AI Workflow for Hydrogen Electrolyzer Dispatch Based on Power Prices

Manual operation locks electrolyzers into a high-overhead cost model. This workflow automates the decision to run based on real-time power prices, hydrogen market values, and storage levels, transforming the asset from a fixed industrial load into a flexible, profit-seeking resource. The upside comes from capturing low-cost renewable energy, avoiding peak grid tariffs, and selling hydrogen when market premiums are highest.

The core is a LangGraph or Temporal-based orchestrator that sequences specialized agents:

1. Data Ingestion Agent: Polls day-ahead & real-time power price APIs (e.g., CAISO, Nord Pool), hydrogen price feeds, and SCADA for tank levels.
2. Forecast Agent: Runs probabilistic price and demand forecasts for the next 24-72 hours.
3. Optimization Agent: Solves for the optimal schedule using electrolyzer efficiency curves, degradation costs, and storage constraints.
4. Dispatch Agent: Sends secure start/stop/ramp commands to the electrolyzer PLC via OPC-UA or a REST API. A separate Exception Agent monitors for API failures or anomalous operations, routing alerts to human engineers.

Production readiness requires deep integration. The workflow must connect to:

• Electrolyzer PLC/SCADA: For real-time state monitoring and command execution, often via OPC-UA or Modbus TCP.
• Hydrogen Storage & Pipeline Systems: To read tank levels and pressure constraints.
• ERP (SAP/Oracle): For capturing operational costs and linking to financial reporting.
• Commodity Trading & Risk Management (CTRM) Systems: To align automated dispatch with broader trading book positions and hedge strategies.
• Energy Management Systems (EMS): For sites co-optimizing electrolyzer load with other behind-the-meter assets like batteries.

A phased build de-risks deployment:

1. Phase 1 – Simulation & Backtesting: Develop agents and optimization logic in a sandbox, backtesting against historical price data to validate economic logic.
2. Phase 2 – Shadow Mode: Run the workflow in parallel with manual operations, comparing its recommended dispatch to actual decisions, tuning for real-world latency and data quality.
3. Phase 3 – Limited Pilot: Automate dispatch for a single electrolyzer stack or during pre-defined low-risk hours, with a human-in-the-loop approval gate.
4. Phase 4 – Full Autonomy: Roll out to the full fleet, with automated execution guarded by pre-defined risk limits (e.g., max daily runtime, price volatility thresholds).

Autonomous control of heavy industrial assets demands robust guardrails:

• Pre-Commitment Checks: Validate power purchase agreement (PPA) terms, grid connection limits, and maintenance schedules before any dispatch signal is sent.
• Dynamic Risk Limits: Automatically throttle or pause trading if price volatility or forecast uncertainty exceeds configured thresholds.
• Explainability & Audit Trail: Every dispatch decision must log the input data, agent reasoning, and optimization result for regulatory and internal audit.
• Human Escalation Layer: Engineers receive alerts for system failures or edge cases (e.g., tank near capacity, extreme price spikes) and can override via a secure dashboard.

The financial case is built on energy arbitrage and increased asset value. By running primarily during low-price periods (often overnight or during midday solar peaks), the levelized cost of hydrogen drops significantly. Further upside comes from providing grid balancing services if the electrolyzer can modulate quickly. The build pays back by reducing reliance on manual traders/operators, minimizing costly operation during high-price hours, and creating a more bankable, predictable revenue stream for project financiers.

This foundational agent continuously polls and validates data from disparate sources: day-ahead and real-time electricity price feeds (e.g., from CAISO, PJM APIs), hydrogen spot market indices, and granular weather forecasts for renewable generation that impacts grid prices. It normalizes timestamps, handles API failures with retry logic, and writes clean, time-series data to a unified data store, creating the single source of truth for all downstream decision logic.

The core decision agent runs a constrained optimization model every 5-15 minutes. It ingests the latest price forecasts, current tank levels, and hydrogen demand schedules. The model incorporates the electrolyzer's non-linear efficiency curve (kWh/kg), minimum turndown ratio, and startup/shutdown costs. It solves for the schedule that maximizes gross margin over the optimization horizon (e.g., 24-48 hours), outputting a simple RUN/STANDBY command and target power draw.

A secure, low-latency integration component translates high-level dispatch commands into equipment-specific instructions for the plant's Programmable Logic Controller (PLC) or SCADA system. It handles protocol conversion (e.g., OPC UA, Modbus), sends setpoints for rectifier power, and confirms command acceptance. This layer includes critical safety interlocks to prevent commands that would violate tank pressure limits or other hard operational constraints.

A supervisory agent monitors for conditions requiring human review: model confidence scores below threshold, extreme price volatility, equipment alarms from SCADA, or tank levels nearing capacity. It routes these exceptions via Slack, Microsoft Teams, or a dedicated dashboard to a plant operator with contextual data and recommended actions. The workflow pauses or enters a safe holding pattern until a manual approve, override, or reject decision is logged, ensuring auditability.

A post-dispatch analytics agent compares actual power consumption and hydrogen production against the model's forecasted schedule. It calculates realized margin, quantifies the value of automated decisions, and flags persistent deviations for model retraining. This component maintains a lightweight digital twin of the electrolyzer system, enabling 'what-if' scenario analysis for maintenance planning or contract negotiation without disrupting live operations.

The workflow is coordinated by a central orchestration engine, built on a framework like LangGraph, which defines the state machine for the entire process. It manages the execution sequence of agents, passes data between them, and enforces approval gates. Integrated observability tools capture traces, logs, and metrics for every decision cycle, providing a single pane for monitoring system health, latency, and business KPIs like $ margin captured per MWh.