CSEE JPES · 2026

Quantum Learning and Estimation for Coordinated Operation between Distribution Networks and Energy Communities

Yingrui Zhuang, Lin Cheng, Yuji Cao, Tongxin Li, Ning Qi, Yan Xu, Yue Chen

CSEE Journal of Power and Energy Systems

Journal accepted version dated 2026. The first preprint appeared in 2025.

Can quantum methods assist coordination with limited local information?

A hybrid quantum learning model estimates energy community responses to prices, while quantum amplitude estimation supports uncertainty calculations. Numerical studies assess accuracy and resource requirements. Reported timing advantages assume ideal quantum devices rather than deployed hardware.

Research themes

Cite this paper

Yingrui Zhuang, Lin Cheng, Yuji Cao, Tongxin Li, Ning Qi, Yan Xu, Yue Chen. Quantum Learning and Estimation for Coordinated Operation between Distribution Networks and Energy Communities. CSEE Journal of Power and Energy Systems, 2026. https://doi.org/10.17775/CSEEJPES.2025.05720

BibTeX
@article{tongxin-quantum-coordination,
  title = {{Quantum Learning and Estimation for Coordinated Operation between Distribution Networks and Energy Communities}},
  author = {Yingrui Zhuang and Lin Cheng and Yuji Cao and Tongxin Li and Ning Qi and Yan Xu and Yue Chen},
  year = {2026},
  journal = {CSEE Journal of Power and Energy Systems},
  url = {https://doi.org/10.17775/CSEEJPES.2025.05720},
  doi = {10.17775/CSEEJPES.2025.05720}
}
2026AAAI

Adaptive Riemannian Graph Neural Networks

Xudong Wang, Chris Ding, Tongxin Li, Jicong Fan

ARGNN learns a node specific Riemannian metric instead of imposing one fixed curvature on an entire graph. A diagonal metric parameterization and geometric regularization support efficient training, with convergence analysis and experiments on homophilic and heterophilic graphs.

2026Applied Energy

Counterfactual load forecasting with LLM-structured events and representation learning

Yujie Chen, Yifei Gao, Runyao Yu, Yuhe Wu, Guangyu Wang, Yue Chen, Tongxin Li

NACF turns news into structured treatments and estimates load trajectories under alternative event conditions. Reweighting and representation balancing address observed confounding. Experiments examine factual accuracy and interpretable demand perturbations without claiming that unobserved counterfactual outcomes can be directly validated.