Hydrogen Energy Storage and Energy Aggregation Systems Utilizing Machine Learning for Virtual Power Plants

Abstract

An energy control system employing agentic machine learning techniques to intelligently manage hydrogen production and storage, solar energy production, and interfacing with external systems such as the grid and virtual power plants (VPPs). In accordance with various embodiments of the present invention, a hydrogen storage assembly includes an electrolyzer, a hydrogen storage system, a hydrogen fuel cell, an inverter, an electrochemical energy storage module (e.g., batteries), a power conversion system, and a control system incorporating machine learning techniques, such as reinforcement learning models used to train a set of specialized agents configured to intelligently handle surplus and deficit power conditions during on-grid and off-grid states. The systems and methods may be used, for example, to optimize energy distribution based on behavioral metadata and to implement a fractal grid architecture.

CPC Classifications