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Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Author

Listed:
  • Ahmad Alzahrani

    (Electrical Engineering Department, College of Engineering, Najran University, Najran 11001, Saudi Arabia)

  • Ghulam Hafeez

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Sajjad Ali

    (Department of Telecommunication Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Sadia Murawwat

    (Department of Electrical Engineering, Lahore College for Women University, Lahore 51000, Pakistan)

  • Muhammad Iftikhar Khan

    (Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Khalid Rehman

    (Department of Electrical Engineering, CECOS University of IT and Emerging Sciences, Peshawar 25100, Pakistan)

  • Azher M. Abed

    (Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon 51001, Iraq)

Abstract

Multi-objective energy optimization is indispensable for energy balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective optimization is challenging due to uncertainty and multi-conflicting parameters at both the generation and demand sides. Thus, opting for a model that can solve load and distributed energy source scheduling problems is necessary. This work presents a model for operation cost and pollution emission optimization with renewable generation in the SPG. Solar photovoltaic and wind are renewable energy which have a fluctuating and uncertain nature. The proposed system uses the probability density function (PDF) to address uncertainty of renewable generation. The developed model is based on a multi-objective wind-driven optimization (MOWDO) algorithm to solve a multi-objective energy optimization problem. To validate the performance of the proposed model a multi-objective particle swarm optimization (MOPSO) algorithm is used as a benchmark model. Findings reveal that MOWDO minimizes the operational cost and pollution emission by 11.91% and 6.12%, respectively. The findings demonstrate that the developed model outperforms the comparative models in accomplishing the desired goals.

Suggested Citation

  • Ahmad Alzahrani & Ghulam Hafeez & Sajjad Ali & Sadia Murawwat & Muhammad Iftikhar Khan & Khalid Rehman & Azher M. Abed, 2023. "Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9970-:d:1177250
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    References listed on IDEAS

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