Context

Hydrogen hubs must balance varying producer capacities with dynamic consumer demand, but manual allocation and simple internal models slow decisions and often lead to inefficiencies. As inputs change frequently and data remains scattered, maintaining a stable and transparent dispatch process becomes challenging.

The Hydrogen Dispatch Balancer applies an AI‑first architecture built with Microsoft Planner Designer, converting planning requirements into structured processes and data models. Copilot evaluates capacity, demand, and scenarios to generate optimized, grounded dispatch recommendations.

Challenges

Hydrogen dispatching is hindered by inconsistent producer and consumer inputs, making allocation decisions unreliable. Manual calculations increase the risk of unmet demand, over‑allocation, or idle capacity. Teams lack clarity on why decisions are made, and early‑stage hubs relying on simplified logic face even greater inefficiencies. Maintaining traceability and audit‑ready reasoning across dispatch cycles is also difficult.

Solution

Microsoft Planner Designer defines hub roles, workflows, and data structures, creating a strong foundation for AI‑driven optimization. Copilot analyzes all capacity and demand inputs, compares scenarios, and produces clear, explainable dispatch plans.

The Dispatch Planner Copilot supports plan creation and decision explanation, while the Dispatch Task Manager enables review, approval, and execution with integrated BI views, exports, and alerts. Automated workflows ensure continuous monitoring and improvement,resulting in faster, more transparent, and highly efficient hydrogen dispatch.