A quantum-classical hybrid framework for optimal energy storage systems planning
- 1Power System Resilience, Oak Ridge National Laboratory
- 2IonQ Inc.
Abstract
The extensive deployment of power-electronics introduce spatial-temporal variability that can degrade voltage quality and operational reliability. Energy storage systems (ESS) can mitigate these effects through fast active and reactive power support, but their value is contingent on coordinated siting and sizing. Integrated formulations that minimize voltage deviations, reduce substation power-flow variability, and account for installation costs typically yield in large-scale mixed-integer optimization problems that are computationally burdensome for classical solvers and may yet not lead to the most optimum solution. To address these challenges, this paper proposes a two-stage hybrid quantum–classical planning framework that separates binary siting from continuous sizing and operation. In Stage I, the siting problem is reformulated as a Quadratic Unconstrained Binary Optimization model and solved via a hybrid quantum workflow. Acting as a “quantum sieve,” stochastic sampling generates a diverse set of candidate site combinations that classical single-point methods can overlook. In Stage II, selected site sets are evaluated using a classical convex solver (SOCP) to compute optimal ESS capacities and operating setpoints subject to network constraints, ensuring physical feasibility. Experiments on IonQ Forte hardware show grid-standard accuracy with industry-standard classical solvers. Although current hardware latencies limit performance in the NISQ era, the paper outlines scaling pathways and discusses key practical hurdles, including state-preparation overlap and higher-order cost couplings.
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BibTeX
@article{hasan2026quantumclassical,
title = {{A quantum-classical hybrid framework for optimal energy storage systems planning}},
author = {Md Shamim Hasan and Willie Aboumrad and Phani R. V. Marthi and Evgeny Epifanovsky and George Siopsis and Martin Roetteler and Suman Debnath},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62843-2},
url = {https://doi.org/10.1038/s41598-026-62843-2},
note = {CC BY},
}
RIS
TY - JOUR
TI - A quantum-classical hybrid framework for optimal energy storage systems planning
AU - Md Shamim Hasan
AU - Willie Aboumrad
AU - Phani R. V. Marthi
AU - Evgeny Epifanovsky
AU - George Siopsis
AU - Martin Roetteler
AU - Suman Debnath
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62843-2
UR - https://doi.org/10.1038/s41598-026-62843-2
AB - The extensive deployment of power-electronics introduce spatial-temporal variability that can degrade voltage quality and operational reliability. Energy storage systems (ESS) can mitigate these effects through fast active and reactive power support, but their value is contingent on coordinated siting and sizing. Integrated formulations that minimize voltage deviations, reduce substation power-flow variability, and account for installation costs typically yield in large-scale mixed-integer optimization problems that are computationally burdensome for classical solvers and may yet not lead to the most optimum solution. To address these challenges, this paper proposes a two-stage hybrid quantum–classical planning framework that separates binary siting from continuous sizing and operation. In Stage I, the siting problem is reformulated as a Quadratic Unconstrained Binary Optimization model and solved via a hybrid quantum workflow. Acting as a “quantum sieve,” stochastic sampling generates a diverse set of candidate site combinations that classical single-point methods can overlook. In Stage II, selected site sets are evaluated using a classical convex solver (SOCP) to compute optimal ESS capacities and operating setpoints subject to network constraints, ensuring physical feasibility. Experiments on IonQ Forte hardware show grid-standard accuracy with industry-standard classical solvers. Although current hardware latencies limit performance in the NISQ era, the paper outlines scaling pathways and discusses key practical hurdles, including state-preparation overlap and higher-order cost couplings.
SN - 2045-2322
ER -