Hydrogen Energy Storage and Aggregation System Patent Application

ChangeBridge: Patent Apps - Power Supply (H02J)

Detected March 26th, 2026

Summary

The USPTO has published a patent application (US20260088618A1) for a hydrogen energy storage and aggregation system utilizing machine learning for virtual power plants. The application details an energy control system designed to intelligently manage hydrogen production, storage, and solar energy production, interfacing with external systems like the grid and VPPs.

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What changed

This document is a patent application (US20260088618A1) filed with the USPTO, detailing a system for hydrogen energy storage and aggregation using machine learning for virtual power plants. The application describes an energy control system that employs agentic machine learning techniques to manage hydrogen production and storage, solar energy production, and grid interaction. It includes components like an electrolyzer, hydrogen storage, fuel cell, inverter, battery storage, and a control system trained with reinforcement learning agents to handle power surplus and deficit conditions.

As this is a patent application, it does not impose direct regulatory obligations or compliance deadlines on entities. However, it signals potential future technological developments in energy management and virtual power plants. Companies involved in energy storage, hydrogen technology, renewable energy integration, and AI-driven control systems may find the disclosed technology relevant for research and development or potential licensing opportunities. Compliance officers should note this as a technological development rather than an immediate regulatory requirement.

Source document (simplified)

← USPTO Patent Applications

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

Application US20260088618A1 Kind: A1 Mar 26, 2026

Inventors

Herve-David Gregoire-Mazzocco, Sean G. Widmer

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.