Quantum-Inspired Optimization Models for Renewable Energy Scheduling and Grid Stability

  • Authors

    • Klaus Samelson Professor of Computer Science, Technical University of Munich, Germany. Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2024PI5G2W

    Published 05-04-2024

  • Quantum-Inspired Optimization, Renewable Energy Scheduling, Smart Grid Stability, Energy Storage Systems, Evolutionary Computation, Power System Optimization, Renewable Uncertainty Management, Intelligent Energy Management

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    Quantum-Inspired Optimization Models for Renewable Energy Scheduling and Grid Stability. (2024). International Journal of Modern Research in Science & Engineering, 7(1), 01-15. https://doi.org/10.67228/30716357/IJMRSE-2024PI5G2W

  • Abstract

    The growing integration of renewable energy sources creates challenges in energy scheduling and grid stability. This paper presents a quantum-inspired optimization framework for efficient renewable energy scheduling under uncertainty. The proposed model optimizes energy dispatch by considering renewable forecasting, battery storage, load variation, and grid constraints while minimizing operating cost, renewable curtailment, voltage deviation, and frequency fluctuations. The framework combines quantum-inspired solution encoding, adaptive evolutionary search, and stability-aware optimization to improve scheduling performance. Simulation results demonstrate superior renewable utilization, reduced scheduling errors, enhanced grid stability, and higher computational efficiency compared with conventional optimization methods such as GA and PSO. The proposed approach provides a scalable foundation for future intelligent and autonomous renewable energy management systems.

  • References

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