Quantum-AI optimization of hybrid thermal-to-electric energy conversion systems for sustainable power infrastructure

ORAL

Abstract

Emerging quantum and AI methodologies are introducing new possibilities for modeling, optimization, and control of hybrid thermal-to-electric energy conversion systems. This work presents an integrated quantum-AI framework for optimizing thermoelectric, thermoelectrochemical, and thermophotovoltaic processes within sustainable power networks. Developed by Resilient Entanglement Inc., a UN Science-Based Targets initiative (SBTi)–approved company and a signatory of the UN Women's Empowerment Principles, reflecting its dual commitment to climate action and gender equality. Its work represents a critical step toward reducing greenhouse gas emissions, ensuring U.S. and global energy security, and building climate-resilient power infrastructure while reinforcing international cooperation at the intersection of technology, sustainability, and policy.

Ab initio–inspired QUBO formulations and quantum approximate optimization algorithms (QAOA) are employed to model coupled transport phenomena and minimize conversion losses across multi-material architectures. Simulations performed on hardware demonstrate improved runtime efficiency and robust convergence under stochastic noise.

Resilient Entanglement Inc. is the first worldwide women-owned company in the quantum and AI smart grid field. The company is a proud participant of the UN Global Compact and an official partner of UNESCO's International Year of Quantum (2025), demonstrating its deep alignment with the UN Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). The company also holds a family portfolio of 12 quantum and AI energy innovation patents.

*This work was supported in part by the Colorado Office of Economic Development and International Trade (OEDIT) Advanced Industries Accelerator Grant (CTGG1 2023-3346), the U.S. Department of Energy (DOE) Oak Ridge National Laboratory (ORNL) Quantum Energy Systems collaboration, and Eaton Research Laboratories (CARE project). Additional support was provided through Resilient Entanglement Inc.'s internal R&D program focused on quantum-AI solutions for sustainable power systems.

–

Publication: T. Christeson, A. Khodaei, R. Eskandarpour, Quantum-Compatible Unit Commitment Modeling through Logarithmic Discretization, 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), pp. 1–5, 2024.

M. H. Ullah, R. Eskandarpour, H. Zheng, A. Khodaei, Quantum Computing for Smart Grid Applications, IET Generation, Transmission & Distribution, vol. 16, no. 21, pp. 4239–4257, 2022.

S. Koretsky, P. Gokhale, J. M. Baker, J. Viszlai, H. Zheng, N. Gurung, R. Burg, R. Eskandarpour, et al., Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment, 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), 2021.

R. Eskandarpour, K. Ghosh, A. Khodaei, A. Paaso, Experimental Quantum Computing to Solve Network DC Power Flow Problem, arXiv preprint arXiv:2106.12032, 2021.

A. Arab, A. Khodaei, R. Eskandarpour, M. P. Thompson, Y. Wei, Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review, IEEE Access, vol. 9, pp. 61577–61593, 2021.

R. Eskandarpour, K. J. B. Ghosh, A. Khodaei, A. Paaso, L. Zhang, Quantum-Enhanced Grid of the Future: A Primer, IEEE Access, vol. 8, pp. 188993–189002, 2020.

R. Eskandarpour, K. Ghosh, A. Khodaei, L. Zhang, A. Paaso, S. Bahramirad, Quantum Computing Solution of DC Power Flow, arXiv preprint arXiv:2010.02442, 2020.

R. Eskandarpour, P. Gokhale, A. Khodaei, F. T. Chong, A. Paaso, Quantum Computing for Enhancing Grid Security, IEEE Transactions on Power Systems, vol. 35, no. 5, pp. 4135–4137, 2020.

R. Eskandarpour, A. Khodaei, Probabilistic Load Curtailment Estimation Using Posterior Probability Model and Twin Support Vector Machine, Journal of Modern Power Systems and Clean Energy, vol. 7, no. 4, pp. 665–675, 2019.

R. Eskandarpour, Power System Resilience Enhancement Using Artificial Intelligence, 2019.

R. Eskandarpour, A. Khodaei, A. Paaso, N. M. Abdullah, Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events, arXiv preprint arXiv:1810.02866, 2018.

R. Eskandarpour, A. Khodaei, Leveraging Accuracy–Uncertainty Tradeoff in SVM to Achieve Highly Accurate Outage Predictions, IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 1139–1141, 2017.

R. Eskandarpour, A. Khodaei, A. Arab, Load Curtailment Estimation in Response to Extreme Events, CIGRE USNC Grid of the Future Symposium, 2017.

R. Eskandarpour, A. Khodaei, Component Outage Estimation Based on Support Vector Machine, IEEE PES General Meeting, 2017, pp. 1–5.

R. Eskandarpour, A. Khodaei, A. Arab, Improving Power Grid Resilience Through Predictive Outage Estimation, North American Power Symposium (NAPS), 2017, pp. 1–5.

R. Eskandarpour, A. Khodaei, Machine Learning-Based Power Grid Outage Prediction in Response to Extreme Events, IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 3315–3316, 2016.

R. Eskandarpour, A. Khodaei, J. Lin, Event-Driven Security-Constrained Unit Commitment with Component Outage Estimation Based on Machine Learning Method, North American Power Symposium (NAPS), 2016, pp. 1–6.

R. Eskandarpour, H. Lotfi, A. Khodaei, Optimal Microgrid Placement for Enhancing Power System Resilience in Response to Weather Events, North American Power Symposium (NAPS), 2016, pp. 1–6.

R. Eskandarpour, G. Edwards, A. Khodaei, Resilience-Constrained Unit Commitment Considering the Impact of Microgrids, North American Power Symposium (NAPS), 2016, pp. 1–5.

R. Eskandarpour, A. Khodaei, J. Lin, Event-Driven Security-Constrained Unit Commitment, IEEE PES Innovative Smart Grid Technologies Conference, 2016.

Presenters

  • Rozhin Eskandarpour

    • Resilient Entanglement Inc.

Authors

  • Rozhin Eskandarpour

    • Resilient Entanglement Inc.