Tianqiao Zhao

Christian Moya, Guang Lin, Tianqiao Zhao et al.

This paper designs an Operator Learning framework to approximate the dynamic response of synchronous generators. One can use such a framework to (i) design a neural-based generator model that can interact with a numerical simulator of the rest of the power grid or (ii) shadow the generator's transient response. To this end, we design a data-driven Deep Operator Network~(DeepONet) that approximates the generators' infinite-dimensional solution operator. Then, we develop a DeepONet-based numerical scheme to simulate a given generator's dynamic response over a short/medium-term horizon. The proposed numerical scheme recursively employs the trained DeepONet to simulate the response for a given multi-dimensional input, which describes the interaction between the generator and the rest of the system. Furthermore, we develop a residual DeepONet numerical scheme that incorporates information from mathematical models of synchronous generators. We accompany this residual DeepONet scheme with an estimate for the prediction's cumulative error. We also design a data aggregation (DAgger) strategy that allows (i) employing supervised learning to train the proposed DeepONets and (ii) fine-tuning the DeepONet using aggregated training data that the DeepONet is likely to encounter during interactive simulations with other grid components. Finally, as a proof of concept, we demonstrate that the proposed DeepONet frameworks can effectively approximate the transient model of a synchronous generator.

Yunkai Hu, Tianqiao Zhao, Meng Yue

This paper introduces a novel Large Language Models (LLMs)-assisted agent that automatically converts natural-language descriptions of power system optimization scenarios into compact, solver-ready formulations and generates corresponding solutions. In contrast to approaches that rely solely on LLM to produce solutions directly, the proposed method focuses on discovering a mathematically compatible formulation that can be efficiently solved by off-the-shelf optimization solvers. Directly using LLMs to produce solutions often leads to infeasible or suboptimal results, as these models lack the numerical precision and constraint-handling capabilities of established optimization solvers. The pipeline integrates a domain-aware prompt and schema with an LLM, enforces feasibility through systematic validation and iterative repair, and returns both solver-ready models and user-facing results. Using the unit commitment problem as a representative case study, the agent produces optimal or near-optimal schedules along with the associated objective costs. Results demonstrate that coupling the solver with task-specific validation significantly enhances solution reliability. This work shows that combining AI with established optimization frameworks bridges high-level problem descriptions and executable mathematical models, enabling more efficient decision-making in energy systems

Feiqin Zhu, Dmitrii Torbunov, Zhongjing Jiang et al.

Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outputs. This non-uniqueness poses a critical barrier to accurate and unique identification. This work introduces a novel parameter estimation framework to address such limits: the Joint Conditional Diffusion Model-based Inverse Problem Solver (JCDI). By leveraging the stochasticity of diffusion models, JCDI produces possible solutions revealing underlying distributions. Joint conditioning on multiple observations further narrows the posterior distributions of non-identifiable parameters. For the challenging task in dynamic power systems: composite load model parameterization, JCDI achieves a 58.6% reduction in parameter estimation error compared to the single-condition model. It also accurately replicates system's dynamic responses under various electrical faults, with root mean square errors below 4*10^(-3), outperforming existing deep-reinforcement-learning and supervised learning approaches. Given its data-driven nature, JCDI provides a universal framework for parameter estimation while effectively mitigating the non-uniqueness challenge across scientific domains.