Neural Architecture Search-Driven Multi-Objective Coordinated Load Frequency Control and Automatic Voltage Regulation for Renewable-Dominated Multi-Area Power Systems

The large-scale integration of renewable energy sources (RESs), electric vehicles (EVs), and battery energy storage systems (BESSs) has significantly reduced system inertia and intensified frequency–voltage coupling in modern interconnected power systems, thereby challenging the effectiveness of conventional secondary control strategies. To address these emerging issues, this paper proposes an intelligent, control-aware evolutionary multi-objective Neural Architecture Search (EMO–NAS) framework for coordinated Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR) in renewable-dominated multi-area power systems. Unlike existing approaches that rely on fixed or heuristically selected controller structures, the proposed framework treats the controller architecture itself as an explicit decision variable and autonomously synthesizes task-specific control policies through multi-objective evolutionary optimization. The coordinated LFC–AVR problem is formulated by simultaneously minimizing frequency deviation, tie-line power oscillations, voltage deviation, rate of change of frequency (RoCoF), control effort, and robustness degradation, while satisfying practical operational constraints including generation rate limits, actuator bounds, and BESS state-of-charge restrictions. A structured NAS search space incorporating feedforward, recurrent, and temporal architectures is evaluated using closed-loop time-domain simulations under realistic disturbances, renewable intermittency, EV variability, and parameter uncertainty. Feasibility and stability are enforced through constraint-aware penalties and robust domain randomization. Comprehensive simulation studies on three- and four-area interconnected systems demonstrate that the proposed EMO–NAS controller achieves substantial performance improvements compared with optimally tuned fractional-order PID, robust sliding mode, and fixed-architecture neural controllers. Quantitatively, reductions of approximately 30–35% in frequency deviation, 25–35% in tie-line power oscillations, and up to 30% in RoCoF are achieved, while completely eliminating constraint violations. Robustness analysis under ±50% parameter uncertainty and Monte Carlo simulations further confirm superior stability, generalization, and scalability. These results establish architecture-level optimization as a powerful and systematic pathway for designing robust, coordinated secondary controllers in future low-inertia, renewable-dominated power systems.