Quantum computing has emerged as a promising computational paradigm to address unresolved challenges in the modeling and control of modern power systems. However, most existing studies focus on offline simulations, and a practical framework for validating quantum algorithms in real-time operational environments remains lacking. This study proposes a quantum hardware-in-the-loop framework that integrates a real-time digital simulator with quantum and quantum-inspired hardware to solve combinatorial power flow and optimal power flow formulations under dynamic operating conditions. The proposed framework is validated using the IEEE 9- bus test system and a modified version with integrated solar and wind farms. The results confirm successful integration and convergence within a predefined tolerance. The study also identifies key limitations and challenges, such as limited access to quantum and digital annealers and current scalability limitations, that must be considered in future developments. Nevertheless, the results highlight the potential of quantum computing to significantly enhance the modeling and control of future power systems with high penetration of renewable energy sources.
Quantum computing has emerged as a promising computational paradigm to address unresolved challenges in the modeling and control of modern power systems. However, most existing studies focus on offline simulations, and a practical framework for validating quantum algorithms in real-time operational environments remains lacking. This study proposes a quantum hardware-in-the-loop framework that integrates a real-time digital simulator with quantum and quantum-inspired hardware to solve combinatorial power flow and optimal power flow formulations under dynamic operating conditions. The proposed framework is validated using the IEEE 9- bus test system and a modified version with integrated solar and wind farms. The results confirm successful integration and convergence within a predefined tolerance. The study also identifies key limitations and challenges, such as limited access to quantum and digital annealers and current scalability limitations, that must be considered in future developments. Nevertheless, the results highlight the potential of quantum computing to significantly enhance the modeling and control of future power systems with high penetration of renewable energy sources.
Quantum Green Computing represents a transformative paradigm that merges the power of quantum computational techniques with the principles of sustainability to drive innovation in renewable resource management.This paper investigates the interplay between quantum algorithms, energy-efficient system architectures, and advanced renewable energy technologies-including solar photovoltaics, wind energy systems, and bioenergy solutions.By leveraging the unique capabilities of quantum computing, such as superior optimization, high-dimensional modeling, and rapid problem-solving, the study explores novel strategies for addressing the inherent complexities of renewable energy integration.The research emphasizes three core dimensions: (1) the application of quantum optimization algorithms to enhance grid stability, load balancing, and real-time decision-making; (2) the development of energy-efficient quantum-inspired hardware architectures that align with green computing objectives; and (3) the formulation of predictive and adaptive models for renewable resource allocation using quantum-inspired approaches.Furthermore, the paper highlights case studies that demonstrate how quantum techniques can significantly improve grid management, predictive maintenance of renewable infrastructures, and long-term forecasting of resource availability under dynamic environmental conditions.By critically evaluating these emerging pathways, the study outlines a vision where quantum-enabled sustainability not only reduces carbon emissions but also maximizes energy utilization efficiency.This integration holds the potential to revolutionize the global renewable energy landscape, enabling resilient, lowcarbon ecosystems capable of supporting the growing demands of a sustainable future.
The rapid proliferation of grid-edge distributed energy resources has significantly increased the operational complexity of modern power systems. Consequently, conventional computational techniques face growing scalability and computational-efficiency challenges in addressing large-scale optimization and control, uncertainty management, nonlinear dynamics, and combinatorial decision-making in smart grid operations. Quantum computing has therefore emerged as a promising computational paradigm that can complement classical methods in addressing selected computationally intensive problems. In this context, this paper presents a comprehensive structured review of quantum computing applications in smart grid operations. Following a transparent keyword-based literature search, the paper classifies and assesses existing studies across monitoring and estimation, system planning, operation and control, security, reliability and resilience, stability assessment, data-driven intelligence, and digital twin technologies. The paper also describes fundamental quantum-computing concepts and key algorithms, highlighting their relevance for power system applications. Furthermore, it reviews the current state of quantum hardware, software frameworks, simulators, cloud services, and emerging hardware-agnostic ecosystems that support cross-platform application development and deployment. The reviewed studies are examined by implementation environment, benchmarking practices, application scale, and evidence of computational advantage. Finally, the principal challenges associated with practical implementation are discussed, and future research directions are outlined. This paper provides a consolidated and evidence-calibrated perspective on the current state and future potential of quantum computing for next-generation smart grid operations.
Quantum computing has the potential to solve many computational problems exponentially faster than classical computers. The high shares of renewables and the wide deployment of converter-interfaced resources require new tools that shall drastically accelerate power system computations, including optimization and security assessment, which can benefit from quantum computing. To the best of our knowledge, this is the first paper that goes beyond quantum computing simulations and performs an experimental application of Quantum Computing for power systems on a real quantum computer. We use five different quantum computers, apply the HHL quantum algorithm, and examine the impact of current noisy quantum hardware on the accuracy and speed of an AC power flow algorithm. We perform the same studies on a 3-bus and a 5-bus system with real quantum computers to identify challenges and open research questions related with the scalability of these algorithms.
This research investigates the transformative potential of Quantum Power Flow (QPF) in power systems analysis. Leveraging quantum computing principles, the study explores quantum algorithms for power flow simulations to enhance computational efficiency andsolution accuracy. The Quantum Power Flow method is introduced, addressing challenges arising from the growing complexity of power systems, especially with increased renewable energy integration. The mathematical foundation of the Quantum Power Flow method is detailed, emphasizing quantum parallelism and computational advantages. Experimental validation, simulations, and research case studies collectively establish the practical viability and versatility of the proposed quantum approach. A 5 and 3-bus power system case study illustrates its applicability.Acknowledging current quantum computing challenges, including error susceptibility, the study highlights ongoing advancements and error correction mechanisms' potential to overcome limitations. The research concludes by discussing broader implications of quantum power system analysis and outlining future research avenues. In summary, this research comprehensively explores Quantum Power Flow, offering theoretical insights and practical demonstrations. As quantum computing evolves, this study contributes to the paradigm shift in power system analysis, opening new horizons for research and application..
This study introduces a hybrid quantum-classical dispatching framework designed for power systems with high renewable penetration. The proposed method integrates a variational quantum algorithm with classical optimization to provide noise-resilient performance under realistic hardware constraints. Extensive numerical tests and a real-world case study demonstrate significant improvements in cost reduction, dispatch reliability, and robustness to device noise. The approach highlights the potential of near-term quantum computing to address critical challenges in renewable energy integration. The results bridge the gap between quantum algorithms and practical energy system operations, offering a pathway for sustainable and efficient power system management.
Modern distribution grids face unprecedented challenges from intermittent renewable integration, extreme weather events, and the need for real-time optimal power flow (OPF) solutions that preserve physical constraints while minimizing operational costs. This paper introduces Constraint-Aware QAOA for Real-Time Quantum Smart Grid Optimization, a novel hybrid quantum-classical framework that directly addresses renewable dispatch, battery scheduling, and demand response in distribution networks. We formulate the stochastic OPF problem with renewable integration as a constrained optimization challenge and propose three innovative solution approaches: (1) Constraint-Preserving QAOA (CP-QAOA) with a novel Power-Flow Preserving Mixer (PF-Mixer) that enforces Kirchhoff-style flow conservation during quantum evolution, (2) Phase-Encoded Power-Flow (PEPF) mapping that reduces qubit requirements by 3× through mixed binary-phase representation, and (3) Hybrid Receding-Horizon Quantum Controller (HRQC) enabling subminute reoptimization via intelligent problem decomposition. For renewable integration modeling, we develop multimodal fusion techniques combining real-time generation forecasts, battery state-of-charge dynamics, and demand response signals with quantum superposition states. Our experimental evaluation on IEEE test systems with realistic renewable penetration scenarios demonstrates significant improvements: the CP-QAOA approach achieves 18.3% reduction in operational costs compared to classical OPF baselines, while quantum methods provide > 95% constraint feasibility compared to < 31% for standard QAOA with penalty encoding. The hybrid framework shows strong promise for scalable deployment in real smart grid infrastructure, with practical implications for quantum-enhanced grid operations under high renewable penetration.