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H2INVEST functionality description

Purpose

Enable consumer-convenient and economic build-up of hydrogen infrastructure.

Achieved by:

  • Generating scenarios of regional hydrogen demand and HRS development to grant high consumer convenience (HRS availablity/siting)
  • Generating quasi-least-cost scenarios for supply infrastructure considering logics of markets and investors
  • Assessing scenario dependency on external factors (energy & equipment costs, subsidies, etc.)
  • Producing equipment-list type outputs for subsequent calculation of business models
  • Producing detailed (per individual HRS) outputs to assess interdependencies between location, energy pathway, GHG emissions, costs etc. and develop balanced subsidy/cross-financing schemes

Inputs

  • Selection criteria for hydrogen fuel demand (population, car density, purchase power)
  • Share of population to be supplied with hydrogen per time
  • Overall number of vehicles per time
  • Specific hydrogen demand per vehicle per time
  • Overall number of HRS per time
  • Conventional refuelling stations (locations, turnover, site data)
  • HRS, production, transport technology data (investment, O&M, energy use, lifetime, subsidies)
  • Energy data (GHG, costs per time)
  • Existing infrastructure (pipelines, liquefiers: locations, available capacity)
  • Production locations (coordinates, energy type availability)
  • Road distances between production locations and HRS
  • Business case data (classification into business divisions, transfer price system, financial numbers)

General Approach

A consecutive 2-step economic optimization model, split in H2-demand identification and H2-distribution/supply optimization.

Spatially distributed hydrogen demand

The overall hydrogen demand of the studied region is defined exogenously (vehicle numbers * specific hydrogen demand). The spatial distribution of hydrogen demand to demographic areas within the region is calculated based on a partially statistical set of criteria: population and car density, purchasing power, neighbouring regions with H2 supply, visibility of measures, political support, innovativeness of an area and willingness to switch to alternative fuels. Based on these criteria, for every time step a list of areas where hydrogen demand exists is created, and the hydrogen demand of every area is calculated based on local growth functions to match the overall demand.

Hydrogen Refuelling Station (HRS) siting

The spatial demand is translated into an optimum set of HRS locations following logistic aspects and existing fuelling station patterns. HRS within cities and towns are sited based on cluster analyses, and based on a maximum mesh size for inter-city corridors. Criteria of conventional refuelling stations such as motorfuel sales, number of fuelling positions, visibility and accessibility can be respected in the siting routine. The spatial demand is assigned to the chosen HRS locations.

Example: HRS distribution in Berlin, Germany 2020

Integrated hydrogen supply

For each consecutive time step (e.g. 1 year), the model calculates an integrated solution of individual supply chains to all sited hydrogen refuelling stations (up to several 1000s), including usage of existing facilities, siting of new production plants (central or onsite) and liquefiers and least-cost distribution (pipeline grids, gaseous/liquid hydrogen truck delivery). The solution is improved until no further cost minimization can be achieved. Exogenous restrictions can be specified at any level (e.g. limited availability of feedstocks at production locations; practicality of onsite technology at specific HRS, practicality of certain pipeline routes). Continuity of once installed/purchased equipment throughout its lifetime is respected. Mobile equipment (i.e. onsite hydrogen production, refuelling units) may be moved to new locations at every new time step.

Business and consortium models

Based on infrastructure optimization results the business module provides financial evaluation of consortium models by creating business divisions out of the required equipment types, units and regions, defining transfer prices between the divisions along entire value chain and calculating relevant key performance indicators. The model allows a flexible scenario-specific definition of involved investors represented by different business divisions and an in-depth analysis of contributions between them. It also takes into account both general economic factors such as inflation, company taxation and technology subsidies and business-specific numbers such as assets depreciation and valuation under International Financial Reporting Standards (IFRS), management overhead allocation, working capital management and target capital structure. Also the third-party contributions (e.g. subsidies) required to trigger investments can be determined.

Scenario analysis

The effects of different external conditions (e.g. policy support measures, equipment costs, energy prices) on the least-cost solution (production and distribution portfolio) can be evaluated by scenario analyses. Also, impacts of investment strategies (risk-friendly, risk-aversity) and technology failures can be tested. With the business module, the effect of different consortium models and pricing strategies on the different players (divisions) of a consortium can be evaluated.

Results

  • H2 demand, H2 fuel costs and CO2 emissions at each individual HRS
  • Production plant types, sizes, sites and numbers
  • Feedstock demand by site
  • H2 distribution technologies, pathways, transported H2-quantities by route
  • Production costs, distribution costs, GHG emissions aggregated
  • Business results (KPI and discounted cash flow) per division
  • Visualisation of results with Geographic Information System (GIS) software

Hamburg 2020

Example: Production and distribution in Hamburg, 2020 (Case Study Hamburg+Berlin)

 

Berlin 2020

Example: Production and distribution in Berlin, 2020 (Case Study Hamburg+Berlin)

 

Example: Average hydrogen costs and overall scenario costs (Case Study Hamburg+Berlin)

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