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Nested Optimization Algorithms for Accurately Sizing a Clean Energy Smart Grid System, Considering Uncertainties and Demand Response

Author

Listed:
  • Ali M. Eltamaly

    (Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Zeyad A. Almutairi

    (Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia
    Mechanical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

Driven by environmental concerns and dwindling fossil fuels, a global shift towards renewable energy for electricity generation is underway, with ambitions for complete reliance by 2050. However, the intermittent nature of renewable power creates a supply–demand mismatch. This challenge can be addressed through smart grid concepts that utilize demand-side management, energy storage systems, and weather/load forecasting. This study introduces a sizing technique for a clean energy smart grid (CESG) system that integrates these strategies. To optimize the design and sizing of the CESG, two nested approaches are proposed. The inner approach, “Optimal Operation,” is performed hourly to determine the most efficient operation for current conditions. The outer approach, “Optimal Sizing,” is conducted annually to identify the ideal size of grid components for maximum reliability and lowest cost. The detailed model incorporating component degradation predicted the operating conditions, showing that real-world conditions would make the internal loop computationally expensive. A lotus effect optimization algorithm (LEA) that demonstrated superior performance in many applications is utilized in this study to increase the convergence speed. Although there is a considerable reduction in the convergence time when using a nested LEA (NLEA), the convergence time is still long. To address this issue, this study proposes replacing the internal LEA loop with an artificial neural network, trained using data from the NLEA. This significantly reduces computation time while maintaining accuracy. Overall, the use of DR reduced the cost by about 28% compared with avoiding the use of DR. Moreover, the use of NLEA reduced the convergence time of the sizing problem by 43% compared with the best optimization algorithm used for comparison. The replacement of the inner LEA optimization loop reduced the convergence time of sizing the CESG to 1.08%, compared with the NLEA performance.

Suggested Citation

  • Ali M. Eltamaly & Zeyad A. Almutairi, 2025. "Nested Optimization Algorithms for Accurately Sizing a Clean Energy Smart Grid System, Considering Uncertainties and Demand Response," Sustainability, MDPI, vol. 17(6), pages 1-37, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2744-:d:1616023
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    References listed on IDEAS

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