GeoX

Geospatial analysis of commodity production costs

GeoX calculates the locational cost of production, storage, transport, and conversion of different commodities to meet demand in a specified location. These costs can be compared to current or projected prices in the region under study to assess competitiveness. Currently, green hydrogen and green ammonia are implemented as commodities in GeoX. However, the code is written in a modular way to allow further commodities to be implemented.

The model outputs the levelised cost (LC) of the specified commodity at the specified demand location including production, storage, transport, and conversion costs from each production location.

In the code provided, the use case of hydrogen and ammonia production in Namibia is provided as an example. Parameter references for the hydrogen case are attached. However, as the code is written in a generalized way, it is possible to analyse all sorts of regions.

GeoX builds upon a preliminary code iteration produced by Leander Müller, available under a CC-BY-4.0 licence: https://github.com/leandermue/GEOH2. It also integrates code produced by Nick Salmon under an MIT licence: https://github.com/nsalmon11/LCOH_Optimisation

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Setup instructions

Follow the steps below before running GeoX. Note that GeoX is implemented using Snakemake. This lets you automate code execution using different "rules". When we refer to rules below, we're just talking about running different parts of the codebase that execute different functionalities.

1) Clone the repository

First, clone the GeoX repository using git.

... % git clone https://github.com/ClimateCompatibleGrowth/GeoX.git

2) Set up the environment

The python package requirements are in the environment.yaml file. You can install these requirements in a new environment using the mamba package and environment manager (installation instructions here):

.../GEOX % mamba env create -f environment.yaml

Then activate this new environment using:

.../GEOX % mamba activate geox

3) Get a Climate Data Store API key

To simulate commodities which use renewable energy for production, historical climate data is needed as an input. The get_weather_data rule can be used to download the relevant data from the ERA-5 reanalysis dataset using Atlite to create a cutout. For this process to work, you need to register and set up your CDS API key as described on the Climate Data Store website.

4) Install a solver

For the plant_optimization rule to work, you will need a solver installed on your computer. You can use any solver that works with PyPSA, such as Cbc, a free, open-source solver, or Gurobi, a commerical solver with free academic licenses available. Install your solver of choice following the instructions for use with Python and your operating system in the solver's documentation.

Note

Snakemake uses Cbc as a solver by default. This will be installed upon environment setup. To check that this is installed, activate your environment and enter mamba list in your terminal for the environment's list of packages. If you choose to use Cbc, no additional solver needs to be installed.

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Preparing input data

Next, you'll need to prepare input data. GeoX has two types of input data: (1) spatial input data as a hexagon shapefile; and (2) techno-economic parameters in spreadsheets.

1) Prepare input hexagons

First, prepare spatial input data as a set of hexagons using the H3 standard. These hexagons are provided in the repository for the illustrative case study of Namibia. To analyse a different area of interest, the hexagon file needs to be changed, but needs to follow the logic of the one provided.

A full walkthrough on making these hexagons, including tools to create them, are available in the GeoH2-data-prep repository. The hexagon file needs to have the following attributes:

• waterbody_dist: Distance to selected waterbodies in area of interest
• waterway_dist: Distance to selected waterways in area of interest
• ocean_dist: Distance to ocean coastline
• grid_dist: Distance to transmission network
• road_dist: Distance to road network
• theo_pv: Theoretical potential of standardized PV plants (can be determined with GLAES)
• theo_wind: Theoretical potential of standardized wind turbines (can be determined with GLAES)

The ccg-spider repository can be used to combine different spatial data into standard H3 hexagons. Once you have created a hexagon file with these features, save it in the data folder as hex_final_[COUNTRY ISO CODE].geojson.

Important

COUNTRY ISO CODE is the country's ISO standard 2-letter abbreviation.

2) Prepare input parameter Excel files

Next, prepare the techno-economic input parameter spreadsheets. Required input parameters include the spatial area of interest, total annual demand for the commodity, and prices and cost of capital for infrastructure investments. These values can be either current values or projected values for a single snapshot in time. The parameter values for running the model can be specified in a set of Excel files in the parameters folder.

• Basic plant: The basic_[COMMODITY ABBREVIATION]_plant folder contains several csv files containing the global parameters for optimizing the plant design. All power units are MW and all energy units are MWh. For more information on these parameters, refer to the PyPSA documentation.

Important

COMMODITY ABBREVIATION can be 'h2' or 'nh3' for currently implemented commodities. The excel files must be kept in a folder with the commodity name within another folder with the title matching the Country ISO Code. As currently implemented, the commodity must be either "hydrogen" or "ammonia". For the illustrative case of Namibia, we have them in a folder titled "NA" with two sub-folders "hydrogen" and "ammonia".

• Conversion parameters: conversion_parameters.xlsx includes parameters related to converting between states of the commodity. This is only needed in the "hydrogen" folder.

• Country parameters: country_parameters.xlsx includes country- and technology-specific interest rates, heat and electricity costs, and asset lifetimes.

  • Interest rates should be expressed as a decimal, e.g. 5% as 0.05.
  • Asset lifetimes should be in years.

• Demand parameters: demand_parameters.xlsx includes a list of demand centers. For each demand center, its lat-lon location, annual demand, and commodity state for that demand must be specified.

• Pipeline parameters: pipeline_parameters.xlsx includes the price, capacity, and lifetime data for different sizes of pipeline.

• Technology parameters: technology_parameters.xlsx includes water parameters, road infrastructure parameters, and whether road and pipeline construction is allowed.

• Transport parameters: transport_parameters.xlsx includes the parameters related to road transport of the commodity, including truck speed, cost, lifetime, and capacity.

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Running GeoX with Snakemake

Once you've done the repository setup and prepared your input data, you're ready to run GeoX!

This repository uses Snakemake to automate its workflow (for a gentle introduction to Snakemake, see Getting Started with Snakemake on The Carpentries Incubator).

1) Modify the config file

High-level workflow settings are controlled in the config file: config.yaml.

Wildcards: These are used in the scenario section of the config file to specify the data used in the workflow. They can be changed to match the case you're analysing. They are:

• country: an ISO standard 2-letter abbreviation
• weather_year: a 4-digit year between 1940 and 2023 included in the ERA5 dataset
• plant_type: the commodity to be produced (currently either "hydrogen" or "ammonia")

Weather data: The amount of years you want to download weather data for should be added into years_to_check. For instance, if you want your weather data to start in 2015 and span five years, weather_year should be 2015 and years_to_check should be 5. You can set the frequency of data to be used in optimisation using freq (i.e., "1H" for hourly, "3H" for three-hourly, etc.)

Generators: The types of renewable generators considered for plant construction are included in the generators_dict section. Currently, only Solar and Wind can be considered. Dependent on which generators you are using, you can change the panel value for Solar and the turbine value for Wind. In the gen_capacity section, you will find both solar and wind, which can be changed to match values that you are analysing.

Other: You will have to set the solver to the solver name that you are going to be using. You will also have to set whether a water_limit is True or False (i.e., whether you want to consider water scarcity in your process). In the transport section, pipeline_construction and road_construction can be switched from True to False, as needed.

Note

country and weather_year can be a list of more than one, depending on how many countries and years you are analysing. You must ensure all other input files that are needed for each country are where they should be as previously described.

2) Run GeoX rules

GeoX assumes that you will run it using Snakemake by entering rule names in the terminal. When you do this, Snakemake will run all the necessary rules to achieve the rule you entered and create the desired output. Rules are defined in the Snakefile.

Snakemake requires a specification of the NUMBER OF CORES TO BE USED when you run a rule. This can be up to 4.

Each rule has a different expected run time:

• The get_weather_data rule, depending on country size and your internet connection, could take from a few minutes to several hours to run. Ensure that you have space on your computer to store the data, which can be several GB.
• The optimize_plant rule, depending on country size and the number of demand centers, could take from several minutes to several hours to run.
• The optimize_transport rule, depending on country size, should take a few minutes to run.
• All other rules take a few seconds to run.

1) Get weather data (if needed)

If you already have weather data for your area of interest, just use step 2! If not, you need to run two rules. First, run the prep rule to: (1) assign country-specific interest rates, technology lifetimes, and heat and electricity prices from country_parameters.xlsx to different hexagons based on their country; and (2) drop any duplicated hexagons that do not belong to the country that is to be run for.

snakemake -j [NUMBER OF CORES TO BE USED] run_prep

Then, run the weather data which will download the data you need from the CDS API:

snakemake -j [NUMBER OF CORES TO BE USED] run_weather

2) Run optimization or mapping rules

Finally, run the GeoX optimization process. These rules are used to run the whole process without having to go through each rule step-by-step. If any input files are changed after a completed run, the same command can be used again and Snakemake will only run the necessary scripts to ensure the results are up to date.

The total commodity cost for all scenarios can be run by entering the following rule into the terminal. Make sure you have the necessary weather file(s) in the cutouts folder before running.

snakemake -j [NUMBER OF CORES TO BE USED] optimise_all

Similarly, you can map commodity costs for all scenarios with the following rule:

snakemake -j [NUMBER OF CORES TO BE USED] map_all

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Definitions of all rules

While all rules are discussed here for completeness, you do not need to enter each rule one-by-one. You can simply run one of the optimization or mapping rules, and Snakemake will ensure that all required rules are completed to achieve this. Please refer to the steps in the previous section for most usages.

prep_main rule

This will assign country-specific interest rates, technology lifetimes, and heat and electricity prices from country_parameters.xlsx to different hexagons based on their country. As well as, drop any duplicated hexagons that do not belong to the country that is to be run for.

snakemake -j [NUMBER OF CORES TO BE USED] run_prep

get_weather_data rule

This will download weather data from your area of interest for generation optimization. Make sure you have run the prep_main rule before running this rule as it needs the outputted file as an input.

snakemake -j [NUMBER OF CORES TO BE USED] run_weather

optimize_transport rule

Note

PLANT TYPE is the commodity to be produced (currently either "hydrogen" or "ammonia"). This will calculate the cost of the optimal commodity transportation strategy from each hexagon to each demand center, using both pipelines and road transport, and parameters from technology_parameters.xlsx, demand_parameters.xlsx, and country_parameters.xlsx. This will also take into account the costs for conversion needed for hydrogen to a different demand state.

snakemake -j [NUMBER OF CORES TO BE USED] resources/hex_transport_[COUNTRY ISO CODE]_[PLANT TYPE].geojson

calculate_water_costs rule

This will calculate water costs from the ocean and freshwater bodies for commodity production in each hexagon using parameters/technology_parameters.xlsx and parameters/country_parameters.xlsx.

snakemake -j [NUMBER OF CORES TO BE USED] resources/hex_water_[COUNTRY ISO CODE]_[PLANT TYPE].geojson

optimize_plant rule

This will design a plant to meet the commodity demand profile for each demand center for each transportation method to each demand center. Ensure that you have specified your plant parameters in the parameters/basic_[COMMODITY ABBREVIATION]_plant folder, and your demand centers in parameters/demand_parameters.xlsx.

snakemake -j [NUMBER OF CORES TO BE USED] resources/hex_lc_[COUNTRY ISO CODE]_[WEATHER YEAR]_[PLANT TYPE].geojson

calculate_total_cost rule

This will combine results to find the lowest-cost method of producing, transporting, and converting the commodity for each demand center.

snakemake -j [NUMBER OF CORES TO BE USED] results/hex_total_cost_[COUNTRY ISO CODE]_[WEATHER YEAR]_[PLANT TYPE].geojson

calculate_cost_components rule

This will calculate the cost for each type of equipment in each polygon.

snakemake -j [NUMBER OF CORES TO BE USED] results/hex_cost_components_[COUNTRY ISO CODE]_[WEATHER YEAR]_[PLANT TYPE].geojson

calculate_map_costs rule

This will visualize the spatial variation in different costs per kilogram of hydrogen.

snakemake -j [NUMBER OF CORES TO BE USED] plots/[COUNTRY ISO CODE]_[WEATHER YEAR]_[PLANT TYPE]

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Limitations

This model considers only greenfield generation. Therefore it does not consider using grid electricity or existing generation. The model further assumes that all excess electricity is curtailed.

While the design of the commodity plant is convex and therefore guarenteed to find the global optimum solution if it exists, the selection of the trucking strategy is greedy to avoid the long computation times and potential computational intractability associated with a mixed-integer optimization problem.

Currently, only land transport is considered in the model. To calculate the cost of commodity production for export, any additional costs for conversion and transport via ship or undersea pipeline must be added in post-processing.

Transport costs are calculated from the center of the hexagon to the demand center. When using large hexagon sizes, this assumption may over- or underestimate transportation costs significantly. Additionally, only path length is considered when calculating the cost of road and pipeline construction. Additional costs due to terrain are not considered.

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Citation

If you decide to use GeoX, please kindly cite us using the following:

Halloran, C., Leonard, A., Salmon, N., Müller, L., & Hirmer, S. (2024). GeoH2 model: Geospatial cost optimization of green hydrogen production including storage and transportation. MethodsX, 12, 102660. https://doi.org/10.1016/j.mex.2024.102660.

@article{Halloran_GeoH2_model_Geospatial_2024,
author = {Halloran, Claire and Leonard, Alycia and Salmon, Nicholas and Müller, Leander and Hirmer, Stephanie},
doi = {10.1016/j.mex.2024.102660},
journal = {MethodsX},
month = jun,
pages = {102660},
title = {{GeoH2 model: Geospatial cost optimization of green hydrogen production including storage and transportation}},
volume = {12},
year = {2024}
}

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Case study parameters

This repository includes sample parameters for a hydrogen production case in Namibia. References for these parameters are included in the tables below for reference. For the results of this case, please refer to the GeoH2 model MethodsX article: https://doi.org/10.1016/j.mex.2024.102660.

Green hydrogen plant parameters:

Hardware	Parameter	Value	Units	Ref.
Solar photovoltaic	Capex	1,470,000	€/MW	Allington et al., 2021
Wind turbines	Capex	1,580,000	€/MW	Allington et al., 2021
Hydrogen electrolysis	Capex	1,250,000	€/MW	Müller et al., 2022
Hydrogen electrolysis	Efficiency	0.59	MWh H2/MWh el	Taibi et al., 2020
Hydrogen compression	Isentropic efficiency	0.051	MWh el/MWh H2	Müller et al., 2022
Hydrogen storage unloading	Efficiency	1	MWh H2/MWh H2-stored	Assumption
Battery	Capex	95,000	€/MW	BloombergNEF, 2022
Hydrogen storage	Capex	21,700	€/MWh	Müller et al., 2022

Conversion parameters:

Process	Parameter	Value	Units	Ref.
500 bar compression	Heat capacity	0.0039444	kWh/kg/K	Kurzweil and Dietlmeier, 2016
500 bar compression	Input temperature	298.15	K	Müller et al., 2022
500 bar compression	Input pressure	25	bar	Müller et al., 2022
500 bar compression	Isentropic exponent	1.402		Kurzweil and Dietlmeier, 2016
500 bar compression	Isentropic efficiency	0.8		Müller et al., 2022
500 bar compression	Compressor lifetime	15	years	Cerniauskas, 2021
500 bar compression	Compressor capex coefficient	40,035	€/kg H2/day	Cerniauskas, 2021
500 bar compression	Compressor opex	4	% capex/year	Cerniauskas, 2021
Hydrogen liquification	Electricity demand	9.93	kWh/kg H2	Ausfelder and Dura
Hydrogen liquification	Capex quadratic coefficient	-0.0002	€/(kg H2)^2	Müller et al., 2022
Hydrogen liquification	Capex linear coefficient	1,781.9	€/kg H2	Müller et al., 2022
Hydrogen liquification	Capex constant	300,000,000	€	Müller et al., 2022
Hydrogen liquification	Opex	8	% capex/year	Cerniauskas, 2021
Hydrogen liquification	Plant lifetime	20	years	Cerniauskas, 2021
LOHC hydrogenation	Electricity demand	0.35	kWh/kg H2	Andersson and Grönkvist, 2019
LOHC hydrogenation	Heat demand	-9	kWh/kg H2	Hydrogenious, 2022
LOHC hydrogenation	Capex coefficient	0.84	kWh/kg H2/year	IEA, 2020
LOHC hydrogenation	Opex	4	% capex/year	IEA, 2020
LOHC hydrogenation	Plant lifetime	25	years	IEA, 2020
LOHC hydrogenation	Carrier costs	2	€/kg carrier	Clark, 2020
LOHC hydrogenation	Carrier ratio	16.1	kg carrier/kg H2	Arlt and Obermeier, 2017
LOHC dehydrogenation	Electricity demand	0.35	kWh/kg H2	Andersson and Grönkvist, 2019
LOHC dehydrogenation	Heat demand	12	kWh/kg H2	Hydrogenious, 2022
LOHC dehydrogenation	Capex coefficient	2.46	kWh/kg H2	IEA, 2020
LOHC dehydrogenation	Opex	4	% capex/year	IEA, 2020
LOHC dehydrogenation	Plant lifetime	25	years	IEA, 2020
Ammonia synthesis	Electricity demand	2.809	kWh/kg H2	IEA, 2021
Ammonia synthesis	Capex coefficient	0.75717	kWh/g H2/year	IEA, 2021
Ammonia synthesis	Opex	1.5	% capex/year	IEA, 2020
Ammonia synthesis	Plant lifetime	25	years	IEA, 2020
Ammonia cracking	Heat demand	4.2	kWh/kg H2	Andersson and Grönkvist, 2019
Ammonia cracking	Capex coefficient	17,262,450	kWh/g H2/hour	Cesaro et al., 2021
Ammonia cracking	Opex	2	% capex/year	Müller et al., 2022
Ammonia cracking	Plant lifetime	25	years	Müller et al., 2022

Trucking parameters:

Hardware	Parameter	Value	Units	Ref.
All trucks	Average truck speed	70	km/h	Assumption
All trucks	Working hours	24	h/day	Assumption
All trucks	Diesel price	1.5	€/L	Assumption
All trucks	Driver wage	2.85	€/h	Müller et al., 2022
All trucks	Working days	365	days/year	Assumption
All trucks	Max driving distance	160,000	km/year	Müller et al., 2022
All trucks	Truck capex	160,000	€	Reuss et al., 2017
All trucks	Truck Opex	12	% capex/year	Reuss et al., 2017
All trucks	Diesel consumption	35	L/100 km	Reuss et al., 2017
All trucks	Truck lifetime	8	years	Reuss et al., 2017
All trucks	Trailer lifetime	12	years	Reuss et al., 2017
500 bar hydrogen trailer	Trailer capex	660,000	€	Cerniauskas, 2021
500 bar hydrogen trailer	Trailer opex	2	% capex/year	Cerniauskas, 2021
500 bar hydrogen trailer	Trailer capacity	1,100	kg H2	Cerniauskas, 2021
500 bar hydrogen trailer	Loading and unloading time	1.5	hours	Cerniauskas, 2021
Liquid hydrogen trailer	Trailer capex	860,000	€	Reuss et al., 2017
Liquid hydrogen trailer	Trailer opex	2	% capex/year	Reuss et al., 2017
Liquid hydrogen trailer	Trailer capacity	4,300	kg H2	Reuss et al., 2017
Liquid hydrogen trailer	Loading and unloading time	3	hours	Reuss et al., 2017
LOHC trailer	Trailer capex	660,000	€	IEA, 2020
LOHC trailer	Trailer opex	2	% capex/year	Reuss et al., 2017
LOHC trailer	Trailer capacity	1,800	kg H2	Reuss et al., 2017
LOHC trailer	Loading and unloading time	1.5	hours	Reuss et al., 2017
Ammonia trailer	Trailer capex	210,000	€	IEA, 2020
Ammonia trailer	Trailer opex	2	% capex/year	IEA, 2020
Ammonia trailer	Trailer capacity	2,600	kg H2	IEA, 2020
Ammonia trailer	Loading and unloading time	1.5	hours	IEA, 2020

Road parameters:

Road length	Parameter	Value	Units	Ref.
Short road (<10 km)	Capex	626,478.45	€/km	Müller et al., 2022
Long road (>10 km)	Capex	481,866.6	€/km	Müller et al., 2022
All roads	Opex	7,149.7	€/km/year	Müller et al., 2022

Pipeline parameters:

Pipeline size	Parameter	Value	Units	Ref.
All pipelines	Opex	1.25	% capex/year	Jens et al., 2021
All pipelines	Availability	95	%	Müller et al., 2022
All pipelines	Pipeline lifetime	42.5	years	Jens et al., 2021
All pipelines	Compressor lifetime	24	years	Jens et al., 2021
All pipelines	Electricity demand	0.000614	kWh/kg H2/km	Jens et al., 2021
Large pipeline	Maximum capacity	13	GW	Jens et al., 2021
Large pipeline	Pipeline capex	2,800,000	€/km	Jens et al., 2021
Large pipeline	Compressor capex	620,000	€/km	Jens et al., 2021
Medium pipeline	Maximum capacity	4.7	GW	Jens et al., 2021
Medium pipeline	Pipeline capex	2,200,000	€/km	Jens et al., 2021
Medium pipeline	Compressor capex	310,000	€/km	Jens et al., 2021
Small pipeline	Maximum capacity	1.2	GW	Jens et al., 2021
Small pipeline	Pipeline capex	90,000	€/km	Jens et al., 2021
Small pipeline	Compressor capex	90,000	€/km	Jens et al., 2021

Water parameters:

Type	Parameter	Value	Units	Ref.
Freshwater	Treatment electricity demand	0.4	kWh/m^3 water	US Dept. of Energy, 2016
Ocean water	Treatment electricity demand	3.7	kWh/m^3 water	Patterson et al., 2019
All water	Transport cost	0.1	€/100 km/m^3 water	Zhou and Tol, 2005
All water	Water specific cost	1.25	€/m^3 water	Wasreb, 2019
All water	Water demand	21	L water/kg H2	Taibi et al., 2020

Country-specific parameters:

Country	Parameter	Value	Units	Ref.
Namibia	Electricity price	0.10465	€/kWh	GlobalPetrolPrices.com
Namibia	Heat price	0.02	€/kWh	Assumption
Namibia	Solar interest rate	6	%	Assumption
Namibia	Solar lifetime	20	years	Assumption
Namibia	Wind interest rate	6	%	Assumption
Namibia	Wind lifetime	20	years	Assumption
Namibia	Plant interest rate	6	%	Assumption
Namibia	Plant lifetime	20	years	Assumption
Namibia	Infrastructure interest rate	6	%	Assumption
Namibia	Infrastructure lifetime	50	years	Müller et al., 2022