Event-Triggered T-S Fuzzy Load Frequency Control With Variable Probabilistic Release for Renewable Energy Integrated Power Systems

Abstract

This paper explores a T-S fuzzy-model-based approach for load frequency control in network-based power systems under random false data injection attacks, taking into account the integration of electric vehicles and photovoltaic power generation systems. Amidst uncertainties in parameters and nonlinear items of integrated power systems, a T-S fuzzy model is formulated, enabling convenient design and analysis of the rolling horizon optimal control (RHOC) strategy with variable control gains to ensure mean-square asymptotic stability in power systems. Considering the broad dispersion of new energy generation systems across the power grid, the use of network communication has become crucial. To overcome the limitation of network bandwidth while ensuring frequency stability, a novel event-triggered mechanism employing a varying probabilistic release strategy (VPRS) within grouped triggered data-packets is proposed for transmitting power frequency signals. Initially, a conventional event-triggered mechanism is established to generate primary triggering packets, stored in a buffer until designated packet capacity of the group is reached. Then, following the probabilistic updating algorithm in RHOC strategy, the transmission task is executed upon the arrival of the final triggered packet within each group. The efficacy of the proposed method is validated through a numerical example. Note to Practitioners—This paper introduces a T-S fuzzy-model-based RHOC strategy for power systems with renewable energy, addressing challenges posed by limited communication resources and random false data injection attacks. The innovative event-triggered mechanism with a VPRS optimizes network utilization for power systems, while the RHOC strategy with variable control gains guarantees mean-square asymptotic stability despite uncertainties and nonlinearities within the power network. While the efficacy of the approach is demonstrated through numerical examples, practitioners should take into account practical constraints, such as scalability issues and the complexities involved in real-world implementation. Future research will focus on enhancing scalability and addressing implementation challenges to facilitate broader adoption in power systems.