Hybrid Quantum-Classical Optimization Algorithms for Energy-Efficient Smart Grids

Milad Rahmati

Abstract


The efficient management of energy resources in modern smart grids is becoming increasingly critical due to growing energy demands and the need for sustainability. To address these challenges, this study introduces a novel hybrid optimization approach that combines quantum computing techniques with classical algorithms. By leveraging the strengths of Variational Quantum Algorithms (VQAs) alongside traditional optimization methods for preprocessing and postprocessing, the proposed framework offers an effective solution to complex combinatorial problems inherent in smart grid operations. Experimental evaluations on simulated grid models demonstrate significant improvements in energy efficiency—up to 25%—compared to conventional optimization techniques. This work highlights the transformative potential of quantum computing in advancing the operational efficiency of energy systems and ensuring scalability for future smart grid applications.


Keywords


Quantum computing; hybrid optimization; smart grid; energy management; quantum-classical algorithms; variational quantum algorithms; combinatorial optimization; sustainable energy systems

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DOI: http://dx.doi.org/10.5281/zenodo.14929332

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