023. "Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods," Energies, MDPI, vol. 16(14), pages 1-25, July.
    2. Ahmed M. Nassef & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Ahmad Baroutaji, 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    3. Izhar Us Salam & Muhammad Yousif & Muhammad Numan & Kamran Zeb & Moatasim Billah, 2023. "Optimizing Distributed Generation Placement and Sizing in Distribution Systems: A Multi-Objective Analysis of Power Losses, Reliability, and Operational Constraints," Energies, MDPI, vol. 16(16), pages 1-28, August.
    4. Elseify, Mohamed A. & Hashim, Fatma A. & Hussien, Abdelazim G. & Kamel, Salah, 2024. "Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems," Applied Energy, Elsevier, vol. 353(PA).
    5. Abdulaziz Alanazi & Tarek I. Alanazi, 2023. "Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
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