Model Predictive Control–Based Load-Frequency Regulation of Grid-Forming Inverter–Based Power Systems
Xiaojing Qi, Jianyong Zheng, Fei Mei
Abstract
The high-penetrated renewables enable flexible and versatile dispatchable resources in power systems. As the digitalization process improves with the help of information and communication technology (ICT), a real-time online control framework is essential to regulate such a low-inertia power system dominated by renewables. To enable control practices, a control-oriented model is developed linking load increments to frequency dynamics. Based on such a control-oriented model, a model predictive control (MPC)–based online feedback algorithm is well designed by the virtue of rapid data transmission enabled by digitalization. The MPC optimization is a convex quadratic programming, where an objective function that balances frequency deviations and load increments is formulated and operational constraints are integrated into a matrix-type inequality. In the end, the proposed MPC-based load-frequency control framework is illustrated by several case studies, where validations and analyses of constraints inside the model are also included.