Compare / validate results

Functions to compare and validate results.

References

[1]

Deane, P. (2021) Our Climate Neutral Future: Zero by 50. Wind Energy Ireland. Available at: https://windenergyireland.com/images/files/our-climate-neutral-future-0by50-final-report.pdf (Accessed: 8 February 2024).

[2]

Pashchenko, D. (2024) ‘Green hydrogen as a power plant fuel: What is energy efficiency from production to utilization?’, Renewable Energy, 223, p. 120033. Available at: https://doi.org/10.1016/j.renene.2024.120033.

[3]

The Royal Society (2023) Large-scale electricity storage. London: The Royal Society. Available at: https://royalsociety.org/electricity-storage (Accessed: 15 September 2023).

[4]

Department of the Environment, Climate and Communications (2023) National Hydrogen Strategy. Government of Ireland. Available at: https://www.gov.ie/en/publication/624ab-national-hydrogen-strategy/ (Accessed: 25 July 2023).

class h2ss.compare.HiddenPrints

Suppress print statements: https://stackoverflow.com/a/45669280

h2ss.compare.electricity_demand_ie(data)

Compare the capacity to Ireland’s electricity demand in 2050.

Parameters

datapandas.Series

Pandas series or dataframe column of capacities

Notes

Figures from [1]. Assume that the conversion of hydrogen to electricity is 50% efficient; When fuel with 100% H2 is used, higher heating value and lower heating value efficiency are 48.7% and 57.5%, respectively [2]. This does not account for transmission losses. Assume that the hydrogen demand is 17% of the electricity demand, based on the Royal Society report on energy storage [3].

h2ss.compare.hydrogen_demand_ie(data)

Compare the capacity to Ireland’s hydrogen demand in 2050.

Parameters

datapandas.Series

Pandas series or dataframe column of capacities

Notes

Data from the National Hydrogen Strategy [4].

h2ss.compare.distance_from_pipeline(cavern_df, pipeline_data_path)

Calculate the distance of the caverns from the nearest pipeline.

Parameters

cavern_dfgeopandas.GeoDataFrame

Dataframe of potential caverns

pipeline_data_pathstr

Path to the offshore pipeline Shapefile data

h2ss.compare.calculate_number_of_caverns(cavern_df, weibull_wf_data)

Calculate the number of caverns required by each wind farm.

Parameters

cavern_dfgeopandas.GeoDataFrame

Dataframe of potential caverns

weibull_wf_datapandas.DataFrame

Dataframe of the Weibull distribution parameters for the wind farms

h2ss.compare.load_all_data(keep_orig=False)

Load all input datasets.

Parameters

keep_origbool

Whether to keep the original constraints datasets after buffering

Returns

tuple[xarray.Dataset, geopandas.GeoDataFrame, dict[str, geopandas.GeoDataFrame]]

The halite data, extent, and exclusions

h2ss.compare.capacity_function(ds, extent, exclusions, cavern_diameter, cavern_height)

Calculate the energy storage capacity for different cases.

Parameters

dsxarray.Dataset

Xarray dataset of the halite data

extentgeopandas.GeoSeries

Extent of the data

exclusionsdict[str, geopandas.GeoDataFrame]

Dictionary of exclusions data

cavern_diameterfloat

Diameter of the cavern [m]

cavern_heightfloat

Height of the cavern [m]

Returns

tuple[geopandas.GeoDataFrame, geopandas.GeoDataFrame]

Dataframes of the caverns and zones of interest

Notes

Uses the defaults apart from the changing cavern diameters and heights.

h2ss.compare.optimisation_function(ds, extent, exclusions, cavern_diameter, cavern_height)

Run all capacity and optimisation functions.

Parameters

dsxarray.Dataset

Xarray dataset of the halite data

extentgeopandas.GeoSeries

Extent of the data

exclusionsdict[str, geopandas.GeoDataFrame]

Dictionary of exclusions data

cavern_diameterfloat

Diameter of the cavern [m]

cavern_heightfloat

Height of the cavern [m]

Returns

tuple[geopandas.GeoDataFrame, geopandas.GeoDataFrame, geopandas.GeoDataFrame, geopandas.GeoSeries]

Dataframes of the caverns, zones, Weibull parameters, and injection point

Notes

Uses the defaults apart from the changing cavern diameters and heights.