Hantao Cui

Sungjoo Chung, Ying Zhang, Meng Yue et al.

Black-box modeling of inverter-based resources (IBRs) has become essential for real-time grid operation and control in the presence of proprietary electronic control architectures. Existing machine learning (ML)-based online dynamic trajectory prediction approaches using IBR black-box models either significantly accumulate prediction errors when multiple surrogates are simultaneously used or ignore measurement errors, limiting their deployment in practical grids. To address these limitations, this paper proposes a novel network interdependency-informed ML algorithm for online dynamic trajectory prediction in IBR-integrated power systems. A modular spatiotemporal attention network (STAN)-based predictor for the black-box modeling of each IBR unit is first proposed. Utilizing past measurements, the proposed STAN can effectively capture and predict the spatiotemporal dynamics of IBRs by employing an attention mechanism to attend to the most pertinent features for trajectory prediction. Furthermore, a novel hybrid physics-informed loss function that integrates a decoupled linearized AC power flow formulation is proposed. The proposed loss function effectively ensures physical consistency of predictions within network operation while avoiding the computational complexity of iterative power flow solving, thereby enabling efficient gradient backpropagation and overall improved prediction accuracy. Case studies on the IEEE 14- and WECC 179-bus systems demonstrate that the proposed method achieves significant accuracy enhancement and robustness against measurement errors, outperforming recent ML-based trajectory prediction methods.

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.