Quantum Computing as a Catalyst for Microgrid Management: Enhancing Decentralized Energy Systems Through Innovative Computational Techniques

Minghong Liu, Mengke Liao, Ruilong Zhang, Xin Yuan, Zhaoqun Zhu and Zhi Wu ()
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Minghong Liu: State Grid Xinjiang Economic Research Institute, Urumqi 830063, China
Mengke Liao: State Grid Xinjiang Economic Research Institute, Urumqi 830063, China
Ruilong Zhang: State Grid Xinjiang Economic Research Institute, Urumqi 830063, China
Xin Yuan: State Grid Xinjiang Economic Research Institute, Urumqi 830063, China
Zhaoqun Zhu: State Grid Xinjiang Economic Research Institute, Urumqi 830063, China
Zhi Wu: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Sustainability, 2025, vol. 17, issue 8, 1-27

Abstract: This paper introduces a groundbreaking framework for optimizing microgrid operations using the Quantum Approximate Optimization Algorithm (QAOA). The increasing integration of decentralized energy systems, characterized by their reliance on renewable energy sources, presents unique challenges, including the stochastic nature of energy supply-and-demand management. Our study leverages quantum computing to enhance the operational efficiency and resilience of microgrids, transcending the limitations of traditional computational methods. The proposed QAOA-based model formulates the microgrid scheduling problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, suitable for quantum computation. This approach not only accommodates complex operational constraints—such as energy conservation, peak load management, and cost efficiency—but also dynamically adapts to the variability inherent in renewable energy sources. By encoding these constraints into a quantum-friendly Hamiltonian, QAOA facilitates a parallel exploration of multiple potential solutions, enhancing the probability of reaching an optimal solution within a feasible time frame. We validate our model through a comprehensive simulation using real-world data from a microgrid equipped with photovoltaic systems, wind turbines, and energy storage units. The results demonstrate that QAOA outperforms conventional optimization techniques in terms of cost reduction, energy efficiency, and system reliability. Furthermore, our study explores the scalability of quantum algorithms in energy systems, providing insights into their potential to handle larger, more complex grid architectures as quantum technology advances. This research not only underscores the viability of quantum algorithms in real-world applications but also sets a precedent for future studies on the integration of quantum computing into energy management systems, paving the way for more sustainable, efficient, and resilient energy infrastructures.

Keywords: quantum computing; microgrid optimization; decentralized energy systems; quantum approximate optimization algorithm (QAOA); sustainable energy management; energy system resilience (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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