This paper proposes a lightweight, simplified Q-learning energy management strategy for extended-range electric vehicles (REEVs), successfully implemented on an STM32 microcontroller, fully satisfying strict on-board embedded system constraints and providing a highly feasible solution for intelligent REEV energy manage...
An AI-driven digital twin (DT) framework that pairs a real-time physical-data grid model with a layered AI optimisation engine made up of an LSTM/Transformer forecaster, a graph neural network (GNN) state estimator, and a deep reinforcement learning (DRL) dispatch controller is proposed.
In distribution network and microgrids, energy storage (ES) systems possess four-quadrant operational capabilities, making them inherently high-quality resources for reactive power (RP) regulation. However, existing research has primarily focused on optimizing the active power of ES to achieve economic objectives, whil...
A TFT-MPC-CR framework that combines Temporal Fusion Transformer forecasting, Model Predictive Control (MPC), AI-based False Data Injection (FDI) detection, and adaptive resilient control is proposed that is expected to provide accurate prediction, efficient energy management, rapid attack response, and secure autonomo...
The increasing complexity of modern power systems, driven by high renewable penetration, load variability, and operational uncertainty, demands fast and reliable solutions to the AC optimal power flow problem (AC-OPF). Traditional optimization methods, though accurate, often struggle with scalability and high computati...
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