Decentralized Adaptive Event-Triggered Load Frequency Control for EV-Integrated Power Systems Under State-Input Constraints

Abstract

This paper addresses the load frequency control (LFC) problem for electric vehicle (EV)-integrated power systems under state and input constraints. A decentralized adaptive dynamic programming (ADP) control scheme with an event-triggered (ET) mechanism is developed to achieve efficient frequency regulation across interconnected areas. To enforce state constraints, a logarithmic barrier transformation is introduced. Furthermore, a non-quadratic utility function is used to handle input constraints. Additionally, an event-triggered rule with a dead-zone operator is introduced to reduce communication and controller updates while guaranteeing the absence of Zeno behavior. The uniform ultimate boundedness of the closed-loop system is established using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed algorithm. Note to Practitioners—For practical deployment, the controller can operate independently in each control area as part of secondary frequency control. It uses locally available signals, including area frequency and tie-line power deviations, generator mechanical-power and governor-valve-position measurements or estimates, and aggregate EV power from the EV aggregator. The ACE integral state is obtained locally from the frequency and tie-line power deviations. These signals are used for critic adaptation and evaluation of the triggering condition. When a triggering event occurs, the area control command is updated and allocated to the conventional generation and EV regulation channels according to their participation factors. Between events, the previous command is maintained. This implementation relies on reliable local measurements or state estimates, adequate computational resources for online critic adaptation, and an event-driven interface to the controllable generation and EV resources. In practice, measurement uncertainty, communication delays and packet losses, variations in EV availability and state of charge, and insufficient excitation may affect online learning and control performance. Further validation under realistic operating conditions is needed before field deployment.