AI 中文总结
针对大规模并网逆变器资源，提出并发输电参数学习与电流控制方法，在线估计参数并优化功率分配，仿真验证其能改善电网事件下的动态响应。
AI 中文摘要
大规模逆变器资源（IBRs）接入电网带来了两个关键挑战，即最优发电和输电系统稳定性。本文针对并网型IBRs提出了并发输电参数学习与最优发电及电流控制方法。所提方法利用电流测量在线估计输电参数，使IBRs能够根据不断变化的电网条件调整其控制动作。学习到的参数估计被纳入基于优化的功率分配策略中，以确定最优功率设定点。同时，设计了一种电流控制方案，基于估计值以及来自高层优化器的有功和无功功率参考值来调节并网（GFL）IBRs的电流输出，从而改善对电网事件的动态响应。通过在MATLAB-Simulink环境中的仿真验证了所提框架的有效性。从结果中可以看出并发估计器和电流控制器在电网事件期间的影响。
英文摘要
The integration of large-scale inverter-based resources (IBRs) into the grid presents two key challenges in terms of optimal power generation and transmission system stability. This work proposes concurrent transmission parameter learning and optimal power generation and current control for the grid-following IBRs. The proposed approach estimates transmission parameters online using the current measurements, enabling the IBRs to adapt their control actions to evolving grid conditions. The learned parameter estimates are incorporated into an optimization-based power distribution strategy to determine the optimal power setpoints. In parallel, a current control scheme is designed to regulate the grid-following (GFL) IBRs current output based on the estimates and received active and reactive power references from the high-level optimizer, thereby improving the dynamic response to the grid events. The effectiveness of the proposed framework is validated through a simulation in a MATLAB-Simulink environment. From the results, the impact of the concurrent estimator and current controller during the grid events can be seen.
CommentsSubmitted to the IEEE Texas Power and Energy Conference (TPEC), 2027. 7 Figures
