Kyung-bin Kwon

Kyung-Bin Kwon, Min Gyung Yu, Sayak Mukherjee et al.

Grid-forming (GFM) inverters are essential for enhancing stability in modern power systems with high penetration of inverter-based resources (IBRs). However, their performance highly depends on control parameters tuning, particularly the active power-frequency droop coefficient. This parameter presents a trade-off among competing objectives, including damping, settling time, rate of change of frequencies (RoCoF) and frequency nadirs. This paper proposes a real-time, adaptive optimization framework based on Extremum Seeking Control (ESC) to dynamically tune the GFM droop gain. A multi-objective cost function balances conflicting performance goals such as oscillation energy, frequency nadir, RoCoF, and post-disturbance settling performance. The approach is validated through numerical simulations on a modified IEEE 68-bus system. Results demonstrate that the cost function is convex with respect to the droop parameter, justifying gradient-based optimization. Furthermore, the ESC algorithm successfully tracks the time-varying optimal droop coefficient in real-time as network conditions change, thereby ensuring robust and near-optimal system performance without requiring an analytical grid model.

Kyung-bin Kwon, Lintao Ye, Vijay Gupta et al.

Non-ideal communication links, especially delays, critically affect fast networked controls in power systems, such as the wide-area damping control (WADC). Traditionally, a delay estimation and compensation approach is adopted to address this cyber-physical coupling, but it demands very high accuracy for the fast WADC and cannot handle other cyber concerns like link failures or {cyber perturbations}. Hence, we propose a new risk-constrained framework that can target the communication delays, yet amenable to general uncertainty under the cyber-physical couplings. Our WADC model includes the synchronous generators (SGs), and also voltage source converters (VSCs) for additional damping capabilities. To mitigate uncertainty, a mean-variance risk constraint is introduced to the classical optimal control cost of the linear quadratic regulator (LQR). Unlike estimating delays, our approach can effectively mitigate large communication delays by improving the worst-case performance. A reinforcement learning (RL)-based algorithm, namely, stochastic gradient-descent with max-oracle (SGDmax), is developed to solve the risk-constrained problem. We further show its guaranteed convergence to stationarity at a high probability, even using the simple zero-order policy gradient (ZOPG). Numerical tests on the IEEE 68-bus system not only verify SGDmax's convergence and VSCs' damping capabilities, but also demonstrate that our approach outperforms conventional delay compensator-based methods under estimation error. While focusing on performance improvement under large delays, our proposed risk-constrained design can effectively mitigate the worst-case oscillations, making it equally effective for addressing other communication issues and cyber perturbations.

Kyung-Bin Kwon, Sayak Mukherjee, Ramij R. Hossain et al.

This letter develops a novel physics-informed neural ordinary differential equations-based framework to emulate the proprietary dynamics of the inverters -- essential for improved accuracy in grid dynamic simulations. In current industry practice, the original equipment manufacturers (OEMs) often do not disclose the exact internal controls and parameters of the inverters, posing significant challenges in performing accurate dynamic simulations and other relevant studies, such as gain tunings for stability analysis and controls. To address this, we propose a Physics-Informed Latent Neural ODE Model (PI-LNM) that integrates system physics with neural learning layers to capture the unmodeled behaviors of proprietary units. The proposed method is validated using a grid-forming inverter (GFM) case study, demonstrating improved dynamic simulation accuracy over approaches that rely solely on data-driven learning without physics-based guidance.

Kyung-bin Kwon, Hao Zhu

Effectively operating electrical vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces scales with the number of EVs and is thus very large and time-varying. This dimensionality issue affects the efficiency and convergence properties of generic RL algorithms. We develop aggregation schemes that are based on the emergency of EV charging, namely the laxity value. A least-laxity first (LLF) rule is adopted to consider only the total charging power of the EVCS which ensures the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Based on the proposed representation, policy gradient method is used to find the best parameters for the linear Gaussian policy . Numerical results have validated the performance improvement of the proposed representation approaches in attaining higher rewards and more effective policies as compared to existing approximation based approach.