TL;DR
An intelligent Model Predictive Control framework for optimal power flow management in microgrids, with the objective of enhancing operational resilience, reducing diesel fuel consumption, and preventing blackouts through coordinated electric vehicle (EV) charging and discharging is proposed.
Convergence and multi-run statistical analysis further confirm the robustness, stability, and reproducibility of the trained policy, demonstrating the effectiveness of DRL as an intelligent and scalable solution for next-generation microgrid PQ control.
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...
A proximal policy optimization (PPO)-based energy management system (EMS) for a grid-connected community microgrid integrating photovoltaic and wind generation, battery storage, an electrolyzer, hydrogen storage, a fuel cell, and diesel backup is developed.
HVAC systems represent a major share of building energy consumption. Traditional control strategies are limited in coordinating energy-comfort tradeoffs across multiple zones simultaneously. Reinforcement learning (RL) offers adaptive, data-driven control that optimizes performance over time. However, deploying learned...
A Deep Reinforcement Learning-based energy management system employing a Deep Q-Network to coordinate battery–supercapacitor operation within a renewable microgrid is developed and evaluated, demonstrating the feasibility of applying deep reinforcement learning to coordinated battery–supercapacitor energy management an...
The results demonstrate that intelligently maximizing zero-emission resources actively suppresses compounding carbon liabilities (averting a 21.6% financial premium), providing system planners with a resilient, data-driven methodology to optimize infrastructure investments without over-sizing centralized grid connectio...
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