Quantum Computing technology is currently prevailing as a promising candidate for expanding the research horizons in the areas of power engineering and transportation. An across-the-board view of this technology identifies that the intersecting research of interest which covers both areas is the identification of innovative energy storage technologies. Quantum Computers (QC) provide the capability to develop an innovative energy storage system, but its operating principles encompass the domains of Quantum Physics (QP) and Quantum Mechanics (QM) and thus limit the understanding of its underlying functionality. The applicable storage system under research using QC, termed Quantum Battery (QB) is considered to be a theoretical construct, which means that it is not directly observable. This paper attempts to piece together the QM and its applications in a QC, directed towards the identification of an innovative QB, by providing a review of the existing researches. Also, the explanations of various postulates and their corresponding formulations leading to the QM framework behind a QB is presented. This is followed by a review of the underlying operation of a QC and its application in performing battery research. A summary of the existing QB researches is also presented which are categorized into particle- and chemistry-driven (quantum) battery researches.
Quantum Computing technology is currently prevailing as a promising candidate for expanding the research horizons in the areas of power engineering and transportation. An across-the-board view of this technology identifies that the intersecting research of interest which covers both areas is the identification of innovative energy storage technologies. Quantum Computers (QC) provide the capability to develop an innovative energy storage system, but its operating principles encompass the domains of Quantum Physics (QP) and Quantum Mechanics (QM) and thus limit the understanding of its underlying functionality. The applicable storage system under research using QC, termed Quantum Battery (QB) is considered to be a theoretical construct, which means that it is not directly observable. This paper attempts to piece together the QM and its applications in a QC, directed towards the identification of an innovative QB, by providing a review of the existing researches. Also, the explanations of various postulates and their corresponding formulations leading to the QM framework behind a QB is presented. This is followed by a review of the underlying operation of a QC and its application in performing battery research. A summary of the existing QB researches is also presented which are categorized into particle- and chemistry-driven (quantum) battery researches.
Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy generations, varying nature loads, importance of active role of distribution system and consumer participation in grid operation has changed the landscape of classical power grids. Implementation of smarter applications to plan, monitor, operate the grid safely are deemed paramount for efficient, secure and reliable functioning of grid. These smarter applications for modern power systems demand capabilities such as real-time monitoring, dynamic security analysis, stochastic power flow calculations, large-scale data analytics, and high-dimensional combinatorial and constrained optimization. Although sophisticated computations to process gigantic volume of data to produce useful information in a time critical manner is the paradigm of future grid operations, these enhanced functionalities impose significantly higher computational demands compared to traditional approaches used in conventional power systems. Advancements in quantum technologies holds promising solution for dealing with demanding computational complexity of power system related applications. In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next, we present the fundamental principles of quantum computing, introduce key quantum algorithms, and offer a comparison of the computational load between classical and quantum approaches for few important mathematical problems, indicating their relevance to various power system applications. Additionally, we provide an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. To this end, this article serves as a bridge for power engineers to the quantum domain by outlining fundamental principles of quantum computation, facilitating a smoother transition to the future of power system computations and also provides quantum experts with insights into new application areas for quantum computing within power systems.
Abstract Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next‐generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state‐of‐art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real‐world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis.
Quantum mechanics (QM) forms the basis for quantum computation (QC), a paradigm that provides distinctive problem-solving abilities. Although quantum computing (QC) has demonstrated potential in theoretical physics and computer science, its practical applications, particularly in power systems, have not been thoroughly studied. The objective of this work is to highlight the use of quantum computing (QC) as a solution for addressing the rising computational difficulties in power systems. The text provides an introduction to the historical history and essential concepts of QC, and then explores its modern applications in solving intricate power system problems. The aforementioned subjects comprise the study of alternating current (AC) and direct current (DC)” power flow, contingency analysis, state estimation, modeling of electromagnetic transients, fault diagnostics, unit commitment, and challenges related to facility location-allocation.” Despite the existing limits of noisy intermediate-scale quantum (NISQ) devices, the potential of quantum computing to address significant power system concerns on a wide scale holds promise, mostly driven by anticipated advancements in technology and increased research endeavors.
Lithium-ion batteries (LIBs) are critical in modern energy storage systems, powering everything from portable electronics to electric vehicles. However, optimizing their performance and longevity remains a significant challenge. Quantum simulation has emerged as a promising tool to model the complex electrochemical processes within LIBs, offering insights into charging mechanisms and degradation pathways th'at classical methods struggle to capture. This paper presents a systematic literature review of quantum simulation techniques applied to LIBs, focusing on charging and degradation modeling. We analyze the current state-of-the-art, identify key techniques, and discuss the potential of quantum computing to revolutionize battery research. Our findings highlight the advantages of quantum simulations in capturing quantum mechanical and quantum chemistry effects which are critical for accurate battery modeling. Trends in the literature suggest a move toward algorithm optimization, integration with classical methods, and the development of quantum-inspired techniques.
Abstract Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm has brought QC much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era—the next logical step being the quantum advantage whereby quantum computers solve a real‐world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. QC is a highly topical and fast‐moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on QC will be invaluable to understand the state‐of‐the‐art of this emerging field and identify open challenges for the QC community to address in the coming years. This article presents a comprehensive review of QC literature and proposes taxonomy of QC. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post‐quantum cryptography, and quantum computer hardware development captures the current state‐of‐the‐art in the respective areas. The article identifies and highlights various open challenges and promising future directions for research and innovation in QC.
The aim of this paper is to report on the state of the art of the literature on the most recent challenges in the energy domain that can be addressed through the use of quantum computing technology. More in detail, to the best of the authors’ knowledge, the scope of the literature review considered in this paper is specifically limited to forecasting, grid management (namely, scheduling, dispatching, stability, and reliability), battery production, solar cell production, green hydrogen and ammonia production, and carbon capture. These challenges have been identified as the most relevant business needs currently expressed by energy companies on their path towards a net-zero economy. A critical discussion of the most relevant methodological approaches and experimental setups is provided, together with an overview of future research directions. Overall, the key finding of the paper, based on the proposed literature review, is twofold: namely, (1) quantum computing has the potential to trigger significant transformation in the energy domain by drastically reducing CO2 emissions, especially those relative to battery production, solar cell production, green hydrogen and ammonia production, as well as point-source and direct-air carbon capture technology; and (2) quantum computing offers enhanced optimization capability relative to relevant challenges that concern forecasting solar and wind resources, as well as managing power demand, facility allocation, and ensuring reliability and stability in power grids.