├── README.md ├── .gitignore ├── environment.yaml ├── .pre-commit-config.yaml ├── data └── connection.csv ├── src ├── availabiltiy.ipynb ├── sankey.ipynb └── plots.ipynb └── LICENSE /README.md: -------------------------------------------------------------------------------- 1 | # Dashboard for PyPSA-Eur-Sec 2 | 3 | Based on Holoviews. 4 | 5 | ## Deploy 6 | 7 | with `heroku` -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *__pycache__ 2 | .snakemake 3 | .vscode 4 | dconf 5 | results 6 | *.ipynb_checkpoints 7 | #*.ipynb 8 | summary 9 | *.nc 10 | *.log 11 | *.pdf 12 | *.png 13 | *.html 14 | 15 | -------------------------------------------------------------------------------- /environment.yaml: -------------------------------------------------------------------------------- 1 | name: 2 | pypsa-dashboard 3 | 4 | channels: 5 | - conda-forge 6 | 7 | dependencies: 8 | - python 9 | - pip 10 | - ipython 11 | - pre-commit 12 | - mamba 13 | - yaml 14 | - pypsa 15 | - cartopy 16 | - networkx 17 | - hvplot 18 | - geoviews 19 | - nodejs 20 | - bokeh 21 | - holoviews 22 | - xarray 23 | - pandas 24 | - geopandas 25 | - numpy 26 | - panel 27 | - jupyterlab 28 | - atlite 29 | - pyepsg 30 | - plotly 31 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/pre-commit/pre-commit-hooks 3 | rev: v4.3.0 4 | hooks: 5 | - id: trailing-whitespace 6 | #- id: end-of-file-fixer 7 | - id: check-yaml 8 | - id: check-merge-conflict 9 | - id: check-added-large-files 10 | args: ['--maxkb=1000'] 11 | # - repo: https://github.com/fsfe/reuse-tool 12 | # rev: latest 13 | # hooks: 14 | # - id: reuse 15 | - repo: https://github.com/psf/black 16 | rev: 22.10.0 17 | hooks: 18 | - id: black 19 | - repo: https://github.com/aflc/pre-commit-jupyter 20 | rev: v1.2.1 21 | hooks: 22 | - id: jupyter-notebook-cleanup 23 | args: 24 | - --remove-kernel-metadata 25 | -------------------------------------------------------------------------------- /data/connection.csv: -------------------------------------------------------------------------------- 1 | source,target,value 2 | electricity,central air heat pump,1 3 | electricity,central resistive heater,1 4 | H2,fuel cell,1 5 | solid biomass,biomass CHP,1 6 | CH4,central gas boiler,1 7 | CH4,gas CHP,1 8 | central air heat pump,district heating,1 9 | central resistive heater,district heating,1 10 | fuel cell,district heating,1 11 | biomass CHP,district heating,1 12 | central gas boiler,district heating,1 13 | gas CHP,district heating,1 14 | electricity distribution,air heat pump,1 15 | electricity distribution,resistive heater,1 16 | CH4 distribution,gas boiler,1 17 | electricity distribution,ground heat pump,1 18 | air heat pump,urban individual,1 19 | resistive heater,urban individual,1 20 | gas boiler,urban individual,1 21 | gas boiler,rural individual,1 22 | ground heat pump,rural individual,1 23 | central solar thermal,district heating,1 24 | solar thermal,urban individual,1 25 | solar thermal,rural individual,1 26 | urban individual,urban residential,1 27 | urban individual,urban service,1 28 | rural individual,rural residential,1 29 | rural individual,rural service,1 30 | solar energy,solar thermal,1 31 | solar energy,central solar thermal,1 32 | electricity,electricity distribution,1 33 | CH4,CH4 distribution,1 34 | -------------------------------------------------------------------------------- /src/availabiltiy.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "73b1f3a7-3535-44b1-ac4d-c37fe577e1ef", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import xarray as xr\n", 11 | "import numpy as np\n", 12 | "import hvplot.xarray\n", 13 | "from rasterio.warp import transform" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "id": "ae426cf2-fc91-4559-9773-cebb2cb27270", 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def get_latlon(da):\n", 24 | " \n", 25 | " # Compute the lon/lat coordinates with rasterio.warp.transform\n", 26 | " ny, nx = len(da['y']), len(da['x'])\n", 27 | " x, y = np.meshgrid(da['x'], da['y'])\n", 28 | "\n", 29 | " # Rasterio works with 1D arrays\n", 30 | " lon, lat = transform(da.crs, {'init': 'EPSG:4326'},\n", 31 | " x.flatten(), y.flatten())\n", 32 | " lon = np.asarray(lon).reshape((ny, nx))\n", 33 | " lat = np.asarray(lat).reshape((ny, nx))\n", 34 | " da.coords['lon'] = (('y', 'x'), lon)\n", 35 | " da.coords['lat'] = (('y', 'x'), lat)\n", 36 | " da = da.drop_vars([\"x\", \"y\"])\n", 37 | " return da\n", 38 | "\n", 39 | "fn = \"../data/onwind-av-595.tif\"\n", 40 | "\n", 41 | "def convert_to_dataset(fn):\n", 42 | " da = xr.open_rasterio(fn)\n", 43 | " da = get_latlon(da).mean(dim='band')\n", 44 | " ds = da.where((da.values < 255) & (da.values > 0)).to_dataset(name='availability')\n", 45 | " #ds.to_netcdf(fn.replace(\".tif\", \".nc\")) # much memory\n", 46 | " return ds\n", 47 | "\n", 48 | "\n", 49 | "%memit\n", 50 | "ds = convert_to_dataset(fn)\n", 51 | "\n", 52 | "%memit\n", 53 | "ds.hvplot.contourf(\n", 54 | " 'lon',\n", 55 | " 'lat',\n", 56 | " 'availability',\n", 57 | " geo=True,\n", 58 | " tiles=\"CartoLight\",\n", 59 | " cmap='Greens',\n", 60 | " alpha=0.5,\n", 61 | " frame_height=800,\n", 62 | " colorbar=False,\n", 63 | " legend=False,\n", 64 | " hover=False,\n", 65 | " title=\"Available Land\"\n", 66 | ").opts(\n", 67 | " active_tools=['pan', 'wheel_zoom']\n", 68 | ")" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": null, 74 | "id": "1dc75d2f-2be1-4505-b581-7a1d02de1682", 75 | "metadata": {}, 76 | "outputs": [], 77 | "source": [] 78 | } 79 | ], 80 | "metadata": { 81 | "kernelspec": { 82 | "display_name": "", 83 | "language": "python", 84 | "name": "" 85 | }, 86 | "language_info": { 87 | "codemirror_mode": { 88 | "name": "ipython", 89 | "version": 3 90 | }, 91 | "file_extension": ".py", 92 | "mimetype": "text/x-python", 93 | "name": "python", 94 | "nbconvert_exporter": "python", 95 | "pygments_lexer": "ipython3", 96 | "version": "3.8.8" 97 | } 98 | }, 99 | "nbformat": 4, 100 | "nbformat_minor": 5 101 | } -------------------------------------------------------------------------------- /src/sankey.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "a813a8a4-ee0f-4daf-abcb-d0315b1309fd", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pypsa\n", 11 | "import pandas as pd\n", 12 | "from pypsa.descriptors import get_switchable_as_dense\n", 13 | "import plotly.graph_objects as go\n", 14 | "import numpy as np\n", 15 | "import yaml\n", 16 | "from matplotlib.colors import to_rgba\n", 17 | "pd.set_option('display.max_rows', 150)" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": null, 23 | "id": "5f98d87b-a1af-41bb-abcf-e91f02f3f459", 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "path = \"../../pr/\"" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": null, 33 | "id": "e8b246eb-25a5-4e11-b3e2-c663d566d16a", 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "with open(path + \"pypsa-eur-sec/config.yaml\") as file:\n", 38 | " config = yaml.safe_load(file)\n", 39 | "\n", 40 | "colors = config[\"plotting\"][\"tech_colors\"]\n", 41 | "\n", 42 | "colors[\"electricity grid\"] = \"teal\"\n", 43 | "colors[\"ground-sourced ambient\"] = \"orchid\"\n", 44 | "colors[\"air-sourced ambient\"] = \"thistle\"" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "id": "82d4255a-00f2-4226-8bd0-ce341395602e", 51 | "metadata": {}, 52 | "outputs": [], 53 | "source": [ 54 | "n = pypsa.Network(\n", 55 | " path + \"pypsa-eur-sec/results/your-run-name-overnight-dev/postnetworks/elec_s_60_lv1.25__Co2L0p0-365H-T-H-B-I-solar+p3-dist1_2030.nc\"\n", 56 | ")" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": null, 62 | "id": "6ca681e7-b23e-4546-9853-ab5354cd9f03", 63 | "metadata": {}, 64 | "outputs": [], 65 | "source": [ 66 | "columns = [\"label\", \"source\", \"target\", \"value\"]" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": null, 72 | "id": "386ae142-95fb-4e3a-a524-ed0335a2145d", 73 | "metadata": { 74 | "tags": [] 75 | }, 76 | "outputs": [], 77 | "source": [ 78 | "gen = (n.snapshot_weightings @ n.generators_t.p).groupby([\n", 79 | " n.generators.carrier, n.generators.carrier, n.generators.bus.map(n.buses.carrier)\n", 80 | "]).sum().div(1e6) # TWh\n", 81 | "\n", 82 | "gen.index.set_names(columns[:-1], inplace=True)\n", 83 | "gen = gen.reset_index(name='value')\n", 84 | "gen = gen.loc[gen.value>0.1]" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": null, 90 | "id": "68dec579-9201-49af-bb67-78de52ed5dfa", 91 | "metadata": { 92 | "tags": [] 93 | }, 94 | "outputs": [], 95 | "source": [ 96 | "gen[\"source\"] = gen[\"source\"].replace({\n", 97 | " \"gas\": \"fossil gas\",\n", 98 | " \"oil\": \"fossil oil\"\n", 99 | "})" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": null, 105 | "id": "9c9778aa-4553-4b25-b54a-b860f17f9cf7", 106 | "metadata": { 107 | "tags": [] 108 | }, 109 | "outputs": [], 110 | "source": [ 111 | "sto = (n.snapshot_weightings @ n.stores_t.p).groupby([\n", 112 | " n.stores.carrier, n.stores.carrier, n.stores.bus.map(n.buses.carrier)\n", 113 | "]).sum().div(1e6)\n", 114 | "sto.index.set_names(columns[:-1], inplace=True)\n", 115 | "sto = sto.reset_index(name='value')\n", 116 | "sto = sto.loc[sto.value>.1]" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": null, 122 | "id": "291df2b3-6047-481c-a662-0619e0688183", 123 | "metadata": { 124 | "tags": [] 125 | }, 126 | "outputs": [], 127 | "source": [ 128 | "su = (n.snapshot_weightings @ n.storage_units_t.p).groupby([\n", 129 | " n.storage_units.carrier, n.storage_units.carrier, n.storage_units.bus.map(n.buses.carrier)\n", 130 | "]).sum().div(1e6)\n", 131 | "su.index.set_names(columns[:-1], inplace=True)\n", 132 | "su = su.reset_index(name='value')\n", 133 | "su = su.loc[su.value>.1]" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": null, 139 | "id": "81b75732-57a6-4cb3-8fbb-89bef7f4d7be", 140 | "metadata": {}, 141 | "outputs": [], 142 | "source": [ 143 | "load = (n.snapshot_weightings @ get_switchable_as_dense(n, \"Load\", \"p_set\")).groupby([\n", 144 | " n.loads.carrier, n.loads.carrier, n.loads.bus.map(n.buses.carrier)\n", 145 | "]).sum().div(1e6).swaplevel() # TWh\n", 146 | "load.index.set_names(columns[:-1], inplace=True)\n", 147 | "load = load.reset_index(name='value')" 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": null, 153 | "id": "53b869dd-ce7a-44fc-bef2-f4f00a7f7ec0", 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [ 157 | "load = load.loc[~load.label.str.contains(\"emissions\")]\n", 158 | "load.target += \" demand\"" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": null, 164 | "id": "4321f89c-7134-4bf3-b5e3-de0ddc6285b7", 165 | "metadata": {}, 166 | "outputs": [], 167 | "source": [ 168 | "for i in range(5):\n", 169 | " n.links[f\"total_e{i}\"] = (n.snapshot_weightings @ n.links_t[f\"p{i}\"]).div(1e6) # TWh\n", 170 | " n.links[f\"carrier_bus{i}\"] = n.links[f\"bus{i}\"].map(n.buses.carrier)" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": null, 176 | "id": "a18986e8-4d4a-4da1-8169-0ea212992197", 177 | "metadata": {}, 178 | "outputs": [], 179 | "source": [ 180 | "def calculate_losses(x):\n", 181 | " energy_ports = x.loc[\n", 182 | " x.index.str.contains(\"carrier_bus\") &\n", 183 | " ~x.str.contains(\"co2\", na=False)\n", 184 | " ].index.str.replace(\"carrier_bus\", \"total_e\")\n", 185 | " return -x.loc[energy_ports].sum()\n", 186 | "\n", 187 | "n.links[\"total_e5\"] = n.links.apply(calculate_losses, axis=1)\n", 188 | "n.links[\"carrier_bus5\"] = \"losses\"" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": null, 194 | "id": "ecd0d6f3-3dcc-435b-ab69-647323856c9e", 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [ 198 | "df = pd.concat([\n", 199 | " n.links.groupby([\"carrier\", \"carrier_bus0\", \"carrier_bus\" + str(i)]).sum()[\"total_e\" + str(i)] for i in range(1,6)\n", 200 | "]).reset_index()\n", 201 | "df.columns = columns" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": null, 207 | "id": "48e26814-b2ba-42dc-9aec-ba8c254a5247", 208 | "metadata": {}, 209 | "outputs": [], 210 | "source": [ 211 | "# fix heat pump energy balance\n", 212 | "\n", 213 | "hp = n.links.loc[n.links.carrier.str.contains(\"heat pump\")]\n", 214 | "\n", 215 | "hp_t_elec = n.links_t.p0.filter(like=\"heat pump\")\n", 216 | "\n", 217 | "hp_elec = (-n.snapshot_weightings @ hp_t_elec).groupby([hp[\"carrier\"], hp[\"carrier_bus0\"], hp[\"carrier_bus1\"]]).sum().div(1e6).reset_index()\n", 218 | "hp_elec.columns = columns\n", 219 | "\n", 220 | "df = df.loc[~(df.label.str.contains(\"heat pump\") & (df.target == 'losses'))]\n", 221 | "\n", 222 | "df.loc[df.label.str.contains(\"heat pump\"), \"value\"] -= hp_elec[\"value\"].values\n", 223 | "\n", 224 | "df.loc[df.label.str.contains(\"air heat pump\"), \"source\"] = \"air-sourced ambient\"\n", 225 | "df.loc[df.label.str.contains(\"ground heat pump\"), \"source\"] = \"ground-sourced ambient\"\n", 226 | "\n", 227 | "df = pd.concat([df, hp_elec])" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": null, 233 | "id": "78a51a1e-eada-4ce3-b515-a960e4c6bd38", 234 | "metadata": {}, 235 | "outputs": [], 236 | "source": [ 237 | "df = df.set_index([\"label\", \"source\", \"target\"]).squeeze()" 238 | ] 239 | }, 240 | { 241 | "cell_type": "code", 242 | "execution_count": null, 243 | "id": "bf882f43-ba4d-4306-b9eb-209e2344e00d", 244 | "metadata": {}, 245 | "outputs": [], 246 | "source": [ 247 | "df = pd.concat([\n", 248 | " df.loc[df<0].mul(-1),\n", 249 | " df.loc[df>0].swaplevel(1, 2),\n", 250 | "]).reset_index()\n", 251 | "\n", 252 | "df.columns = columns" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": null, 258 | "id": "c8d6d312-802d-4e1a-8fa8-8af99d8d99fd", 259 | "metadata": {}, 260 | "outputs": [], 261 | "source": [ 262 | "# make DAC demand\n", 263 | "df.loc[df.label=='DAC', \"target\"] = \"DAC\"" 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": null, 269 | "id": "bedabe97-0011-4acd-9439-9c40b2578a1d", 270 | "metadata": {}, 271 | "outputs": [], 272 | "source": [ 273 | "connections = pd.concat([\n", 274 | " df,\n", 275 | " gen,\n", 276 | " su,\n", 277 | " sto,\n", 278 | " load,\n", 279 | "]).sort_index().reset_index(drop=True)" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": null, 285 | "id": "ab5bdfd1-bcc8-4665-81da-070df4ef3f94", 286 | "metadata": {}, 287 | "outputs": [], 288 | "source": [ 289 | "# aggregation\n", 290 | "\n", 291 | "src_contains = connections.source.str.contains\n", 292 | "trg_contains = connections.target.str.contains\n", 293 | "\n", 294 | "connections.loc[src_contains(\"low voltage\"), \"source\"] = \"AC\"\n", 295 | "connections.loc[trg_contains(\"low voltage\"), \"target\"] = \"AC\"\n", 296 | "connections.loc[src_contains(\"water tank\"), \"source\"] = \"water tank\"\n", 297 | "connections.loc[trg_contains(\"water tank\"), \"target\"] = \"water tank\"\n", 298 | "connections.loc[src_contains(\"solar thermal\"), \"source\"] = \"solar thermal\"\n", 299 | "connections.loc[src_contains(\"battery\"), \"source\"] = \"battery\"\n", 300 | "connections.loc[trg_contains(\"battery\"), \"target\"] = \"battery\"\n", 301 | "connections.loc[src_contains(\"Li ion\"), \"source\"] = \"battery\"\n", 302 | "connections.loc[trg_contains(\"Li ion\"), \"target\"] = \"battery\"\n", 303 | "\n", 304 | "connections.loc[src_contains(\"heat\") & ~src_contains(\"demand\"), \"source\"] = \"heat\"\n", 305 | "connections.loc[trg_contains(\"heat\") & ~trg_contains(\"demand\"), \"target\"] = \"heat\"" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": null, 311 | "id": "aeed8a23-f45f-4627-a324-2ef3c3ba253c", 312 | "metadata": {}, 313 | "outputs": [], 314 | "source": [ 315 | "connections = connections.loc[\n", 316 | " ~(connections.source == connections.target) \n", 317 | " & ~connections.source.str.contains(\"co2\")\n", 318 | " & ~connections.target.str.contains(\"co2\")\n", 319 | " & ~connections.source.str.contains(\"emissions\")\n", 320 | " & ~connections.source.isin(['gas for industry', \"solid biomass for industry\"])\n", 321 | " & (connections.value >= 0.5)\n", 322 | "]" 323 | ] 324 | }, 325 | { 326 | "cell_type": "code", 327 | "execution_count": null, 328 | "id": "02884f54-197e-478e-aac6-32877ef806bc", 329 | "metadata": {}, 330 | "outputs": [], 331 | "source": [ 332 | "where = connections.label=='urban central gas boiler'\n", 333 | "connections.loc[where] = connections.loc[where].replace(\"losses\", \"fossil gas\")" 334 | ] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": null, 339 | "id": "98d6e9fd-e796-4844-802b-87ca0deae74f", 340 | "metadata": {}, 341 | "outputs": [], 342 | "source": [ 343 | "connections.replace(\"AC\", \"electricity grid\", inplace=True)" 344 | ] 345 | }, 346 | { 347 | "cell_type": "code", 348 | "execution_count": null, 349 | "id": "cbc458ec-e312-4a5d-828c-5ecb48371a16", 350 | "metadata": {}, 351 | "outputs": [], 352 | "source": [ 353 | "labels = np.unique(connections[[\"source\", \"target\"]])" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": null, 359 | "id": "fedea992-d294-4ccf-8ae8-26a030364e82", 360 | "metadata": {}, 361 | "outputs": [], 362 | "source": [ 363 | "nodes = pd.Series({v: i for i, v in enumerate(labels)})" 364 | ] 365 | }, 366 | { 367 | "cell_type": "code", 368 | "execution_count": null, 369 | "id": "8c5a62a8-615a-4e99-9fcb-94b9c75314b9", 370 | "metadata": {}, 371 | "outputs": [], 372 | "source": [ 373 | "node_colors = pd.Series(nodes.index.map(colors).fillna(\"grey\"), index=nodes.index)" 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": null, 379 | "id": "f3c83a27-57c3-4aeb-b8dc-f1d1e74c0b80", 380 | "metadata": {}, 381 | "outputs": [], 382 | "source": [ 383 | "link_colors = [\"rgba{}\".format(to_rgba(node_colors[src], alpha=0.5)) for src in connections.source]" 384 | ] 385 | }, 386 | { 387 | "cell_type": "code", 388 | "execution_count": null, 389 | "id": "8f4b647a-9f8b-4ee1-87b5-8554e18dc157", 390 | "metadata": {}, 391 | "outputs": [], 392 | "source": [ 393 | "fig = go.Figure(go.Sankey(\n", 394 | " arrangement=\"snap\", # [snap, nodepad, perpendicular, fixed]\n", 395 | " valuesuffix = \"TWh\",\n", 396 | " valueformat = \".1f\",\n", 397 | " node = dict(\n", 398 | " pad=15,\n", 399 | " thickness=10,\n", 400 | " label=nodes.index,\n", 401 | " color=node_colors\n", 402 | " ),\n", 403 | " link = dict(\n", 404 | " source=connections.source.map(nodes),\n", 405 | " target=connections.target.map(nodes),\n", 406 | " value=connections.value,\n", 407 | " label=connections.label,\n", 408 | " color=link_colors,\n", 409 | " )\n", 410 | "))\n", 411 | "\n", 412 | "fig.update_layout(\n", 413 | " title=\"Sankey Diagram: PyPSA-Eur-Sec\",\n", 414 | " font_size=15\n", 415 | ")\n", 416 | "\n", 417 | "fig.write_html(\"Co2L0p0.html\")" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": null, 423 | "id": "d46b699e-9cd1-49a0-bde8-62ec44775d65", 424 | "metadata": {}, 425 | "outputs": [], 426 | "source": [] 427 | }, 428 | { 429 | "cell_type": "code", 430 | "execution_count": null, 431 | "id": "d71666a0-cb19-4e7f-b2b3-c7738b061aa4", 432 | "metadata": {}, 433 | "outputs": [], 434 | "source": [] 435 | }, 436 | { 437 | "cell_type": "code", 438 | "execution_count": null, 439 | "id": "9fb71d86-50c1-45e4-bcb9-a9d361283956", 440 | "metadata": {}, 441 | "outputs": [], 442 | "source": [] 443 | }, 444 | { 445 | "cell_type": "code", 446 | "execution_count": null, 447 | "id": "9c463429-fed7-4f76-8ebb-c637e295f389", 448 | "metadata": {}, 449 | "outputs": [], 450 | "source": [] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": null, 455 | "id": "41c5de09-0405-436b-bbb3-9414c87147f8", 456 | "metadata": {}, 457 | "outputs": [], 458 | "source": [] 459 | }, 460 | { 461 | "cell_type": "code", 462 | "execution_count": null, 463 | "id": "def746c4-2a0f-416b-81eb-46e455096f94", 464 | "metadata": {}, 465 | "outputs": [], 466 | "source": [] 467 | }, 468 | { 469 | "cell_type": "code", 470 | "execution_count": null, 471 | "id": "41c6859d-3393-40aa-a235-ebc05f635de4", 472 | "metadata": {}, 473 | "outputs": [], 474 | "source": [] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": null, 479 | "id": "e359d938-8c1a-41af-85f2-af3507fc03f7", 480 | "metadata": {}, 481 | "outputs": [], 482 | "source": [] 483 | } 484 | ], 485 | "metadata": { 486 | "kernelspec": { 487 | "display_name": "", 488 | "language": "python", 489 | "name": "" 490 | }, 491 | "language_info": { 492 | "codemirror_mode": { 493 | "name": "ipython", 494 | "version": 3 495 | }, 496 | "file_extension": ".py", 497 | "mimetype": "text/x-python", 498 | "name": "python", 499 | "nbconvert_exporter": "python", 500 | "pygments_lexer": "ipython3", 501 | "version": "3.8.8" 502 | } 503 | }, 504 | "nbformat": 4, 505 | "nbformat_minor": 5 506 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | Loss Approximation 635 | Copyright (C) 2020 FN 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Loss Approximation Copyright (C) 2020 FN 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /src/plots.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pypsa\n", 10 | "import atlite\n", 11 | "import yaml\n", 12 | "\n", 13 | "import pandas as pd\n", 14 | "import numpy as np\n", 15 | "import geopandas as gpd\n", 16 | "import xarray as xr\n", 17 | "import networkx as nx\n", 18 | "\n", 19 | "import panel as pn\n", 20 | "import panel.widgets as pnw\n", 21 | "import holoviews as hv\n", 22 | "\n", 23 | "import cartopy.crs as ccrs\n", 24 | "\n", 25 | "from rasterio.warp import transform\n", 26 | "\n", 27 | "import hvplot.pandas\n", 28 | "import hvplot.xarray\n", 29 | "import hvplot.networkx as hvnx" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": null, 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [ 38 | "from bokeh.models.formatters import DatetimeTickFormatter\n", 39 | "pn.extension()" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": null, 45 | "metadata": {}, 46 | "outputs": [], 47 | "source": [ 48 | "path = \"../../pr/\"\n", 49 | "clusters = 60" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "with open(path + \"pypsa-eur-sec/config.yaml\") as file:\n", 59 | " config = yaml.safe_load(file)\n", 60 | "\n", 61 | "colors = config[\"plotting\"][\"tech_colors\"]" 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": {}, 67 | "source": [ 68 | "## Solved Network" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": null, 74 | "metadata": {}, 75 | "outputs": [], 76 | "source": [ 77 | "n = pypsa.Network(\n", 78 | " path + f\"pypsa-eur-sec/results/your-run-name-overnight-dev/postnetworks/elec_s_{clusters}_lv1.25__Co2L0p0-365H-T-H-B-I-solar+p3-dist1_2030.nc\"\n", 79 | ")" 80 | ] 81 | }, 82 | { 83 | "cell_type": "markdown", 84 | "metadata": {}, 85 | "source": [ 86 | "## Geometry Polygon Data" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": null, 92 | "metadata": {}, 93 | "outputs": [], 94 | "source": [ 95 | "# shapes\n", 96 | "nodes = gpd.read_file(path + f\"pypsa-eur/resources/regions_onshore_elec_s_{clusters}.geojson\").set_index('name')\n", 97 | "cts = gpd.read_file(path + \"pypsa-eur/resources/country_shapes.geojson\").set_index('name')" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": null, 103 | "metadata": {}, 104 | "outputs": [], 105 | "source": [ 106 | "regions = gpd.read_file(path + \"pypsa-eur/resources/regions_onshore.geojson\").append(\n", 107 | " gpd.read_file(path + \"pypsa-eur/resources/regions_offshore.geojson\"))\n", 108 | "regions = regions.dissolve('name') \n", 109 | "onregions = gpd.read_file(path + \"pypsa-eur/resources/regions_onshore.geojson\").set_index('name')\n", 110 | "regions[\"Area\"] = regions.to_crs(epsg=3035).area.div(1e6)\n", 111 | "onregions[\"Area\"] = onregions.to_crs(epsg=3035).area.div(1e6)" 112 | ] 113 | }, 114 | { 115 | "cell_type": "markdown", 116 | "metadata": {}, 117 | "source": [ 118 | "## Model Inputs" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [ 127 | "# country-level data\n", 128 | "co2 = pd.read_csv(path + \"pypsa-eur-sec/resources/co2_totals.csv\", index_col=0)\n", 129 | "energy = pd.read_csv(path + \"pypsa-eur-sec/resources/energy_totals.csv\", index_col=0)\n", 130 | "transport = pd.read_csv(path + \"pypsa-eur-sec/resources/transport_data.csv\", index_col=0)\n", 131 | "biomass = pd.read_csv(path + \"pypsa-eur-sec/resources/biomass_potentials.csv\", index_col=0)" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": null, 137 | "metadata": {}, 138 | "outputs": [], 139 | "source": [ 140 | "# nodal-level data\n", 141 | "pop = pd.read_csv(path + f\"pypsa-eur-sec/resources/pop_layout_elec_s_{clusters}.csv\", index_col=0)\n", 142 | "idist = pd.read_csv(path + f\"pypsa-eur-sec/resources/industrial_distribution_key_elec_s_{clusters}.csv\", index_col=0)\n", 143 | "ienergy = pd.read_csv(path + f\"pypsa-eur-sec/resources/industrial_energy_demand_elec_s_{clusters}.csv\", index_col=0)\n", 144 | "iproduction = pd.read_csv(path + f\"pypsa-eur-sec/resources/industrial_production_elec_s_{clusters}.csv\", index_col=0)\n", 145 | "ienergy[\"total\"] = ienergy.sum(axis=1)\n", 146 | "iproduction[\"total\"] = iproduction.sum(axis=1)" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": null, 152 | "metadata": {}, 153 | "outputs": [], 154 | "source": [ 155 | "def cmap(select):\n", 156 | " if \"bio\" in select:\n", 157 | " return \"Greens\"\n", 158 | " elif \"solar\" in select:\n", 159 | " return \"Reds\"\n", 160 | " elif \"wind\" in select:\n", 161 | " return \"Blues\"\n", 162 | " elif \"LULUCF\" in select:\n", 163 | " return \"RdBu\"\n", 164 | " else:\n", 165 | " return \"YlGnBu\"" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "execution_count": null, 171 | "metadata": {}, 172 | "outputs": [], 173 | "source": [ 174 | "def plot_geo(gdf, df, options, clim=None, tiles=None, alpha=1., line_width=0.6, select=True):\n", 175 | "\n", 176 | " if select:\n", 177 | " selector = pnw.Select(options=options)\n", 178 | " else:\n", 179 | " selector = pnw.RadioBoxGroup(options=options)\n", 180 | "\n", 181 | " def _plot(select):\n", 182 | " return gdf.hvplot(\n", 183 | " geo=True,\n", 184 | " frame_height=700,\n", 185 | " c=df[select],\n", 186 | " tiles=tiles,\n", 187 | " alpha=alpha,\n", 188 | " line_width=line_width,\n", 189 | " cmap=cmap(select),\n", 190 | " clim=clim,\n", 191 | " hover_cols=['name']\n", 192 | " ).opts(\n", 193 | " active_tools=['pan', 'wheel_zoom']\n", 194 | " )\n", 195 | "\n", 196 | " plot = pn.bind(_plot, selector)\n", 197 | " widgets = pn.Column(selector, plot)\n", 198 | " return widgets" 199 | ] 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "metadata": {}, 204 | "source": [ 205 | "### country-level" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": null, 211 | "metadata": {}, 212 | "outputs": [], 213 | "source": [ 214 | "plt_co2 = plot_geo(cts, co2, list(co2.columns))" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": null, 220 | "metadata": {}, 221 | "outputs": [], 222 | "source": [ 223 | "plt_energy = plot_geo(cts, energy, list(energy.columns))" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": {}, 229 | "source": [ 230 | "## nodal level" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": null, 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "plt_production = plot_geo(nodes, iproduction, list(iproduction.columns))" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": null, 245 | "metadata": {}, 246 | "outputs": [], 247 | "source": [ 248 | "plt_population = plot_geo(nodes, pop, [\"total\", \"urban\", 'rural'])" 249 | ] 250 | }, 251 | { 252 | "cell_type": "markdown", 253 | "metadata": {}, 254 | "source": [ 255 | "## Powerplantmatching" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "execution_count": null, 261 | "metadata": {}, 262 | "outputs": [], 263 | "source": [ 264 | "plants = pd.read_csv(\"https://raw.githubusercontent.com/FRESNA/powerplantmatching/master/matched_data_red.csv\", index_col=0)\n", 265 | "plants = plants.loc[(plants.lat > 34) & (plants.lon < 72)]" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": null, 271 | "metadata": {}, 272 | "outputs": [], 273 | "source": [ 274 | "ppm_colors = {\n", 275 | " \"Hydro\": 'teal',\n", 276 | " \"Hard Coal\": 'black',\n", 277 | " \"Lignite\": 'grey',\n", 278 | " \"Natural Gas\": 'orange',\n", 279 | " \"Nuclear\": 'red',\n", 280 | " \"Oil\": 'brown',\n", 281 | " \"Bioenergy\": 'green',\n", 282 | " \"Wind\": '#235ebc',\n", 283 | " \"Geothermal\": 'purple',\n", 284 | " \"Solar\": '#f9d002',\n", 285 | " \"Waste\": \"magenta\",\n", 286 | " \"Other\": 'white',\n", 287 | "}" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": null, 293 | "metadata": {}, 294 | "outputs": [], 295 | "source": [ 296 | "plt_powerplants = plants.hvplot.points(\n", 297 | " 'lon',\n", 298 | " 'lat',\n", 299 | " geo=True,\n", 300 | " frame_height=750,\n", 301 | " c='Fueltype',\n", 302 | " cmap=ppm_colors,\n", 303 | " size=plants[\"Capacity\"] / 5,\n", 304 | " alpha=0.4,\n", 305 | " tiles='CartoLight',\n", 306 | " hover_cols=['Name', 'Fueltype', \"Technology\", 'YearCommissioned', \"Retrofit\", \"Capacity\"],\n", 307 | " xlim=(-12,32),\n", 308 | ").opts(\n", 309 | " active_tools=['pan', 'wheel_zoom']\n", 310 | ")" 311 | ] 312 | }, 313 | { 314 | "cell_type": "markdown", 315 | "metadata": {}, 316 | "source": [ 317 | "### Renewable Potentials Unclustered" 318 | ] 319 | }, 320 | { 321 | "cell_type": "code", 322 | "execution_count": null, 323 | "metadata": {}, 324 | "outputs": [], 325 | "source": [ 326 | "wind = pd.Series()\n", 327 | "for profile in ['onwind', 'offwind-ac', 'offwind-dc']:\n", 328 | " ds = xr.open_dataset(f'{path}/pypsa-eur/resources/profile_{profile}.nc')\n", 329 | " wind = wind.append((ds.p_nom_max * ds.profile.sum('time')).to_pandas())\n", 330 | "wind = wind.sum(level=0).reindex(regions.index, fill_value=0)\n", 331 | "wind_per_skm = pd.DataFrame({\"wind\": wind / regions.Area / 1e3}) # GWh" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": null, 337 | "metadata": {}, 338 | "outputs": [], 339 | "source": [ 340 | "ds = xr.open_dataset(f'{path}/pypsa-eur/resources/profile_solar.nc')\n", 341 | "solar = (ds.p_nom_max * ds.profile.sum('time')).to_pandas()\n", 342 | "\n", 343 | "solar = solar.sum(level=0).reindex(onregions.index, fill_value=0)\n", 344 | "solar_per_skm = pd.DataFrame({\"solar\": solar / onregions.Area / 1e3}) # GWh" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": null, 350 | "metadata": {}, 351 | "outputs": [], 352 | "source": [ 353 | "plt_wind_per_skm = plot_geo(regions, wind_per_skm, [\"wind\"], tiles='CartoLight', alpha=0.5, line_width=0.1)" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": null, 359 | "metadata": {}, 360 | "outputs": [], 361 | "source": [ 362 | "plt_solar_per_skm = plot_geo(onregions, solar_per_skm, [\"solar\"], tiles='CartoLight', alpha=0.7, line_width=0.1)" 363 | ] 364 | }, 365 | { 366 | "cell_type": "markdown", 367 | "metadata": {}, 368 | "source": [ 369 | "### Renewable Potentials Clustered" 370 | ] 371 | }, 372 | { 373 | "cell_type": "code", 374 | "execution_count": null, 375 | "metadata": {}, 376 | "outputs": [], 377 | "source": [ 378 | "cfs = n.generators_t.p_max_pu.groupby([n.generators.carrier, n.generators.bus.map(n.buses.location)], axis=1).mean().mean().unstack(0)" 379 | ] 380 | }, 381 | { 382 | "cell_type": "code", 383 | "execution_count": null, 384 | "metadata": {}, 385 | "outputs": [], 386 | "source": [ 387 | "plt_cfs = plot_geo(nodes, cfs, list(cfs.columns))" 388 | ] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "execution_count": null, 393 | "metadata": {}, 394 | "outputs": [], 395 | "source": [ 396 | "pot = n.generators.p_nom_max.groupby([n.generators.carrier, n.generators.bus.map(n.buses.location)]).sum().unstack(0)" 397 | ] 398 | }, 399 | { 400 | "cell_type": "code", 401 | "execution_count": null, 402 | "metadata": {}, 403 | "outputs": [], 404 | "source": [ 405 | "pot.drop(index=\"EU\", columns=['gas', 'oil', 'ror'], inplace=True)" 406 | ] 407 | }, 408 | { 409 | "cell_type": "code", 410 | "execution_count": null, 411 | "metadata": {}, 412 | "outputs": [], 413 | "source": [ 414 | "plt_pot = plot_geo(nodes, pot, list(pot.columns))" 415 | ] 416 | }, 417 | { 418 | "cell_type": "markdown", 419 | "metadata": {}, 420 | "source": [ 421 | "### Nodal Capacities and Costs" 422 | ] 423 | }, 424 | { 425 | "cell_type": "code", 426 | "execution_count": null, 427 | "metadata": {}, 428 | "outputs": [], 429 | "source": [ 430 | "term_p = \"p_nom_opt\"\n", 431 | "term_e = \"e_nom_opt\"" 432 | ] 433 | }, 434 | { 435 | "cell_type": "raw", 436 | "metadata": {}, 437 | "source": [ 438 | "term_e = \"capital_cost * (e_nom_opt-e_nom)\"\n", 439 | "term_p = \"capital_cost * (p_nom_opt-p_nom)\"" 440 | ] 441 | }, 442 | { 443 | "cell_type": "code", 444 | "execution_count": null, 445 | "metadata": {}, 446 | "outputs": [], 447 | "source": [ 448 | "gen = n.generators.eval(term_p).groupby([n.generators.carrier, n.generators.bus.map(n.buses.location)]).sum()\n", 449 | "sto = n.stores.eval(term_e).groupby([n.stores.carrier, n.stores.bus.map(n.buses.location)]).sum()\n", 450 | "local_links = n.links.loc[n.links.bus0.map(n.buses.location) == n.links.bus1.map(n.buses.location)]\n", 451 | "link = local_links.eval(term_p).groupby([local_links.carrier, local_links.bus0.map(n.buses.location)]).sum()\n", 452 | "su = n.storage_units.eval(term_p).groupby([n.storage_units.carrier, n.storage_units.bus]).sum()" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": null, 458 | "metadata": {}, 459 | "outputs": [], 460 | "source": [ 461 | "gen = gen.unstack().drop(\"EU\", axis=1).dropna(how='all')\n", 462 | "link = link.unstack().drop(\"EU\", axis=1).dropna(how='all')\n", 463 | "sto = sto.unstack().drop(\"EU\", axis=1).dropna(how='all')\n", 464 | "su = su.unstack()" 465 | ] 466 | }, 467 | { 468 | "cell_type": "code", 469 | "execution_count": null, 470 | "metadata": {}, 471 | "outputs": [], 472 | "source": [ 473 | "cap = pd.concat([gen, sto, link, su]).T.div(1e3)" 474 | ] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": null, 479 | "metadata": {}, 480 | "outputs": [], 481 | "source": [ 482 | "cap[cap <= 0.1] = 0." 483 | ] 484 | }, 485 | { 486 | "cell_type": "code", 487 | "execution_count": null, 488 | "metadata": {}, 489 | "outputs": [], 490 | "source": [ 491 | "plt_cap = plot_geo(nodes, cap, list(cap.columns))" 492 | ] 493 | }, 494 | { 495 | "cell_type": "markdown", 496 | "metadata": {}, 497 | "source": [ 498 | "## Base Networks" 499 | ] 500 | }, 501 | { 502 | "cell_type": "code", 503 | "execution_count": null, 504 | "metadata": {}, 505 | "outputs": [], 506 | "source": [ 507 | "base = pypsa.Network(path + \"pypsa-eur/networks/base.nc\")" 508 | ] 509 | }, 510 | { 511 | "cell_type": "code", 512 | "execution_count": null, 513 | "metadata": {}, 514 | "outputs": [], 515 | "source": [ 516 | "edge_ln_attrs = [\"s_nom\", \"s_nom_opt\", \"v_nom\", \"type\", \"s_nom_extendable\", \"capital_cost\", \"under_construction\", \"underground\"]\n", 517 | "edge_lk_attrs = [\"p_nom\", \"p_nom_opt\", \"type\", \"p_nom_extendable\", \"capital_cost\", \"under_construction\", \"underground\", \"underwater_fraction\", \"tags\"]" 518 | ] 519 | }, 520 | { 521 | "cell_type": "code", 522 | "execution_count": null, 523 | "metadata": {}, 524 | "outputs": [], 525 | "source": [ 526 | "G_lines = nx.from_pandas_edgelist(base.lines.loc[base.lines.v_nom==380], 'bus0', 'bus1', edge_attr=edge_ln_attrs)\n", 527 | "G_links = nx.from_pandas_edgelist(base.links.loc[base.links.carrier=='DC'], 'bus0', 'bus1', edge_attr=edge_lk_attrs)\n", 528 | "pos = base.buses.loc[base.buses.carrier=='AC', [\"x\", \"y\"]].apply(tuple, axis=1).to_dict()" 529 | ] 530 | }, 531 | { 532 | "cell_type": "code", 533 | "execution_count": null, 534 | "metadata": {}, 535 | "outputs": [], 536 | "source": [ 537 | "network_map = cts.hvplot(\n", 538 | " geo=True,\n", 539 | " alpha=0.,\n", 540 | " height=850,\n", 541 | " width=1400,\n", 542 | " tiles=\"CartoLight\",\n", 543 | ") * \\\n", 544 | "hvnx.draw(\n", 545 | " G_links,\n", 546 | " pos=pos,\n", 547 | " responsive=True,\n", 548 | " geo=True,\n", 549 | " node_size=0,\n", 550 | " edge_color='royalblue',\n", 551 | " inspection_policy=\"edges\",\n", 552 | " edge_width=2,\n", 553 | ") * \\\n", 554 | "hvnx.draw(\n", 555 | " G_lines,\n", 556 | " pos=pos,\n", 557 | " geo=True,\n", 558 | " node_size=0,\n", 559 | " edge_color='firebrick',\n", 560 | " node_color='black',\n", 561 | " inspection_policy=\"edges\",\n", 562 | " edge_width=2,\n", 563 | ").opts(\n", 564 | " active_tools=['pan', 'wheel_zoom']\n", 565 | ")" 566 | ] 567 | }, 568 | { 569 | "cell_type": "code", 570 | "execution_count": null, 571 | "metadata": {}, 572 | "outputs": [], 573 | "source": [ 574 | "if len(base.lines.v_nom.unique()) > 1:\n", 575 | " G_lines_300 = nx.from_pandas_edgelist(base.lines.loc[base.lines.v_nom==300], 'bus0', 'bus1', edge_attr=edge_ln_attrs)\n", 576 | " G_lines_220 = nx.from_pandas_edgelist(base.lines.loc[base.lines.v_nom==220], 'bus0', 'bus1', edge_attr=edge_ln_attrs)\n", 577 | " network_map *= \\\n", 578 | " hvnx.draw(\n", 579 | " G_lines_300,\n", 580 | " pos=pos,\n", 581 | " geo=True,\n", 582 | " node_size=0,\n", 583 | " edge_color='orange',\n", 584 | " edge_width=1.5,\n", 585 | " inspection_policy=\"edges\"\n", 586 | " ) * \\\n", 587 | " hvnx.draw(\n", 588 | " G_lines_220,\n", 589 | " pos=pos,\n", 590 | " geo=True,\n", 591 | " node_size=0,\n", 592 | " edge_width=1,\n", 593 | " edge_color='green',\n", 594 | " inspection_policy=\"edges\",\n", 595 | " )" 596 | ] 597 | }, 598 | { 599 | "cell_type": "markdown", 600 | "metadata": {}, 601 | "source": [ 602 | "## Networks" 603 | ] 604 | }, 605 | { 606 | "cell_type": "code", 607 | "execution_count": null, 608 | "metadata": {}, 609 | "outputs": [], 610 | "source": [ 611 | "G_lines = nx.from_pandas_edgelist(n.lines, 'bus0', 'bus1', edge_attr='s_nom_opt')" 612 | ] 613 | }, 614 | { 615 | "cell_type": "code", 616 | "execution_count": null, 617 | "metadata": {}, 618 | "outputs": [], 619 | "source": [ 620 | "G_links = nx.from_pandas_edgelist(n.links.loc[n.links.carrier=='DC'], 'bus0', 'bus1', edge_attr='p_nom_opt')" 621 | ] 622 | }, 623 | { 624 | "cell_type": "code", 625 | "execution_count": null, 626 | "metadata": {}, 627 | "outputs": [], 628 | "source": [ 629 | "H2 = n.links.loc[n.links.carrier=='H2 pipeline']\n", 630 | "H2[\"location0\"] = H2.bus0.apply(lambda x: x[:-3])\n", 631 | "H2[\"location1\"] = H2.bus1.apply(lambda x: x[:-3])\n", 632 | "G_H2 = nx.from_pandas_edgelist(H2, 'location0', 'location1', edge_attr='p_nom_opt')\n", 633 | "electrolysis = n.links.loc[n.links.carrier=='H2 Electrolysis'].groupby(\"bus0\").p_nom_opt.sum()\n", 634 | "nx.set_node_attributes(G_H2, electrolysis, \"electrolysis\")" 635 | ] 636 | }, 637 | { 638 | "cell_type": "code", 639 | "execution_count": null, 640 | "metadata": {}, 641 | "outputs": [], 642 | "source": [ 643 | "pos = n.buses.loc[n.buses.carrier=='AC', [\"x\", \"y\"]].apply(tuple, axis=1).to_dict()" 644 | ] 645 | }, 646 | { 647 | "cell_type": "code", 648 | "execution_count": null, 649 | "metadata": {}, 650 | "outputs": [], 651 | "source": [ 652 | "elec_net = nodes.hvplot(\n", 653 | " geo=True,\n", 654 | " color='whitesmoke',\n", 655 | " line_color='grey',\n", 656 | " line_width=0.5,\n", 657 | " #transform=ccrs.EuroPP(),\n", 658 | ") * \\\n", 659 | "hvnx.draw(\n", 660 | " G_links, \n", 661 | " pos=pos,\n", 662 | " width=1000,\n", 663 | " height=800,\n", 664 | " node_size=0,\n", 665 | " edge_color='navy',\n", 666 | " edge_width=hv.dim('p_nom_opt') / 3e3,\n", 667 | " geo=True,\n", 668 | " #crs=ccrs.EuroPP(),\n", 669 | " inspection_policy='edges'\n", 670 | ") * \\\n", 671 | "hvnx.draw(\n", 672 | " G_lines, \n", 673 | " pos=pos,\n", 674 | " width=1000,\n", 675 | " height=800,\n", 676 | " node_size=40,\n", 677 | " node_color='gray',\n", 678 | " edge_color='firebrick',\n", 679 | " edge_width=hv.dim('s_nom_opt') / 3e3,\n", 680 | " geo=True,\n", 681 | " #crs=ccrs.EuroPP(),\n", 682 | " inspection_policy='edges'\n", 683 | ").opts(\n", 684 | " active_tools=['pan', 'wheel_zoom']\n", 685 | ")" 686 | ] 687 | }, 688 | { 689 | "cell_type": "code", 690 | "execution_count": null, 691 | "metadata": {}, 692 | "outputs": [], 693 | "source": [ 694 | "h2_net = nodes.hvplot(\n", 695 | " geo=True,\n", 696 | " color='whitesmoke',\n", 697 | " line_color='grey',\n", 698 | " line_width=0.5,\n", 699 | ") * \\\n", 700 | "hvnx.draw(\n", 701 | " G_H2, \n", 702 | " pos=pos,\n", 703 | " width=1000,\n", 704 | " height=800,\n", 705 | " edge_color='cyan',\n", 706 | " edge_width=hv.dim('p_nom_opt') / 3e3,\n", 707 | " node_color='magenta',\n", 708 | " node_size=hv.dim(\"electrolysis\") / 2e2,\n", 709 | " geo=True,\n", 710 | " inspection_policy='edges'\n", 711 | ").opts(\n", 712 | " active_tools=['pan', 'wheel_zoom']\n", 713 | ")" 714 | ] 715 | }, 716 | { 717 | "cell_type": "markdown", 718 | "metadata": {}, 719 | "source": [ 720 | "## System time series" 721 | ] 722 | }, 723 | { 724 | "cell_type": "code", 725 | "execution_count": null, 726 | "metadata": {}, 727 | "outputs": [], 728 | "source": [ 729 | "# one resampled version, one hourly version" 730 | ] 731 | }, 732 | { 733 | "cell_type": "code", 734 | "execution_count": null, 735 | "metadata": {}, 736 | "outputs": [], 737 | "source": [ 738 | "load = n.loads_t.p_set.groupby(n.loads.carrier, axis=1).sum()\n", 739 | "formatter = DatetimeTickFormatter(months='%b')" 740 | ] 741 | }, 742 | { 743 | "cell_type": "code", 744 | "execution_count": null, 745 | "metadata": {}, 746 | "outputs": [], 747 | "source": [ 748 | "plt_load_ts = load.hvplot.area(width=1000, stacked=True, xformatter=formatter)" 749 | ] 750 | }, 751 | { 752 | "cell_type": "code", 753 | "execution_count": null, 754 | "metadata": {}, 755 | "outputs": [], 756 | "source": [ 757 | "selection = [\"offwind-ac\", \"offwind-dc\", \"onwind\", \"ror\", \"solar\"]\n", 758 | "cfs = n.generators_t.p_max_pu.groupby(n.generators.carrier, axis=1).mean()[selection]" 759 | ] 760 | }, 761 | { 762 | "cell_type": "code", 763 | "execution_count": null, 764 | "metadata": {}, 765 | "outputs": [], 766 | "source": [ 767 | "cfs.hvplot.line(width=1000, xformatter=formatter)" 768 | ] 769 | }, 770 | { 771 | "cell_type": "code", 772 | "execution_count": null, 773 | "metadata": {}, 774 | "outputs": [], 775 | "source": [ 776 | "gen = n.generators_t.p.groupby(n.generators.carrier, axis=1).sum()" 777 | ] 778 | }, 779 | { 780 | "cell_type": "code", 781 | "execution_count": null, 782 | "metadata": {}, 783 | "outputs": [], 784 | "source": [ 785 | "su = n.storage_units_t.p.groupby(n.storage_units.carrier, axis=1).sum()" 786 | ] 787 | }, 788 | { 789 | "cell_type": "code", 790 | "execution_count": null, 791 | "metadata": {}, 792 | "outputs": [], 793 | "source": [ 794 | "df = pd.concat([gen, su], axis=1)" 795 | ] 796 | }, 797 | { 798 | "cell_type": "code", 799 | "execution_count": null, 800 | "metadata": {}, 801 | "outputs": [], 802 | "source": [ 803 | "plt_gen_ts = df.hvplot.area(width=1000, height=500, line_width=0, title=\"electricity generation\")" 804 | ] 805 | }, 806 | { 807 | "cell_type": "markdown", 808 | "metadata": {}, 809 | "source": [ 810 | "# Cutouts" 811 | ] 812 | }, 813 | { 814 | "cell_type": "code", 815 | "execution_count": null, 816 | "metadata": {}, 817 | "outputs": [], 818 | "source": [ 819 | "era5 = atlite.Cutout(path + \"pypsa-eur/cutouts/europe-2013-era5.nc\")\n", 820 | "sarah = atlite.Cutout(path + \"pypsa-eur/cutouts/europe-2013-sarah.nc\")" 821 | ] 822 | }, 823 | { 824 | "cell_type": "code", 825 | "execution_count": null, 826 | "metadata": {}, 827 | "outputs": [], 828 | "source": [ 829 | "def plot_cutout(cutout, variable):\n", 830 | "\n", 831 | " return cutout.data.hvplot.quadmesh(\n", 832 | " 'x', 'y', variable,\n", 833 | " frame_height=700,\n", 834 | " cmap='Reds',\n", 835 | " coastline=True,\n", 836 | " project=True,\n", 837 | " geo=True,\n", 838 | " rasterize=True,\n", 839 | " ylim=(34,72),\n", 840 | " xlim=(-12,34),\n", 841 | " #clim=(0,1200),\n", 842 | " widget_location='top',\n", 843 | " #widgets={'time': pnw.DatetimeInput(value=dt.datetime(2013, 2, 8, 0, 0))}\n", 844 | " #tiles='CartoLight'\n", 845 | " #datashade=True, # removes legend\n", 846 | " )" 847 | ] 848 | }, 849 | { 850 | "cell_type": "code", 851 | "execution_count": null, 852 | "metadata": {}, 853 | "outputs": [], 854 | "source": [ 855 | "plt_cutout_wind = plot_cutout(era5, \"wnd100m\")" 856 | ] 857 | }, 858 | { 859 | "cell_type": "code", 860 | "execution_count": null, 861 | "metadata": {}, 862 | "outputs": [], 863 | "source": [ 864 | "plt_cutout_solar = plot_cutout(sarah, \"influx_direct\")" 865 | ] 866 | }, 867 | { 868 | "cell_type": "markdown", 869 | "metadata": {}, 870 | "source": [ 871 | "## Outputs" 872 | ] 873 | }, 874 | { 875 | "cell_type": "code", 876 | "execution_count": null, 877 | "metadata": {}, 878 | "outputs": [], 879 | "source": [ 880 | "import sys, os\n", 881 | "sys.path.insert(0, os.getcwd() + \"/\" + path + \"pypsa-eur-sec/scripts\")\n", 882 | "from plot_summary import rename_techs, preferred_order" 883 | ] 884 | }, 885 | { 886 | "cell_type": "code", 887 | "execution_count": null, 888 | "metadata": {}, 889 | "outputs": [], 890 | "source": [ 891 | "cost_df = pd.read_csv(\n", 892 | " path + \"pypsa-eur-sec/results/your-run-name-overnight-dev/csvs/costs.csv\",\n", 893 | " index_col=list(range(3)),\n", 894 | " header=list(range(4))\n", 895 | ")\n", 896 | "df = cost_df.groupby(cost_df.index.get_level_values(2)).sum()\n", 897 | "df = df / 1e9\n", 898 | "df = df.groupby(df.index.map(rename_techs)).sum()\n", 899 | "\n", 900 | "to_drop = df.index[df.max(axis=1) < 1.]\n", 901 | " \n", 902 | "new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))\n", 903 | "new_columns = df.sum().sort_values().index" 904 | ] 905 | }, 906 | { 907 | "cell_type": "code", 908 | "execution_count": null, 909 | "metadata": {}, 910 | "outputs": [], 911 | "source": [ 912 | "df.columns = [', '.join(col).strip() for col in df.columns.values]" 913 | ] 914 | }, 915 | { 916 | "cell_type": "code", 917 | "execution_count": null, 918 | "metadata": {}, 919 | "outputs": [], 920 | "source": [ 921 | "plt_scen_costs = df.T.hvplot.bar(\n", 922 | " stacked=True,\n", 923 | " rot=65,\n", 924 | " frame_width=800,\n", 925 | " frame_height=600,\n", 926 | " ylim=(0,1000),\n", 927 | " color='Category', cmap=colors)" 928 | ] 929 | }, 930 | { 931 | "cell_type": "code", 932 | "execution_count": null, 933 | "metadata": {}, 934 | "outputs": [], 935 | "source": [ 936 | "energy_df = pd.read_csv(\n", 937 | " path + \"pypsa-eur-sec/results/your-run-name-overnight-dev/csvs/energy.csv\",\n", 938 | " index_col=list(range(2)),\n", 939 | " header=list(range(4))\n", 940 | ")\n", 941 | "df = energy_df.groupby(energy_df.index.get_level_values(1)).sum()\n", 942 | "df = df / 1e6\n", 943 | "df = df.groupby(df.index.map(rename_techs)).sum()\n", 944 | "to_drop = df.index[df.abs().max(axis=1) < 50]\n", 945 | "df = df.drop(to_drop)\n", 946 | "new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))\n", 947 | "new_columns = df.columns.sort_values()" 948 | ] 949 | }, 950 | { 951 | "cell_type": "code", 952 | "execution_count": null, 953 | "metadata": {}, 954 | "outputs": [], 955 | "source": [ 956 | "df.columns = [', '.join(col).strip() for col in df.columns.values]" 957 | ] 958 | }, 959 | { 960 | "cell_type": "code", 961 | "execution_count": null, 962 | "metadata": {}, 963 | "outputs": [], 964 | "source": [ 965 | "plt_scen_energy = df.T.hvplot.bar(stacked=True, rot=65, frame_width=800, frame_height=600, ylim=(-20000,20000), color='Category', cmap=colors)" 966 | ] 967 | }, 968 | { 969 | "cell_type": "markdown", 970 | "metadata": {}, 971 | "source": [ 972 | "## Sankey\n", 973 | "as https://holoviews.org/gallery/demos/bokeh/energy_sankey.html" 974 | ] 975 | }, 976 | { 977 | "cell_type": "code", 978 | "execution_count": null, 979 | "metadata": {}, 980 | "outputs": [], 981 | "source": [ 982 | "edges = pd.read_csv('../data/connection.csv')\n", 983 | "sankey = hv.Sankey(edges, label='Energy Diagram')\n", 984 | "plt_sankes = sankey.opts(label_position='left', edge_color='target', node_color='index', cmap=colors)" 985 | ] 986 | }, 987 | { 988 | "cell_type": "markdown", 989 | "metadata": {}, 990 | "source": [ 991 | "## Industry Sector Ratios" 992 | ] 993 | }, 994 | { 995 | "cell_type": "code", 996 | "execution_count": null, 997 | "metadata": {}, 998 | "outputs": [], 999 | "source": [ 1000 | "iratios = pd.read_csv(path + \"pypsa-eur-sec/resources/industry_sector_ratios.csv\", index_col=0)" 1001 | ] 1002 | }, 1003 | { 1004 | "cell_type": "code", 1005 | "execution_count": null, 1006 | "metadata": {}, 1007 | "outputs": [], 1008 | "source": [ 1009 | "plt_iratios = iratios.T.hvplot.barh(stacked=True, width=1000, height=400, title=\"Industry Sector Ratios [MWh/t material]\")" 1010 | ] 1011 | }, 1012 | { 1013 | "cell_type": "markdown", 1014 | "metadata": {}, 1015 | "source": [ 1016 | "## Land Availability" 1017 | ] 1018 | }, 1019 | { 1020 | "cell_type": "code", 1021 | "execution_count": null, 1022 | "metadata": {}, 1023 | "outputs": [], 1024 | "source": [ 1025 | "def get_latlon(da):\n", 1026 | " \n", 1027 | " # Compute the lon/lat coordinates with rasterio.warp.transform\n", 1028 | " ny, nx = len(da['y']), len(da['x'])\n", 1029 | " x, y = np.meshgrid(da['x'], da['y'])\n", 1030 | "\n", 1031 | " # Rasterio works with 1D arrays\n", 1032 | " lon, lat = transform(da.crs, {'init': 'EPSG:4326'},\n", 1033 | " x.flatten(), y.flatten())\n", 1034 | " lon = np.asarray(lon).reshape((ny, nx))\n", 1035 | " lat = np.asarray(lat).reshape((ny, nx))\n", 1036 | " da.coords['lon'] = (('y', 'x'), lon)\n", 1037 | " da.coords['lat'] = (('y', 'x'), lat)\n", 1038 | " da = da.drop_vars([\"x\", \"y\"])\n", 1039 | " return da" 1040 | ] 1041 | }, 1042 | { 1043 | "cell_type": "code", 1044 | "execution_count": null, 1045 | "metadata": {}, 1046 | "outputs": [], 1047 | "source": [ 1048 | "def convert_to_dataset(fn):\n", 1049 | " da = xr.open_rasterio(fn)\n", 1050 | " da = get_latlon(da).mean(dim='band')\n", 1051 | " ds = da.where((da.values < 255) & (da.values > 0)).to_dataset(name='availability')\n", 1052 | " #ds.to_netcdf(fn.replace(\".tif\", \".nc\")) # much memory\n", 1053 | " return ds" 1054 | ] 1055 | }, 1056 | { 1057 | "cell_type": "code", 1058 | "execution_count": null, 1059 | "metadata": {}, 1060 | "outputs": [], 1061 | "source": [ 1062 | "fn = \"../data/onwind-av-595.tif\"\n", 1063 | "ds = convert_to_dataset(fn)" 1064 | ] 1065 | }, 1066 | { 1067 | "cell_type": "code", 1068 | "execution_count": null, 1069 | "metadata": {}, 1070 | "outputs": [], 1071 | "source": [ 1072 | "av = ds.hvplot.contourf(\n", 1073 | " 'lon',\n", 1074 | " 'lat',\n", 1075 | " 'availability',\n", 1076 | " geo=True,\n", 1077 | " tiles=\"CartoLight\",\n", 1078 | " cmap='Greens',\n", 1079 | " alpha=0.5,\n", 1080 | " frame_height=800,\n", 1081 | " colorbar=False,\n", 1082 | " legend=False,\n", 1083 | " hover=False,\n", 1084 | " title=\"Available Land\"\n", 1085 | ").opts(\n", 1086 | " active_tools=['pan', 'wheel_zoom']\n", 1087 | ")" 1088 | ] 1089 | }, 1090 | { 1091 | "cell_type": "markdown", 1092 | "metadata": {}, 1093 | "source": [ 1094 | "## Hotmaps Raw" 1095 | ] 1096 | }, 1097 | { 1098 | "cell_type": "code", 1099 | "execution_count": null, 1100 | "metadata": {}, 1101 | "outputs": [], 1102 | "source": [ 1103 | "def prepare_hotmaps_database(regions):\n", 1104 | " \"\"\"\n", 1105 | " Load hotmaps database of industrial sites and map onto bus regions.\n", 1106 | " \"\"\"\n", 1107 | "\n", 1108 | " df = pd.read_csv(path + \"pypsa-eur-sec/data/Industrial_Database.csv\", sep=\";\", index_col=0)\n", 1109 | "\n", 1110 | " df[[\"srid\", \"coordinates\"]] = df.geom.str.split(';', expand=True)\n", 1111 | "\n", 1112 | " # remove those sites without valid locations\n", 1113 | " df.drop(df.index[df.coordinates.isna()], inplace=True)\n", 1114 | "\n", 1115 | " df['coordinates'] = gpd.GeoSeries.from_wkt(df['coordinates'])\n", 1116 | "\n", 1117 | " gdf = gpd.GeoDataFrame(df, geometry='coordinates', crs=\"EPSG:4326\")\n", 1118 | "\n", 1119 | " gdf = gpd.sjoin(gdf, regions, how=\"inner\", op='within')\n", 1120 | "\n", 1121 | " gdf.rename(columns={\"index_right\": \"bus\"}, inplace=True)\n", 1122 | " gdf[\"country\"] = gdf.bus.str[:2]\n", 1123 | "\n", 1124 | " return gdf" 1125 | ] 1126 | }, 1127 | { 1128 | "cell_type": "code", 1129 | "execution_count": null, 1130 | "metadata": {}, 1131 | "outputs": [], 1132 | "source": [ 1133 | "hotmaps = prepare_hotmaps_database(nodes)\n", 1134 | "\n", 1135 | "hotmaps[\"geometry\"] = hotmaps.coordinates\n", 1136 | "hotmaps[\"lat\"] = hotmaps.geometry.y\n", 1137 | "hotmaps[\"lon\"] = hotmaps.geometry.x" 1138 | ] 1139 | }, 1140 | { 1141 | "cell_type": "code", 1142 | "execution_count": null, 1143 | "metadata": {}, 1144 | "outputs": [], 1145 | "source": [ 1146 | "plt_hotmaps = hotmaps.hvplot.points(\n", 1147 | " 'lon',\n", 1148 | " 'lat',\n", 1149 | " geo=True,\n", 1150 | " frame_height=750,\n", 1151 | " c='Subsector',\n", 1152 | " size=hotmaps[\"Emissions_ETS_2014\"] / 2e3,\n", 1153 | " alpha=0.4,\n", 1154 | " tiles='CartoLight',\n", 1155 | " hover_cols=['SiteName', \"Emissions_ETS_2014\", \"DataSource\"],\n", 1156 | ").opts(\n", 1157 | " active_tools=['pan', 'wheel_zoom']\n", 1158 | ")" 1159 | ] 1160 | }, 1161 | { 1162 | "cell_type": "code", 1163 | "execution_count": null, 1164 | "metadata": {}, 1165 | "outputs": [], 1166 | "source": [] 1167 | }, 1168 | { 1169 | "cell_type": "code", 1170 | "execution_count": null, 1171 | "metadata": {}, 1172 | "outputs": [], 1173 | "source": [ 1174 | "w0 = pnw.Select(name=\"Run:\", options=[\"your-run-name\"])\n", 1175 | "w1 = pnw.Select(name='Nodes:', options=[60])\n", 1176 | "w2 = pnw.Select(name='Transmission Expansion', options=[1.25])\n", 1177 | "w3 = pnw.Select(name=\"Opts:\", options=[\"Co2L0p0-365H-T-H-B-I-solar+p3-dist1\"])\n", 1178 | "w4 = pnw.Select(name=\"Year:\", options=[2030])\n", 1179 | "\n", 1180 | "box = pn.WidgetBox('### Scenario', w0, w1, w2, w3, w4)" 1181 | ] 1182 | }, 1183 | { 1184 | "cell_type": "code", 1185 | "execution_count": null, 1186 | "metadata": {}, 1187 | "outputs": [], 1188 | "source": [ 1189 | "import panel as pn\n", 1190 | "import numpy as np\n", 1191 | "import holoviews as hv\n", 1192 | "\n", 1193 | "pn.extension()\n", 1194 | "\n", 1195 | "board = pn.template.BootstrapTemplate(title='PyPSA-Eur-Sec Dashboard', header_background=\"#d95568\")\n", 1196 | "\n", 1197 | "pn.config.sizing_mode = 'stretch_width'\n", 1198 | "\n", 1199 | "intro = pn.pane.Markdown('''\n", 1200 | "\n", 1201 | "PyPSA-Eur-Sec is an open model dataset of the European energy system\n", 1202 | "at the transmission network level that covers the full ENTSO-E area.\n", 1203 | "\n", 1204 | "[pypsa-eur-sec.readthedocs.io](https://pypsa-eur-sec.readthedocs.io/)\n", 1205 | "''')\n", 1206 | "\n", 1207 | "_network_map = pn.Row(\n", 1208 | " network_map,\n", 1209 | " align='center'\n", 1210 | ")\n", 1211 | "\n", 1212 | "_existing = pn.Row(\n", 1213 | " pn.Card(plt_powerplants, collapsible=False, title=\"Powerplants\"),\n", 1214 | " pn.Card(plt_hotmaps, collapsible=False, title=\"Industrial Sites\"),\n", 1215 | ")\n", 1216 | "\n", 1217 | "_potentials = pn.Row(\n", 1218 | " pn.Card(plt_wind_per_skm, collapsible=False, title=\"Wind Potential\"),\n", 1219 | " pn.Card(plt_solar_per_skm, collapsible=False, title=\"Solar Potential\"),\n", 1220 | ")\n", 1221 | "\n", 1222 | "_cutouts = pn.Row(\n", 1223 | " pn.Card(plt_cutout_wind, collapsible=False, title=\"Wind Speeds\"),\n", 1224 | " pn.Card(plt_cutout_solar, collapsible=False, title=\"Direct Influx\")\n", 1225 | ")\n", 1226 | "\n", 1227 | "_clustered_potentials = pn.Row(\n", 1228 | " pn.Card(plt_cfs, collapsible=False, title=\"Capacity Factors\"),\n", 1229 | " pn.Card(plt_pot, collapsible=False, title=\"Potential\")\n", 1230 | ")\n", 1231 | "\n", 1232 | "_totals = pn.Row(\n", 1233 | " pn.Card(plt_energy, collapsible=False, title=\"Energy Consumption\"),\n", 1234 | " pn.Card(plt_co2, collapsible=False, title=\"Carbon Emissions\")\n", 1235 | ")\n", 1236 | "\n", 1237 | "_opt_nets = pn.Row(\n", 1238 | " pn.Card(elec_net, collapsible=False, title=\"Electricty Network\"),\n", 1239 | " pn.Card(h2_net, collapsible=False, title=\"Hydrogen Network\")\n", 1240 | ")\n", 1241 | "\n", 1242 | "_timeseries = pn.Column(\n", 1243 | " pn.Card(plt_load_ts, collapsible=False, title=\"Load\"),\n", 1244 | " pn.Card(plt_gen_ts, collapsible=False, title=\"Electricity Generation\")\n", 1245 | ")\n", 1246 | "\n", 1247 | "_sankey = pn.Row(sankey, align=('center', 'center'))\n", 1248 | "\n", 1249 | "_config = pn.pane.JSON(\n", 1250 | " config,\n", 1251 | " sizing_mode='stretch_both',\n", 1252 | " theme='light',\n", 1253 | " hover_preview=True\n", 1254 | ")\n", 1255 | "\n", 1256 | "board.sidebar.append(intro)\n", 1257 | "board.sidebar.append(box)\n", 1258 | "\n", 1259 | "board.main.append(pn.pane.Markdown(\"\"\" \"\"\"))\n", 1260 | "\n", 1261 | "board.main.append(\n", 1262 | " pn.Tabs(\n", 1263 | " (\"Base Network\", _network_map),\n", 1264 | " (\"Infrastructure\", _existing),\n", 1265 | " (\"VRES Potentials\", _potentials),\n", 1266 | " (\"VRES Potentials 2\", _clustered_potentials),\n", 1267 | " (\"Land Availability\", av),\n", 1268 | " (\"Cutouts\", _cutouts),\n", 1269 | " (\"Timeseries\", _timeseries),\n", 1270 | " (\"Energy and Carbon\", _totals),\n", 1271 | " (\"Industry Sectors\", plt_iratios),\n", 1272 | " (\"System Energy\", plt_scen_energy),\n", 1273 | " (\"System Costs\", plt_scen_costs),\n", 1274 | " (\"Networks\", _opt_nets),\n", 1275 | " (\"Expansion Plan\", plt_cap),\n", 1276 | " (\"Sankey\", _sankey),\n", 1277 | " (\"Config\", _config),\n", 1278 | " dynamic=True\n", 1279 | " )\n", 1280 | ")" 1281 | ] 1282 | }, 1283 | { 1284 | "cell_type": "code", 1285 | "execution_count": null, 1286 | "metadata": {}, 1287 | "outputs": [], 1288 | "source": [ 1289 | "board.show() # for development\n", 1290 | "#board.servable() # for deployment" 1291 | ] 1292 | }, 1293 | { 1294 | "cell_type": "code", 1295 | "execution_count": null, 1296 | "metadata": {}, 1297 | "outputs": [], 1298 | "source": [] 1299 | }, 1300 | { 1301 | "cell_type": "code", 1302 | "execution_count": null, 1303 | "metadata": {}, 1304 | "outputs": [], 1305 | "source": [] 1306 | }, 1307 | { 1308 | "cell_type": "code", 1309 | "execution_count": null, 1310 | "metadata": {}, 1311 | "outputs": [], 1312 | "source": [] 1313 | } 1314 | ], 1315 | "metadata": { 1316 | "kernelspec": { 1317 | "display_name": "", 1318 | "language": "python", 1319 | "name": "" 1320 | }, 1321 | "language_info": { 1322 | "codemirror_mode": { 1323 | "name": "ipython", 1324 | "version": 3 1325 | }, 1326 | "file_extension": ".py", 1327 | "mimetype": "text/x-python", 1328 | "name": "python", 1329 | "nbconvert_exporter": "python", 1330 | "pygments_lexer": "ipython3", 1331 | "version": "3.8.8" 1332 | } 1333 | }, 1334 | "nbformat": 4, 1335 | "nbformat_minor": 4 1336 | } --------------------------------------------------------------------------------