{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wind farm optimisation - 2 GW of dedicated offshore wind for hydrogen production"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import cartopy.crs as ccrs\n",
"import contextily as cx\n",
"import geopandas as gpd\n",
"import mapclassify as mc\n",
"import matplotlib.patches as mpatches\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter\n",
"from matplotlib_scalebar.scalebar import ScaleBar\n",
"\n",
"from h2ss import capacity as cap\n",
"from h2ss import compare\n",
"from h2ss import data as rd\n",
"from h2ss import functions as fns\n",
"from h2ss import optimisation as opt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# basemap cache directory\n",
"cx.set_cache_dir(os.path.join(\"data\", \"basemaps\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Halite data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"ds, extent = rd.kish_basin_data_depth_adjusted(\n",
" dat_path=os.path.join(\"data\", \"kish-basin\"),\n",
" bathymetry_path=os.path.join(\"data\", \"bathymetry\"),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constraints"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# exploration wells\n",
"_, wells_b = fns.constraint_exploration_well(\n",
" data_path=os.path.join(\n",
" \"data\",\n",
" \"exploration-wells\",\n",
" \"Exploration_Wells_Irish_Offshore.shapezip.zip\",\n",
" )\n",
")\n",
"\n",
"# wind farms\n",
"wind_farms = fns.constraint_wind_farm(\n",
" data_path=os.path.join(\n",
" \"data\", \"wind-farms\", \"marine-area-consent-wind.zip\"\n",
" )\n",
")\n",
"\n",
"# frequent shipping routes\n",
"_, shipping_b = fns.constraint_shipping_routes(\n",
" data_path=os.path.join(\n",
" \"data\", \"shipping\", \"shipping_frequently_used_routes.zip\"\n",
" ),\n",
" dat_extent=extent,\n",
")\n",
"\n",
"# shipwrecks\n",
"_, shipwrecks_b = fns.constraint_shipwrecks(\n",
" data_path=os.path.join(\n",
" \"data\", \"shipwrecks\", \"IE_GSI_MI_Shipwrecks_IE_Waters_WGS84_LAT.zip\"\n",
" ),\n",
" dat_extent=extent,\n",
")\n",
"\n",
"# subsea cables\n",
"_, cables_b = fns.constraint_subsea_cables(\n",
" data_path=os.path.join(\"data\", \"subsea-cables\", \"KIS-ORCA.gpkg\"),\n",
" dat_extent=extent,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# distance from salt formation edge\n",
"edge_buffer = fns.constraint_halite_edge(dat_xr=ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zones of interest"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"zones, zds = fns.zones_of_interest(\n",
" dat_xr=ds,\n",
" constraints={\"net_height\": 120, \"min_depth\": 500, \"max_depth\": 2000},\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate caverns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"caverns = fns.generate_caverns_hexagonal_grid(\n",
" zones_df=zones,\n",
" dat_extent=extent,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"caverns = fns.cavern_dataframe(\n",
" dat_zone=zds,\n",
" cavern_df=caverns,\n",
" depths={\"min\": 500, \"min_opt\": 1000, \"max_opt\": 1500, \"max\": 2000},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# label caverns by depth and heights\n",
"caverns = fns.label_caverns(\n",
" cavern_df=caverns,\n",
" heights=[120],\n",
" depths={\"min\": 500, \"min_opt\": 1000, \"max_opt\": 1500, \"max\": 2000},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"metadata": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Without constraints...\n",
"Number of potential caverns: 568\n",
"------------------------------------------------------------\n",
"Excluding salt formation edges...\n",
"Number of potential caverns: 539\n",
"------------------------------------------------------------\n",
"Exclude shipping...\n",
"Number of potential caverns: 261\n",
"Caverns excluded: 51.58%\n",
"------------------------------------------------------------\n",
"Exclude wind farms...\n",
"Number of potential caverns: 218\n",
"Caverns excluded: 59.55%\n",
"------------------------------------------------------------\n",
"Exclude cables...\n",
"Number of potential caverns: 218\n",
"Caverns excluded: 59.55%\n",
"------------------------------------------------------------\n",
"Exclude wells...\n",
"Number of potential caverns: 218\n",
"Caverns excluded: 59.55%\n",
"------------------------------------------------------------\n",
"Exclude shipwrecks...\n",
"Number of potential caverns: 218\n",
"Caverns excluded: 59.55%\n",
"------------------------------------------------------------\n"
]
}
],
"source": [
"caverns, _ = fns.generate_caverns_with_constraints(\n",
" cavern_df=caverns,\n",
" exclusions={\n",
" \"wells\": wells_b,\n",
" \"wind_farms\": wind_farms,\n",
" \"shipwrecks\": shipwrecks_b,\n",
" \"shipping\": shipping_b,\n",
" \"cables\": cables_b,\n",
" \"edge\": edge_buffer,\n",
" },\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Capacity"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"caverns[\"cavern_total_volume\"] = cap.cavern_volume(\n",
" height=caverns[\"cavern_height\"]\n",
")\n",
"caverns[\"cavern_volume\"] = cap.corrected_cavern_volume(\n",
" v_cavern=caverns[\"cavern_total_volume\"]\n",
")\n",
"\n",
"caverns[\"t_mid_point\"] = cap.temperature_cavern_mid_point(\n",
" height=caverns[\"cavern_height\"], depth_top=caverns[\"cavern_depth\"]\n",
")\n",
"\n",
"(\n",
" caverns[\"p_operating_min\"],\n",
" caverns[\"p_operating_max\"],\n",
") = cap.pressure_operating(\n",
" thickness_overburden=caverns[\"TopDepthSeabed\"],\n",
" depth_water=-caverns[\"Bathymetry\"],\n",
")\n",
"\n",
"caverns[\"rho_min\"], caverns[\"rho_max\"] = cap.density_hydrogen_gas(\n",
" p_operating_min=caverns[\"p_operating_min\"],\n",
" p_operating_max=caverns[\"p_operating_max\"],\n",
" t_mid_point=caverns[\"t_mid_point\"],\n",
")\n",
"\n",
"(\n",
" caverns[\"working_mass\"],\n",
" caverns[\"mass_operating_min\"],\n",
" caverns[\"mass_operating_max\"],\n",
") = cap.mass_hydrogen_working(\n",
" rho_h2_min=caverns[\"rho_min\"],\n",
" rho_h2_max=caverns[\"rho_max\"],\n",
" v_cavern=caverns[\"cavern_volume\"],\n",
")\n",
"\n",
"caverns[\"capacity\"] = cap.energy_storage_capacity(\n",
" m_working=caverns[\"working_mass\"]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Power curve [MW] and Weibull wind speed distribution"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"# extract data for wind farms at 150 m\n",
"weibull_wf_df = fns.read_weibull_data(\n",
" data_path_weibull=os.path.join(\n",
" \"data\", \"weibull-parameters-wind-speeds\", \"Weibull_150m_params_ITM.zip\"\n",
" ),\n",
" data_path_wind_farms=os.path.join(\n",
" \"data\", \"wind-farms\", \"marine-area-consent-wind.zip\"\n",
" ),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"weibull_powercurve = opt.weibull_distribution(weibull_wf_data=weibull_wf_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of reference wind turbines"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"metadata": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"28.57% of total offshore wind farm capacity dedicated to H₂ production\n"
]
}
],
"source": [
"# 2 GW of offshore wind for green hydrogen production by 2030\n",
"# in addition to 5 GW offshore wind target in CLimate Action Plan 2023\n",
"# pg. 134\n",
"# assume this 2 GW is distributed evenly to the total capacity\n",
"print(\n",
" f\"{2 / 7 * 100:.2f}% of total offshore wind farm capacity dedicated \"\n",
" f\"to H\\N{SUBSCRIPT TWO} production\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"# max wind farm capacity\n",
"weibull_wf_df[\"cap\"] = [int(x * 2 / 7) for x in [1300, 824, 500]]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"# number of 15 MW turbines, rounded down to the nearest integer\n",
"weibull_wf_df[\"n_turbines\"] = opt.number_of_turbines(\n",
" owf_cap=weibull_wf_df[\"cap\"]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Annual energy production [MWh]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"weibull_wf_df = opt.annual_energy_production(weibull_wf_data=weibull_wf_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Annual hydrogen production [kg]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"weibull_wf_df[\"AHP\"] = opt.annual_hydrogen_production(aep=weibull_wf_df[\"AEP\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AHP as a proportion of the total working mass"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"weibull_wf_df[\"AHP_frac\"] = (\n",
" weibull_wf_df[\"AHP\"] / caverns[[\"working_mass\"]].sum().iloc[0]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AHP converted to storage demand [GWh]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"weibull_wf_df[\"demand\"] = cap.energy_storage_capacity(\n",
" m_working=weibull_wf_df[\"AHP\"]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of caverns required based on cumulative working mass and AHP"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"metadata": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Codling Wind Park\n",
"Working mass [kg]: 3.057689E+07\n",
"Number of caverns required: 7–17\n",
"Capacity (approx.) [GWh]: 1,087.05\n",
"------------------------------------------------------------------------------\n",
"Dublin Array\n",
"Working mass [kg]: 1.872921E+07\n",
"Number of caverns required: 4–11\n",
"Capacity (approx.) [GWh]: 656.97\n",
"------------------------------------------------------------------------------\n",
"North Irish Sea Array\n",
"Working mass [kg]: 1.231565E+07\n",
"Number of caverns required: 3–7\n",
"Capacity (approx.) [GWh]: 471.43\n",
"------------------------------------------------------------------------------\n",
"Total number of caverns required: 14–35\n",
"------------------------------------------------------------------------------\n",
"Number of caverns required as a percentage of all available caverns:\n",
"6.42–16.06%\n",
"------------------------------------------------------------------------------\n",
"Total maximum cavern capacity (approx.): 2,215.45 GWh\n"
]
}
],
"source": [
"compare.calculate_number_of_caverns(\n",
" cavern_df=caverns, weibull_wf_data=weibull_wf_df\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Transmission distance [km]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"caverns, injection_point = opt.transmission_distance(\n",
" cavern_df=caverns, wf_data=wind_farms\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Electrolyser capacity [MW]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"weibull_wf_df[\"E_cap\"] = opt.electrolyser_capacity(\n",
" n_turbines=weibull_wf_df[\"n_turbines\"]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CAPEX for pipeline [€ km⁻¹]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"weibull_wf_df[\"CAPEX\"] = opt.capex_pipeline(e_cap=weibull_wf_df[\"E_cap\"])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"metadata": {}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" cap | \n",
" (c, min) | \n",
" (c, max) | \n",
" (c, mean) | \n",
" (k, min) | \n",
" (k, max) | \n",
" (k, mean) | \n",
" n_turbines | \n",
" AEP | \n",
" integral | \n",
" abserr | \n",
" AHP | \n",
" AHP_frac | \n",
" demand | \n",
" E_cap | \n",
" CAPEX | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" Codling Wind Park | \n",
" 371 | \n",
" 10.2 | \n",
" 10.8 | \n",
" 10.500000 | \n",
" 1.9 | \n",
" 2.0 | \n",
" 1.950000 | \n",
" 24 | \n",
" 1.548908e+06 | \n",
" 8.335975 | \n",
" 2.528685e-07 | \n",
" 3.057689e+07 | \n",
" 0.043023 | \n",
" 1018.889987 | \n",
" 298 | \n",
" 953350.780836 | \n",
"
\n",
" \n",
" | 1 | \n",
" Dublin Array | \n",
" 235 | \n",
" 9.9 | \n",
" 10.6 | \n",
" 10.292857 | \n",
" 1.9 | \n",
" 2.0 | \n",
" 1.950000 | \n",
" 15 | \n",
" 9.487500e+05 | \n",
" 8.169630 | \n",
" 3.778893e-07 | \n",
" 1.872921e+07 | \n",
" 0.026353 | \n",
" 624.098782 | \n",
" 186 | \n",
" 868435.651965 | \n",
"
\n",
" \n",
" | 2 | \n",
" North Irish Sea Array | \n",
" 142 | \n",
" 10.7 | \n",
" 11.2 | \n",
" 10.950000 | \n",
" 2.1 | \n",
" 2.2 | \n",
" 2.133333 | \n",
" 9 | \n",
" 6.238637e+05 | \n",
" 8.953423 | \n",
" 1.516351e-07 | \n",
" 1.231565e+07 | \n",
" 0.017329 | \n",
" 410.384794 | \n",
" 112 | \n",
" 806313.935209 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" cavern_depth | \n",
" working_mass | \n",
" capacity | \n",
" distance_ip | \n",
" dist_Codling_Wind_Park | \n",
" dist_Dublin_Array | \n",
" dist_North_Irish_Sea_Array | \n",
" LCOT_Codling_Wind_Park | \n",
" LCOT_Dublin_Array | \n",
" LCOT_North_Irish_Sea_Array | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 218.000000 | \n",
" 2.180000e+02 | \n",
" 218.000000 | \n",
" 218.000000 | \n",
" 218.000000 | \n",
" 218.000000 | \n",
" 218.000000 | \n",
" 218.000000 | \n",
" 218.000000 | \n",
" 218.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 1154.020877 | \n",
" 3.260108e+06 | \n",
" 108.634041 | \n",
" 29.197826 | \n",
" 58.408245 | \n",
" 43.581633 | \n",
" 44.393167 | \n",
" 0.184989 | \n",
" 0.205274 | \n",
" 0.295240 | \n",
"
\n",
" \n",
" | std | \n",
" 365.142804 | \n",
" 7.786651e+05 | \n",
" 25.946851 | \n",
" 6.830597 | \n",
" 11.452952 | \n",
" 13.055932 | \n",
" 7.523248 | \n",
" 0.036273 | \n",
" 0.061495 | \n",
" 0.050034 | \n",
"
\n",
" \n",
" | min | \n",
" 502.745339 | \n",
" 1.678950e+06 | \n",
" 55.946359 | \n",
" 16.263066 | \n",
" 33.027322 | \n",
" 17.542931 | \n",
" 32.980512 | \n",
" 0.104603 | \n",
" 0.082629 | \n",
" 0.219339 | \n",
"
\n",
" \n",
" | 25% | \n",
" 872.651330 | \n",
" 2.685380e+06 | \n",
" 89.482842 | \n",
" 23.216441 | \n",
" 51.592318 | \n",
" 33.503367 | \n",
" 36.808911 | \n",
" 0.163402 | \n",
" 0.157804 | \n",
" 0.244800 | \n",
"
\n",
" \n",
" | 50% | \n",
" 1128.329146 | \n",
" 3.277441e+06 | \n",
" 109.211632 | \n",
" 30.947028 | \n",
" 58.988376 | \n",
" 47.378240 | \n",
" 44.619590 | \n",
" 0.186826 | \n",
" 0.223156 | \n",
" 0.296746 | \n",
"
\n",
" \n",
" | 75% | \n",
" 1433.385847 | \n",
" 3.886101e+06 | \n",
" 129.493525 | \n",
" 34.863247 | \n",
" 67.109072 | \n",
" 54.107289 | \n",
" 50.773606 | \n",
" 0.212546 | \n",
" 0.254851 | \n",
" 0.337673 | \n",
"
\n",
" \n",
" | max | \n",
" 1980.680682 | \n",
" 4.765555e+06 | \n",
" 158.798899 | \n",
" 42.990158 | \n",
" 80.806729 | \n",
" 70.176200 | \n",
" 67.926218 | \n",
" 0.255928 | \n",
" 0.330537 | \n",
" 0.451748 | \n",
"
\n",
" \n",
"
\n",
"