Investment-Based Optimisation of Green Hydrogen Production for Mobility Use

Abstract

This thesis investigates the financial viability of green hydrogen production for mobility applications across four European countries: Germany, Norway, Denmark and Spain. Given the rising importance of hydrogen in hard-to-electrify sectors such as (heavy-duty) transport, the study focuses on understanding how system design and local energy conditions influence the Levelised Cost of Hydrogen (LCOH). A Python-based investment optimisation model was developed using Pyomo and Gurobi to simulate hydrogen production and price predictions over a 20-year period. The model dynamically selects the optimal mix of technologies, including solar PV, wind, hydropower, grid electricity and battery storage, based on country-specific prices, capacity limits, and renewable resource availability.

Four scenarios were analysed: GridOnly, RenewablesGrid, RenewableBattery, and RenewablesOnly. The model calculates key financial indicators such as LCOH, Return on Investment (ROI), and payback period. Results show that renewable-based systems, particularly those with flexible grid support (RenewablesGrid), offer the most favourable outcomes. Spain consistently achieves the lowest LCOH due to high solar availability and low CAPEX. In contrast, grid-only scenarios fail to reach profitability in any country. Battery integration improves system flexibility but adds significant cost, limiting its financial value under current demand assumptions.
The findings provide practical insights for policymakers and investors on where and how to invest in hydrogen infrastructure most effectively. By comparing dynamic optimisation outcomes to standard online LCOH calculators, the study also highlights the added value of using time-resolved modelling to inform investment decisions in hydrogen systems.

Popular Abstract

How to Design Cost-Effective Hydrogen Systems
Comparing energy setups for green hydrogen production in four European countries
Could green hydrogen fuel Europe’s trucks, buses and ferries at a fair cost? That’s the question I set out to answer by modelling and comparing the cost of hydrogen production in four different European countries: Germany, Norway, Denmark and Spain. Using a custom-built optimisation model, I explored how renewable energy, battery storage, and electricity grid access influence production costs, and what combination leads to the lowest possible price for green hydrogen.
The problem: Green hydrogen is promising, but still too expensive
The transport sector is a major contributor to greenhouse gas emissions, and while battery electric cars are growing fast, larger vehicles like trucks and buses remain harder to electrify. Hydrogen, especially if produced using clean electricity, offers a powerful alternative. It’s emission-free at the point of use and can be stored and transported more easily than electricity. However, green hydrogen is still significantly more expensive than fossil-based hydrogen or diesel. 
To become a realistic solution for decarbonising transport, the cost of green hydrogen needs to fall. But how can that be achieved, and what do we need to invest in to make it cost-effective?
What I did: Building a model to simulate and optimise hydrogen systems
To tackle this challenge, I developed an energy system optimisation model in Python, using the tools Pyomo and Gurobi. This model simulates hydrogen production over 20 years, using hourly data for electricity prices and renewable resource availability. It can select the optimal combination of solar, wind, hydropower, grid electricity, and battery storage for each country, while calculating the resulting Levelised Cost of Hydrogen (LCOH), a key financial indicator for the price of hydrogen.

To explore different ways of producing green hydrogen, I compared four distinct system setups. The first, called GridOnly, relies entirely on electricity from the national grid without any on-site renewable energy. The second, RenewablesGrid, combines locally installed renewable sources like solar, wind and hydropower with grid electricity to offer both cost savings and flexibility. The third setup, RenewableBattery, operates completely off-grid by using renewable energy together with battery storage to ensure a stable supply. Finally, the RenewablesOnly configuration is also off-grid but depends solely on real-time renewable generation, without any batteries or external backup.
The findings: Spain wins, grid helps, batteries… not always
One of the most interesting findings was how much country-specific conditions matter. Spain, thanks to its strong solar potential and relatively low investment costs, achieved the lowest hydrogen production costs, around €4.20 per kilogram, well below the average EU cost. Denmark and Norway also performed well due to strong wind and hydropower resources, while Germany, with higher electricity prices and lower solar potential, struggled to reach competitive costs.
Another important result was that systems combining renewables with grid access (RenewablesGrid) were the most financially attractive in all countries. They allowed flexibility without requiring expensive batteries, and avoided oversizing the system in the final years when demand grows.
A surprising outcome was that adding batteries didn’t always help. Although they improved flexibility, the high investment cost often outweighed the benefit, especially under current demand levels. In most countries, the battery scenario only barely broke even.

Why it matters: A practical guide for hydrogen investments
These insights are valuable for policymakers, investors, and developers deciding where and how to deploy hydrogen infrastructure. The model shows how costs vary across locations and what energy combinations make the most sense financially. It also provides a better alternative to common online calculators by using hourly electricity data and simulating flexible system behaviour. This gives more realistic and precise predictions and results.
If used by planning authorities or project developers, this type of model can help identify the most cost-effective setups, avoid overinvestment, and steer funds towards the most promising projects, accelerating the green hydrogen transition across Europe.
A curious detail: Optimising by the hour
One small but impactful detail is that my model doesn’t assume a fixed cost for electricity. Instead, it looks at every hour of the year and shifts hydrogen production to cheaper periods, for example, sunny midday hours in Spain or windy nights in Denmark. This kind of time-based optimisation gave my model an edge over standard tools and helped lower the LCOH.

• author: Huizenga, Stijn Frederik
• supervisor: Diamantis Almpantis
• organization: Department of Energy Sciences
• course: MVKM05 20251
• year: 2025
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: Green hydrogen, Sustainable mobility, Hydrogen for transport, Levelized Cost of Hydrogen (LCOH), Electrolyser optimization, Renewable energy integration, Techno-economic assessment, Hydrogen production cost, Investment optimization, Energy systems modelling, Pyomo, Gurobi optimizer, Scenario analysis, Dynamic simulation, System design optimization, Hourly energy modelling, Solar PV, Wind energy, Hydropower, Battery energy storage, Electrolysis (PEM & ALK), Grid electricity, Renewable-only systems.
• report number: ISRN LUTMDN/TMPH-25/5640-SE
• ISSN: 0282-1990
• language: English
• id: 9200342
• date added to LUP: 2025-06-23 08:59:02
• date last changed: 2025-06-23 08:59:02