Fangxing Li

Buxin She, Brian Chen, Luanzheng Guo et al.

Power system simulation workflows remain expert-intensive. Engineers must translate study intents into code or API calls, execute analyses, and interpret outputs. To automate this workflow, this paper presents PFAgent, a tractable and self-evolving power-flow agent for interactive grid analysis. PFAgent integrates four key capabilities: i) a tractable and interactive architecture for intent parsing, knowledge retrieval, tool execution, and structured reporting; ii) a self-evolution mechanism combining verification-driven refinement and human-in-the-loop feedback; iii) an AI-assisted evaluation and debugging loop that leverages conversational context, generated code, and execution errors for iterative fixing; and iv) an evaluation framework covering task success, convergence validity, numerical consistency, and explanation quality. Verification on IEEE benchmark systems shows that PFAgent can automate case change, analyze voltage violations, perform N-1 contingency analysis, generate plots and concise summaries, and return reproducible results with transparent execution logs. The proposed framework highlights a shift from conventional simulation tools to interactive, tractable, and self-evolving agents for power system analysis.

Hantao Cui, Fangxing Li, Kevin Tomsovic et al.

The electric power system is a cyber-physical system with power flow in the physical system and information flow in the cyber. Simulation is crucial to understanding the dynamics and control of electric power systems yet the underlying communication system has historically been ignored in these studies. This paper aims at meeting the increasing needs to simulate the operations of a real power system including the physical system, the energy management system, the communication system, and the emerging wide-area measurement-based controls. This paper proposes a cyber-physical testbed design and implementation for verifying and demonstrating wide-area control methods based on streaming telemetry and phasor measurement unit data. The proposed decoupled architecture is composed of a differential algebraic equation based physical system simulator, a software-defined network, a scripting language environment for prototyping an EMS system and a control system, all of which are integrated over industry-standard communication protocols. The proposed testbed is implemented using open-source software packages managed by a Python dispatcher. Finally, demonstrations are presented to show two wide-area measurement-based controls - system separation control and hierarchical voltage control, in the implemented testbed.

Yichen Zhang, Hantao Cui, Jianzhe Liu et al.

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interests active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which significantly enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

Yichen Zhang, Feng Qiu, Tianqi Hong et al.

Self-healing capability is one of the most critical factors for a resilient distribution system, which requires intelligent agents to automatically perform restorative actions online, including network reconfiguration and reactive power dispatch. These agents should be equipped with a predesigned decision policy to meet real-time requirements and handle highly complex $N-k$ scenarios. The disturbance randomness hampers the application of exploration-dominant algorithms like traditional reinforcement learning (RL), and the agent training problem under $N-k$ scenarios has not been thoroughly solved. In this paper, we propose the imitation learning (IL) framework to train such policies, where the agent will interact with an expert to learn its optimal policy, and therefore significantly improve the training efficiency compared with the RL methods. To handle tie-line operations and reactive power dispatch simultaneously, we design a hybrid policy network for such a discrete-continuous hybrid action space. We employ the 33-node system under $N-k$ disturbances to verify the proposed framework.