Our electricity infrastructure is getting more complex and heterogeneous. Holistically analyzing grids is therefore increasingly challenging. Co-simulation, i.e. the coordinated execution of independent subsystem simulators, is inherently well suited to handling these challenges. However, the computational needs of calculating power flows within the simulated grid may limit the scalability for large-scale co-simulations. Recent advances in quantum computing offer a potential solution to these concerns: The computing paradigm‘s potential for exponentially speeding up power flow has been shown. To utilize these capabilities for smart grid simulations, we propose quantum–classical co-simulation: integrating simulators running on quantum hardware with an otherwise classical co-simulation. Specifically, we focus on exploiting quantum power flow in smart grid co-simulations. This concept is promising for applications that require comprehensive grid simulation and whose scalability is impeded by the computational properties of power flow. This paper highlights the concept of quantum–classical co-simulation, and advocates for its criticality and applications in supporting smart grid analytics. We encourage and facilitate research by recommending a five-item research roadmap. We also provide a detailed discussion on the potential obstacles in implementing this concept, to help bring its theoretical value to practice.
Our electricity infrastructure is getting more complex and heterogeneous. Holistically analyzing grids is therefore increasingly challenging. Co-simulation, i.e. the coordinated execution of independent subsystem simulators, is inherently well suited to handling these challenges. However, the computational needs of calculating power flows within the simulated grid may limit the scalability for large-scale co-simulations. Recent advances in quantum computing offer a potential solution to these concerns: The computing paradigm‘s potential for exponentially speeding up power flow has been shown. To utilize these capabilities for smart grid simulations, we propose quantum–classical co-simulation: integrating simulators running on quantum hardware with an otherwise classical co-simulation. Specifically, we focus on exploiting quantum power flow in smart grid co-simulations. This concept is promising for applications that require comprehensive grid simulation and whose scalability is impeded by the computational properties of power flow. This paper highlights the concept of quantum–classical co-simulation, and advocates for its criticality and applications in supporting smart grid analytics. We encourage and facilitate research by recommending a five-item research roadmap. We also provide a detailed discussion on the potential obstacles in implementing this concept, to help bring its theoretical value to practice.
Abstract With the rising complexity of our electricity infrastructure, smart grid simulations increasingly rely on co-simulation, which involves jointly executing independent subsystem simulations. However, in large-scale simulation scenarios, such as those involving costly power-flow analysis, co-simulation may experience computational-performance issues. Quantum computing offers a potential solution through quantum–classical co-simulation, in which one or more simulators of an otherwise classical co-simulation are executed on quantum hardware. However, there is no practical realization of this concept that establishes its feasibility. To address this gap, we integrate a quantum power flow simulator with a smart grid co-simulation and conduct an exploratory simulation study using a fictitious case-study scenario. The experiments demonstrate the feasibility of quantum–classical co-simulation; at the same time, they highlight four obstacles to the concept’s realization in practice: (1) To use quantum computing for co-simulation, session-based scheduling is required. (2) Distributed simulation limits possible applications and requires proximity of computing resources. (3) For the efficient extraction of classical information from the quantum states, we need carefully designed operators. (4) Current hardware limitations—such as noise susceptibility and the lack of quantum random access memory—limit practical near-term uses of quantum power flow; therefore, attention should be turned to alternative applications that are more promising in the near term. These findings pave the way for future research on quantum–classical co-simulation and its potential applications in smart grids.
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.
The increasing complexity of modern energy systems—driven by distributed renewable generation, dynamic grid behavior, and multi-scale interactions—necessitates computational models capable of capturing nonlinearities, stochastic variability, and high-dimensional optimization landscapes. Embedding-based quantum simulations offer a promising path forward by integrating quantum solvers into classical energy modeling frameworks, enabling more accurate analysis of localized subsystems while maintaining tractable large-scale system evaluations. This study investigates quantum embedding techniques applied to power flow analysis, renewable integration, grid stability assessment, and energy storage optimization. By partitioning complex energy networks into quantum-treated active regions embedded within classical architectures, the approach enhances the fidelity of system-level simulations while reducing computational overhead. The research highlights the potential of hybrid quantum–classical workflows to accelerate decision-making in smart grid design, improve forecasting under uncertainty, and support the development of sustainable engineering solutions. The results outline a roadmap for deploying quantum-embedded simulations in future energy infrastructures.
Grid modernization efforts with the latest information and communication technologies will significantly benefit smart grids in the coming years. More optical fibre communications between consumers and the control center will promise better demand response and customer engagement, yet the increasing attack surface and man-in-the-middle (MITM) threats can result in security and privacy challenges. Among the studies for more secure smart grid communications, quantum key distribution protocols (QKD) have emerged as a promising option. To bridge the theoretical advantages of quantum communication to its practical utilization, however, comprehensive investigations have to be conducted with realistic cyber-physical smart grid structures and scenarios. To facilitate research in this direction, this paper proposes an open-source, research-oriented co-simulation platform that orchestrates cyber and power simulators under the MOSAIK framework. The proposed platform allows flexible and realistic power flow-based co-simulation of quantum communications and electrical grids, where different cyber and power topologies, QKD protocols, and attack threats can be investigated. Using quantum-based communication under MITM attacks, the paper presented detailed case studies to demonstrate how the platform enables quick setup of a lowvoltage distribution grid, implementation of different protocols and cryptosystems, as well as evaluations of both communication efficiency and security against MITM attacks. The platform has been made available online to empower researchers in the modelling of quantum-based cyber-physical systems, pilot studies on quantum communications in smart grid, as well as improved attack resilience against malicious intruders.
The computational demands of modern power systems are increasing rapidly, posing significant challenges to conventional computing approaches. Traditional methods may not be sufficient to handle the scale and complexity of future power grid operations. In this context, quantum computing (QC) emerges as a potential next-generation solution to address these limitations. Empowered with the quantum mechanics principles such as qubits, superposition and entanglement, QC can be used to efficiently handle the complex computation problems of large-scale grids. Building on this potential, our paper reviews the existing research done in regard to QC applications in smart grids and classifies them on the basis of three core QC's advantages namely - optimization, big data forecasting and security. In the end, we discuss the current challenges associated with employing QC in smart grids.
Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today's power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.