Data-driven quantum approximate optimization algorithm for power systems

Jing, Hang; Wang, Ye; Li, Yan

doi:10.1038/s44172-023-00061-8

Cited by 15 publications

(2 citation statements)

References 46 publications

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“…This duration is not feasible for specific applications, such as stochastic optimal power flow 25 , where a large number of calculations should be completed within a few minutes. Consequently, the power systems community is increasingly interested in exploring novel approaches, including quantum computing [26][27][28] , which have shown promising results in various applications and are, therefore, expected to revolutionize the field of PF analysis in the near future 29 .…”

Section: /15mentioning

confidence: 99%

Adiabatic Quantum Power Flow

Kaseb,

Moller,

Vergara

et al. 2024

Preprint

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Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and (ii) an adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (iii) a scalability study of the AQPF algorithm; and (iv) an extension of the AQPF algorithm for larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.

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Section: /15mentioning

confidence: 99%

Adiabatic Quantum Power Flow

Kaseb,

Moller,

Vergara

et al. 2024

Preprint

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“…Quantum approaches, including quantum annealing and Quantum Approximate Optimization Algorithms (QAOA), exhibit promise. Quantum annealing directly encodes problems into the quantum system's energy landscape [23], while QAOA integrates classical optimization with quantum states to discover optimal solutions [24]. The potential computational advantages of these quantum approaches over traditional heuristic methods necessitate further research for a comprehensive understanding of their effectiveness.…”

Section: Taking Advantage Of Quantum Computing On Scheduling and Disp...mentioning

confidence: 99%

Quantum Computing as a Game Changer on the Path towards a Net-Zero Economy: A Review of the Main Challenges in the Energy Domain

Ricciardi Celsi,

Ricciardi Celsi

2024

Energies

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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.

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