Accelerating Green Hydrogen Via Digitalization - Optimization in Design, Operation and Profitability of a Fully Digitized Green Value Chain

Producing green hydrogen and it’s derivatives for solving climate crisis and energy security is a key focus area in todays energy transition and has been significantly accelerated in Europe as a consequence of replacing Russian gas. TES as a green energy company is striving to become the largest eNG (eNG = synthetic natural gas made from green H2 and circular CO2) producer in the world and is therefore currently setting up various import and reconversion hubs as well as production plants around the globe to turn renewable electricity into green molecules.

Digital tools and AI will play a key role in all aspects fo this value chain – design of the plants, operation of the assets in the most efficient way and also maximizing profits by selecting the downstream outlet markets based on dynamic pricing.

Starting with design of plants, the nature of a renewable electricity based H2 production brings variability in feedstock. So the project developer is left with an optimization problem which takes into account variability of power generation, operational constraints of downstream equipment based on turndown ratios and ramp rates, choices between electricity storage and hydrogen storage to decouple units, utilization of capital intense equipment such as electrolyzers vs. efficiency losses of large quantity power storage, etc.

All of these variables are constantly updating and depend on specific location and renewable profile of a location, price levels, etc. Only with proper digital tools and algorithms/optimizers can the project developer find the optimum setup and size of the individual process units for the lowest possible cost of the produced eNG.

During operation digitalization is needed to ensure a non stop monitoring of the assets in operation for maximum efficiency, emission footprint, allow continuous improvement and real time response to events. Here a proper IoT strategy and a link to traditional process control is needed to ensure the assets are operated in the most efficient way, especially given the dynamic ramping required to follow renewable energy supply profiles, etc.

Last not least the green eNG allows to serve different markets from the import hub. It can be supplied as is as green natural gas replacement with carbon recycle from the offtaker, in which case it competes with natural gas plus emission costs. Second it can be reconverted back into green H2 with full capture and return of the CO2 to the upstream hydrogen production for methanation, so the sold product is green H2. Last it can be converted back to green electricity in an oxycombustion power plant, also capturing the entire CO2 and returning it to the upstream hydrogen production for fresh methanation into eNG. Here the product sold is a green uninterrupted electricity PPA, available when no sun or wind is producing green electricity locally, causing high proce fluctuations. TES envisions using AI and optimizers to maximize the margins by forecasting and providing the products in the most valuable markets at any point in time.