Adaptive long short term memory-PI based control for load frequency regulation in electric vehicle integrated two area power systems

The growing penetration of electric vehicles (EVs) and renewable energy sources has introduced significant nonlinear and stochastic disturbances in modern power systems, posing major challenges to conventional load frequency control (LFC) strategies. To address these issues, a Long Short-Term Memory-based Proportional Integral (LSTM-PI) controller is proposed to enable adaptive frequency regulation in a two-area EV-integrated system. The LSTM network dynamically tunes the proportional and integral gains in response to temporal variations in frequency and tie-line states. The learning module is trained using an Integral of Time weighted Absolute Error (ITAE) objective. Practical non-linearities such as governor deadband, generation-rate constraints, valve saturation, and EV state-of-charge limits are incorporated into both the training dataset and the online control evaluation. A constrained Model Predictive Control (CMPC-PI) is developed as a modern benchmark to compare the proposed technique under identical sampling rates and actuator limits. The proposed method shows better performance under dynamic conditions. Statistical performance analysis has been carried out through 30 runs of Monte Carlo analysis with a mean of 95% confidence intervals. Stability analysis under time-varying gains is supported by a bounded-gain Lyapunov Framework and a delay-margin analysis that addresses sensing and actuation delays. The result analysis confirms that the LSTM-PI controller provides a scalable intelligent solution for modern EV-integrated smart grid frequency regulation. The proposed intelligent adaptive controller provides a scalable, low-complexity, and stability-assured solution for robust load-frequency regulation in nonlinear, EV-integrated smart power grids.