bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

[pre-commit.ci] auto fixes from pre-commit.com hooks

Browse Source 
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot] 2023-05-01 20:42:24 +00:00
parent 325ec50e11
commit 552f9b8bd3
1 changed files with 287 additions and 140 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

317
notebooks/pypsa_data.ipynb
View File

@ -12,10 +12,12 @@
"import os\n", "import os\n",
"from pathlib import Path\n", "from pathlib import Path\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n",
"plt.style.use(\"ggplot\")\n", "plt.style.use(\"ggplot\")\n",
"import pycountry\n", "import pycountry\n",
"import json\n", "import json\n",
"import warnings\n", "import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")" "warnings.filterwarnings(\"ignore\")"
] ]
}, },
@ -82,13 +84,11 @@
"gen = set([col[6:] for col in n.generators_t.p.columns])\n", "gen = set([col[6:] for col in n.generators_t.p.columns])\n",
"\n", "\n",
"for i, country in enumerate(countries):\n", "for i, country in enumerate(countries):\n",
"\n",
" df = pd.DataFrame(index=n.generators_t.p.index)\n", " df = pd.DataFrame(index=n.generators_t.p.index)\n",
" # country_generation = [col for col in n.generators_t.p.columns if col.startswith(country)]\n", " # country_generation = [col for col in n.generators_t.p.columns if col.startswith(country)]\n",
" country_generation = n.generators.loc[n.generators.bus.str.contains(country)]\n", " country_generation = n.generators.loc[n.generators.bus.str.contains(country)]\n",
"\n", "\n",
" for key, gens in pypsa_generation_mapper.items():\n", " for key, gens in pypsa_generation_mapper.items():\n",
"\n",
" # curr_gen = country_generation.loc[\n", " # curr_gen = country_generation.loc[\n",
" # (country_generation.carrier.str.contains(tech) for tech in gens).astype(bool)].index\n", " # (country_generation.carrier.str.contains(tech) for tech in gens).astype(bool)].index\n",
" curr_gen = country_generation.loc[\n", " curr_gen = country_generation.loc[\n",
@ -100,8 +100,7 @@
" else:\n", " else:\n",
" df[key] = np.zeros(len(df))\n", " df[key] = np.zeros(len(df))\n",
"\n", "\n",
" df.to_csv(data_path / \"pypsa_data\" / (country+\".csv\"))\n", " df.to_csv(data_path / \"pypsa_data\" / (country + \".csv\"))"
" "
] ]
}, },
{ {
@ -115,11 +114,12 @@
"\n", "\n",
"\n", "\n",
"for num, country in enumerate(os.listdir(data_path / \"pypsa_data\")):\n", "for num, country in enumerate(os.listdir(data_path / \"pypsa_data\")):\n",
"\n",
" # country = \"GR.csv\"\n", " # country = \"GR.csv\"\n",
" cc = country[:2]\n", " cc = country[:2]\n",
"\n", "\n",
" country_buses = np.unique(n.generators.loc[n.generators.bus.str.contains(cc)].bus.values)\n", " country_buses = np.unique(\n",
" n.generators.loc[n.generators.bus.str.contains(cc)].bus.values\n",
" )\n",
" print(f\"Buses for country {country[:-4]}: \", country_buses)\n", " print(f\"Buses for country {country[:-4]}: \", country_buses)\n",
"\n", "\n",
" if not len(country_buses) == 1:\n", " if not len(country_buses) == 1:\n",
@ -133,9 +133,11 @@
" pypsa_df = pd.read_csv(data_path / \"pypsa_data\" / country, parse_dates=True, index_col=0)\n", " pypsa_df = pd.read_csv(data_path / \"pypsa_data\" / country, parse_dates=True, index_col=0)\n",
" \"\"\"\n", " \"\"\"\n",
" try:\n", " try:\n",
" entsoe_df = pd.read_csv(data_path / \"harmonised_generation_data\" / (\"prepared_\"+country),\n", " entsoe_df = pd.read_csv(\n",
" data_path / \"harmonised_generation_data\" / (\"prepared_\" + country),\n",
" parse_dates=True,\n", " parse_dates=True,\n",
" index_col=0)\n", " index_col=0,\n",
" )\n",
"\n", "\n",
" entsoe_df.columns = [col[:-6] for col in entsoe_df.columns]\n", " entsoe_df.columns = [col[:-6] for col in entsoe_df.columns]\n",
" entsoe_df = entsoe_df.iloc[1:]\n", " entsoe_df = entsoe_df.iloc[1:]\n",
@ -160,7 +162,9 @@
" country_gen = n.generators.loc[n.generators.bus == bus]\n", " country_gen = n.generators.loc[n.generators.bus == bus]\n",
"\n", "\n",
" for tech, pypsa_carrier in pypsa_generation_mapper.items():\n", " for tech, pypsa_carrier in pypsa_generation_mapper.items():\n",
" gens = country_gen.loc[country_gen.carrier.apply(lambda c: c in pypsa_carrier)].index\n", " gens = country_gen.loc[\n",
" country_gen.carrier.apply(lambda c: c in pypsa_carrier)\n",
" ].index\n",
" energy_inflow[tech] = n.generators_t.p[gens].sum(axis=1)\n", " energy_inflow[tech] = n.generators_t.p[gens].sum(axis=1)\n",
"\n", "\n",
" # add inflows from lines\n", " # add inflows from lines\n",
@ -183,10 +187,16 @@
"\n", "\n",
" links_flow = np.zeros(energy_inflow.shape[0])\n", " links_flow = np.zeros(energy_inflow.shape[0])\n",
" if not links0.empty:\n", " if not links0.empty:\n",
" links_flow = - n.links_t.p0[links0].multiply(n.links.loc[links0, \"efficiency\"]).sum(axis=1)\n", " links_flow = (\n",
" -n.links_t.p0[links0]\n",
" .multiply(n.links.loc[links0, \"efficiency\"])\n",
" .sum(axis=1)\n",
" )\n",
"\n", "\n",
" if not links1.empty:\n", " if not links1.empty:\n",
" links_flow -= n.links_t.p1[links1].multiply(n.links.loc[links1, \"efficiency\"]).sum(axis=1)\n", " links_flow -= (\n",
" n.links_t.p1[links1].multiply(n.links.loc[links1, \"efficiency\"]).sum(axis=1)\n",
" )\n",
"\n", "\n",
" energy_inflow[\"Inflow Links\"] = np.maximum(np.zeros_like(links_flow), links_flow)\n", " energy_inflow[\"Inflow Links\"] = np.maximum(np.zeros_like(links_flow), links_flow)\n",
" energy_outflow[\"Outflow Links\"] = np.minimum(np.zeros_like(links_flow), links_flow)\n", " energy_outflow[\"Outflow Links\"] = np.minimum(np.zeros_like(links_flow), links_flow)\n",
@ -195,15 +205,29 @@
" if not storage.empty:\n", " if not storage.empty:\n",
" storage_p = n.storage_units_t.p[storage].sum(axis=1).values\n", " storage_p = n.storage_units_t.p[storage].sum(axis=1).values\n",
" # energy_inflow[\"Storage Discharge\"] = np.maximum(np.zeros_like(links_flow), storage_p)\n", " # energy_inflow[\"Storage Discharge\"] = np.maximum(np.zeros_like(links_flow), storage_p)\n",
" energy_inflow[\"Hydro\"] = energy_inflow[\"Hydro\"].values + np.maximum(np.zeros_like(links_flow), storage_p)\n", " energy_inflow[\"Hydro\"] = energy_inflow[\"Hydro\"].values + np.maximum(\n",
" energy_outflow[\"Storage Charge\"] = np.minimum(np.zeros_like(links_flow), storage_p)\n", " np.zeros_like(links_flow), storage_p\n",
" )\n",
" energy_outflow[\"Storage Charge\"] = np.minimum(\n",
" np.zeros_like(links_flow), storage_p\n",
" )\n",
"\n", "\n",
" energy_inflow = energy_inflow.iloc[:-1].multiply(1e-3)\n", " energy_inflow = energy_inflow.iloc[:-1].multiply(1e-3)\n",
" energy_outflow = energy_outflow.iloc[:-1].multiply(1e-3)\n", " energy_outflow = energy_outflow.iloc[:-1].multiply(1e-3)\n",
" load = n.loads_t.p_set[bus].iloc[:-1].multiply(1e-3)\n", " load = n.loads_t.p_set[bus].iloc[:-1].multiply(1e-3)\n",
"\n", "\n",
" show_techs = energy_inflow.sum().sort_values(ascending=False).iloc[:num_techs_shown].index.tolist()\n", " show_techs = (\n",
" others = energy_inflow.sum().sort_values(ascending=False).iloc[num_techs_shown:].index.tolist()\n", " energy_inflow.sum()\n",
" .sort_values(ascending=False)\n",
" .iloc[:num_techs_shown]\n",
" .index.tolist()\n",
" )\n",
" others = (\n",
" energy_inflow.sum()\n",
" .sort_values(ascending=False)\n",
" .iloc[num_techs_shown:]\n",
" .index.tolist()\n",
" )\n",
" # show_techs = entsoe_df.sum().sort_values(ascending=False).iloc[:num_techs_shown].index.tolist()\n", " # show_techs = entsoe_df.sum().sort_values(ascending=False).iloc[:num_techs_shown].index.tolist()\n",
"\n", "\n",
" show_techs = intersection(show_techs, entsoe_df.columns.tolist())\n", " show_techs = intersection(show_techs, entsoe_df.columns.tolist())\n",
@ -214,58 +238,117 @@
"\n", "\n",
" entsoe_df.index = load.index\n", " entsoe_df.index = load.index\n",
"\n", "\n",
"\n",
" energy_inflow[\"Others\"] = energy_inflow.drop(columns=show_techs).sum(axis=1)\n", " energy_inflow[\"Others\"] = energy_inflow.drop(columns=show_techs).sum(axis=1)\n",
"\n", "\n",
" # plot timeframe\n", " # plot timeframe\n",
" axs[0,0].plot(index, load.loc[index].values, linestyle=\"--\", color=\"k\", linewidth=2, label=\"PyPSA Load\")\n", " axs[0, 0].plot(\n",
" axs[0,1].plot(index, load.loc[index].values, linestyle=\"--\", color=\"k\", linewidth=2)\n", " index,\n",
"\n", " load.loc[index].values,\n",
" axs[0,1].stackplot(index, *[energy_inflow[col].loc[index].values for col in show_techs + [\"Others\"]])\n", " linestyle=\"--\",\n",
" axs[0,1].stackplot(index, *[energy_outflow[col].loc[index].values for col in energy_outflow.columns],\n", " color=\"k\",\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n", " linewidth=2,\n",
" labels=energy_outflow.columns\n", " label=\"PyPSA Load\",\n",
" )\n",
" axs[0, 1].plot(\n",
" index, load.loc[index].values, linestyle=\"--\", color=\"k\", linewidth=2\n",
" )\n", " )\n",
"\n", "\n",
" axs[0,0].stackplot(index, *[entsoe_df[col].loc[index].values for col in show_techs + [\"Others\"]], labels=show_techs+[\"Others\"])\n", " axs[0, 1].stackplot(\n",
" index,\n",
" *[energy_inflow[col].loc[index].values for col in show_techs + [\"Others\"]],\n",
" )\n",
" axs[0, 1].stackplot(\n",
" index,\n",
" *[energy_outflow[col].loc[index].values for col in energy_outflow.columns],\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n",
" labels=energy_outflow.columns,\n",
" )\n",
"\n", "\n",
" axs[0,1].plot(index,\n", " axs[0, 0].stackplot(\n",
" energy_inflow.loc[index][show_techs + [\"Others\"]].sum(axis=1).values + energy_outflow.loc[index].sum(axis=1).values,\n", " index,\n",
" color=\"brown\", linestyle=\":\", linewidth=2, label=\"Accum Gen\")\n", " *[entsoe_df[col].loc[index].values for col in show_techs + [\"Others\"]],\n",
" labels=show_techs + [\"Others\"],\n",
" )\n",
"\n",
" axs[0, 1].plot(\n",
" index,\n",
" energy_inflow.loc[index][show_techs + [\"Others\"]].sum(axis=1).values\n",
" + energy_outflow.loc[index].sum(axis=1).values,\n",
" color=\"brown\",\n",
" linestyle=\":\",\n",
" linewidth=2,\n",
" label=\"Accum Gen\",\n",
" )\n",
"\n", "\n",
" axs[0, 0].legend()\n", " axs[0, 0].legend()\n",
" axs[0, 1].legend()\n", " axs[0, 1].legend()\n",
"\n", "\n",
" \n",
" # plot whole year\n", " # plot whole year\n",
"\n", "\n",
" index = load.resample(coarse_freq).mean().index\n", " index = load.resample(coarse_freq).mean().index\n",
"\n", "\n",
" axs[1,0].plot(index, load.resample(coarse_freq).mean().values, linestyle=\"--\", color=\"k\", linewidth=2, label=\"PyPSA Load\")\n", " axs[1, 0].plot(\n",
" axs[1,1].plot(index, load.resample(coarse_freq).mean().values, linestyle=\"--\", color=\"k\", linewidth=2)\n", " index,\n",
"\n", " load.resample(coarse_freq).mean().values,\n",
" axs[1,1].stackplot(index, *[energy_inflow[col].resample(coarse_freq).mean().values for col in show_techs + [\"Others\"]])\n", " linestyle=\"--\",\n",
" axs[1,1].stackplot(index, *[energy_outflow[col].resample(coarse_freq).mean().values for col in energy_outflow.columns],\n", " color=\"k\",\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n", " linewidth=2,\n",
" labels=energy_outflow.columns\n", " label=\"PyPSA Load\",\n",
" )\n",
" axs[1, 1].plot(\n",
" index,\n",
" load.resample(coarse_freq).mean().values,\n",
" linestyle=\"--\",\n",
" color=\"k\",\n",
" linewidth=2,\n",
" )\n", " )\n",
"\n", "\n",
" axs[1,0].stackplot(index, *[entsoe_df[col].resample(coarse_freq).mean().values for col in show_techs + [\"Others\"]], labels=show_techs+[\"Others\"])\n", " axs[1, 1].stackplot(\n",
" index,\n",
" *[\n",
" energy_inflow[col].resample(coarse_freq).mean().values\n",
" for col in show_techs + [\"Others\"]\n",
" ],\n",
" )\n",
" axs[1, 1].stackplot(\n",
" index,\n",
" *[\n",
" energy_outflow[col].resample(coarse_freq).mean().values\n",
" for col in energy_outflow.columns\n",
" ],\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n",
" labels=energy_outflow.columns,\n",
" )\n",
"\n", "\n",
" axs[1,1].plot(index,\n", " axs[1, 0].stackplot(\n",
" energy_inflow.resample(coarse_freq).mean()[show_techs + [\"Others\"]].sum(axis=1).values + \n", " index,\n",
" energy_outflow.resample(coarse_freq).mean().sum(axis=1).values,\n", " *[\n",
" color=\"brown\", linestyle=\":\", linewidth=2, label=\"Accum Gen\")\n", " entsoe_df[col].resample(coarse_freq).mean().values\n",
" for col in show_techs + [\"Others\"]\n",
" ],\n",
" labels=show_techs + [\"Others\"],\n",
" )\n",
"\n", "\n",
" axs[1, 1].plot(\n",
" index,\n",
" energy_inflow.resample(coarse_freq)\n",
" .mean()[show_techs + [\"Others\"]]\n",
" .sum(axis=1)\n",
" .values\n",
" + energy_outflow.resample(coarse_freq).mean().sum(axis=1).values,\n",
" color=\"brown\",\n",
" linestyle=\":\",\n",
" linewidth=2,\n",
" label=\"Accum Gen\",\n",
" )\n",
"\n", "\n",
" axs[1, 0].legend()\n", " axs[1, 0].legend()\n",
" axs[1, 1].legend()\n", " axs[1, 1].legend()\n",
"\n", "\n",
"\n", " y_min = pd.concat([energy_outflow.sum(axis=1)]).min()\n",
" y_min = pd.concat([\n", " y_max = pd.concat(\n",
" energy_outflow.sum(axis=1)]).min()\n", " [energy_inflow.sum(axis=1), entsoe_df.sum(axis=1)], ignore_index=True\n",
" y_max = pd.concat([\n", " ).max()\n",
" energy_inflow.sum(axis=1), entsoe_df.sum(axis=1)], ignore_index=True).max()\n",
"\n", "\n",
" for ax in axs[:2, :2].flatten():\n", " for ax in axs[:2, :2].flatten():\n",
" ax.set_ylim(y_min, y_max)\n", " ax.set_ylim(y_min, y_max)\n",
@ -279,7 +362,9 @@
" axs[2, 1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n", " axs[2, 1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
"\n", "\n",
" # -------------------------- electricity prices comparison ----------------------------------\n", " # -------------------------- electricity prices comparison ----------------------------------\n",
" prices_col = [col for col in n.buses_t.marginal_price.columns if col.startswith(country[:2])]\n", " prices_col = [\n",
" col for col in n.buses_t.marginal_price.columns if col.startswith(country[:2])\n",
" ]\n",
" pypsa_prices = n.buses_t.marginal_price[prices_col].mean(axis=1)\n", " pypsa_prices = n.buses_t.marginal_price[prices_col].mean(axis=1)\n",
"\n", "\n",
" full_index = pypsa_prices.index\n", " full_index = pypsa_prices.index\n",
@ -287,27 +372,48 @@
" coarse_pypsa_prices = pypsa_prices.resample(coarse_freq).mean()\n", " coarse_pypsa_prices = pypsa_prices.resample(coarse_freq).mean()\n",
" pypsa_prices = pypsa_prices.loc[start:end]\n", " pypsa_prices = pypsa_prices.loc[start:end]\n",
"\n", "\n",
" axs[0,2].plot(pypsa_prices.index, pypsa_prices.values, label=\"PyPSA prices\", color=\"royalblue\")\n", " axs[0, 2].plot(\n",
" axs[1,2].plot(coarse_pypsa_prices.index, coarse_pypsa_prices.values, label=\"PyPSA prices\", color=\"royalblue\")\n", " pypsa_prices.index, pypsa_prices.values, label=\"PyPSA prices\", color=\"royalblue\"\n",
" )\n",
" axs[1, 2].plot(\n",
" coarse_pypsa_prices.index,\n",
" coarse_pypsa_prices.values,\n",
" label=\"PyPSA prices\",\n",
" color=\"royalblue\",\n",
" )\n",
"\n", "\n",
" try:\n", " try:\n",
" entsoe_prices = pd.read_csv(data_path / \"price_data\" / country,\n", " entsoe_prices = pd.read_csv(\n",
" data_path / \"price_data\" / country,\n",
" index_col=0,\n", " index_col=0,\n",
" parse_dates=True,\n", " parse_dates=True,\n",
" ).iloc[:-1]\n", " ).iloc[:-1]\n",
"\n",
" def make_tz_time(time):\n", " def make_tz_time(time):\n",
" return pd.Timestamp(time).tz_convert(\"utc\")\n", " return pd.Timestamp(time).tz_convert(\"utc\")\n",
"\n", "\n",
" # entsoe_prices.index = pd.Series(entsoe_prices.index).apply(lambda time: make_tz_time(time))\n", " # entsoe_prices.index = pd.Series(entsoe_prices.index).apply(lambda time: make_tz_time(time))\n",
" entsoe_prices.index = full_index\n", " entsoe_prices.index = full_index\n",
" mean_abs_error = mean_absolute_error(entsoe_prices.values,\n", " mean_abs_error = mean_absolute_error(\n",
" n.buses_t.marginal_price[prices_col].mean(axis=1).values)\n", " entsoe_prices.values,\n",
" n.buses_t.marginal_price[prices_col].mean(axis=1).values,\n",
" )\n",
"\n", "\n",
" coarse_prices = entsoe_prices.resample(coarse_freq).mean()\n", " coarse_prices = entsoe_prices.resample(coarse_freq).mean()\n",
" entsoe_prices = entsoe_prices.loc[start:end]\n", " entsoe_prices = entsoe_prices.loc[start:end]\n",
"\n", "\n",
" axs[0,2].plot(entsoe_prices.index, entsoe_prices.values, label=\"ENTSOE prices\", color=\"darkred\")\n", " axs[0, 2].plot(\n",
" axs[1,2].plot(coarse_prices.index, coarse_prices.values, label=\"ENTSOE prices\", color=\"darkred\")\n", " entsoe_prices.index,\n",
" entsoe_prices.values,\n",
" label=\"ENTSOE prices\",\n",
" color=\"darkred\",\n",
" )\n",
" axs[1, 2].plot(\n",
" coarse_prices.index,\n",
" coarse_prices.values,\n",
" label=\"ENTSOE prices\",\n",
" color=\"darkred\",\n",
" )\n",
"\n", "\n",
" except FileNotFoundError:\n", " except FileNotFoundError:\n",
" mean_abs_error = None\n", " mean_abs_error = None\n",
@ -329,31 +435,52 @@
"\n", "\n",
" entsoe_ddf = entsoe_df[show_techs + [\"Others\"]].reset_index(drop=True)\n", " entsoe_ddf = entsoe_df[show_techs + [\"Others\"]].reset_index(drop=True)\n",
"\n", "\n",
" entsoe_ddf = pd.concat([\n", " entsoe_ddf = pd.concat(\n",
" entsoe_ddf[col].sort_values(ascending=False).reset_index(drop=True) for col in entsoe_ddf.columns\n", " [\n",
" ], axis=1)\n", " entsoe_ddf[col].sort_values(ascending=False).reset_index(drop=True)\n",
" for col in entsoe_ddf.columns\n",
" ],\n",
" axis=1,\n",
" )\n",
"\n", "\n",
" axs[2,0].stackplot(range(len(entsoe_ddf)), *[entsoe_ddf[col].values for col in entsoe_ddf.columns],\n", " axs[2, 0].stackplot(\n",
" labels=entsoe_ddf.columns)\n", " range(len(entsoe_ddf)),\n",
" *[entsoe_ddf[col].values for col in entsoe_ddf.columns],\n",
" labels=entsoe_ddf.columns,\n",
" )\n",
"\n", "\n",
" pypsa_ddf = energy_inflow[show_techs + [\"Others\"]].reset_index(drop=True)\n", " pypsa_ddf = energy_inflow[show_techs + [\"Others\"]].reset_index(drop=True)\n",
" pypsa_ddf = pd.concat([\n", " pypsa_ddf = pd.concat(\n",
" pypsa_ddf[col].sort_values(ascending=False).reset_index(drop=True) for col in pypsa_ddf.columns\n", " [\n",
" ], axis=1)\n", " pypsa_ddf[col].sort_values(ascending=False).reset_index(drop=True)\n",
" for col in pypsa_ddf.columns\n",
" ],\n",
" axis=1,\n",
" )\n",
"\n", "\n",
" axs[2,1].stackplot(range(len(pypsa_ddf)), *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n", " axs[2, 1].stackplot(\n",
" labels=pypsa_ddf.columns)\n", " range(len(pypsa_ddf)),\n",
" *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
" labels=pypsa_ddf.columns,\n",
" )\n",
"\n", "\n",
" ylim_max = max([pypsa_ddf.max(axis=0).sum(), entsoe_ddf.max(axis=0).sum()])\n", " ylim_max = max([pypsa_ddf.max(axis=0).sum(), entsoe_ddf.max(axis=0).sum()])\n",
"\n", "\n",
" pypsa_ddf = energy_outflow.reset_index(drop=True)\n", " pypsa_ddf = energy_outflow.reset_index(drop=True)\n",
" pypsa_ddf = pd.concat([\n", " pypsa_ddf = pd.concat(\n",
" pypsa_ddf[col].sort_values(ascending=True).reset_index(drop=True) for col in pypsa_ddf.columns\n", " [\n",
" ], axis=1)\n", " pypsa_ddf[col].sort_values(ascending=True).reset_index(drop=True)\n",
" for col in pypsa_ddf.columns\n",
" ],\n",
" axis=1,\n",
" )\n",
"\n", "\n",
" axs[2,1].stackplot(range(len(pypsa_ddf)), *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n", " axs[2, 1].stackplot(\n",
" range(len(pypsa_ddf)),\n",
" *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n", " colors=[\"seagreen\", \"royalblue\", \"gold\"],\n",
" labels=energy_outflow.columns)\n", " labels=energy_outflow.columns,\n",
" )\n",
"\n", "\n",
" ylim_min = energy_outflow.min(axis=0).sum()\n", " ylim_min = energy_outflow.min(axis=0).sum()\n",
"\n", "\n",
@ -361,53 +488,74 @@
" ax.legend()\n", " ax.legend()\n",
" ax.set_ylim(ylim_min, ylim_max)\n", " ax.set_ylim(ylim_min, ylim_max)\n",
"\n", "\n",
" pypsa_totals = pd.concat([energy_inflow[show_techs + [\"Others\"]], energy_outflow], axis=1).sum() \n", " pypsa_totals = pd.concat(\n",
" [energy_inflow[show_techs + [\"Others\"]], energy_outflow], axis=1\n",
" ).sum()\n",
"\n", "\n",
" entsoe_totals = entsoe_df.sum()\n", " entsoe_totals = entsoe_df.sum()\n",
" totals = pd.DataFrame(index=pypsa_totals.index)\n", " totals = pd.DataFrame(index=pypsa_totals.index)\n",
"\n", "\n",
" for tech in pypsa_totals.index:\n", " for tech in pypsa_totals.index:\n",
" if tech not in entsoe_totals.index:\n", " if tech not in entsoe_totals.index:\n",
" entsoe_totals.loc[tech] = 0.\n", " entsoe_totals.loc[tech] = 0.0\n",
"\n", "\n",
" totals[\"Pypsa\"] = pypsa_totals\n", " totals[\"Pypsa\"] = pypsa_totals\n",
" totals[\"Entsoe\"] = entsoe_totals\n", " totals[\"Entsoe\"] = entsoe_totals\n",
" totals[\"Technology\"] = totals.index\n", " totals[\"Technology\"] = totals.index\n",
"\n", "\n",
" totals = pd.concat([\n", " totals = pd.concat(\n",
" pd.DataFrame({\"Source\": [\"PyPSA\" for _ in range(len(pypsa_totals))],\n", " [\n",
" pd.DataFrame(\n",
" {\n",
" \"Source\": [\"PyPSA\" for _ in range(len(pypsa_totals))],\n",
" \"Technology\": pypsa_totals.index,\n", " \"Technology\": pypsa_totals.index,\n",
" \"Total Generation\": pypsa_totals.values,\n", " \"Total Generation\": pypsa_totals.values,\n",
" }),\n", " }\n",
" pd.DataFrame({\"Source\": [\"ENTSO-E\" for _ in range(len(entsoe_totals))],\n", " ),\n",
" pd.DataFrame(\n",
" {\n",
" \"Source\": [\"ENTSO-E\" for _ in range(len(entsoe_totals))],\n",
" \"Technology\": entsoe_totals.index,\n", " \"Technology\": entsoe_totals.index,\n",
" \"Total Generation\": entsoe_totals.values,\n", " \"Total Generation\": entsoe_totals.values,\n",
" }),], axis=0\n", " }\n",
" ),\n",
" ],\n",
" axis=0,\n",
" )\n", " )\n",
"\n", "\n",
" sns.barplot(data=totals, x=\"Technology\", y=\"Total Generation\", hue=\"Source\", ax=axs[2,2],\n", " sns.barplot(\n",
" palette=\"dark\", alpha=.6, edgecolor=\"k\")\n", " data=totals,\n",
" x=\"Technology\",\n",
" y=\"Total Generation\",\n",
" hue=\"Source\",\n",
" ax=axs[2, 2],\n",
" palette=\"dark\",\n",
" alpha=0.6,\n",
" edgecolor=\"k\",\n",
" )\n",
"\n", "\n",
" axs[2, 0].set_xlabel(\"Hours\")\n", " axs[2, 0].set_xlabel(\"Hours\")\n",
" axs[2, 1].set_xlabel(\"Hours\")\n", " axs[2, 1].set_xlabel(\"Hours\")\n",
" axs[2, 2].set_ylabel(\"Total Generation [GWh]\")\n", " axs[2, 2].set_ylabel(\"Total Generation [GWh]\")\n",
" axs[2,2].set_xticks(axs[2,2].get_xticks(), axs[2,2].get_xticklabels(), rotation=45, ha='right')\n", " axs[2, 2].set_xticks(\n",
" axs[2, 2].get_xticks(), axs[2, 2].get_xticklabels(), rotation=45, ha=\"right\"\n",
" )\n",
"\n", "\n",
" corrs = (\n", " corrs = (\n",
" energy_inflow\n", " energy_inflow.corrwith(entsoe_df)\n",
" .corrwith(entsoe_df)\n",
" .drop(index=\"Others\")\n", " .drop(index=\"Others\")\n",
" .dropna()\n", " .dropna()\n",
" .sort_values(ascending=False)\n", " .sort_values(ascending=False)\n",
" )\n", " )\n",
"\n", "\n",
" for col, ax in zip(corrs.index[:2].tolist() + [corrs.index[-1]], axs[3]):\n", " for col, ax in zip(corrs.index[:2].tolist() + [corrs.index[-1]], axs[3]):\n",
" ax.scatter(entsoe_df[col].values,\n", " ax.scatter(\n",
" entsoe_df[col].values,\n",
" energy_inflow[col].values,\n", " energy_inflow[col].values,\n",
" color=\"darkred\",\n", " color=\"darkred\",\n",
" alpha=0.5,\n", " alpha=0.5,\n",
" s=20,\n", " s=20,\n",
" edgecolor=\"k\" \n", " edgecolor=\"k\",\n",
" )\n", " )\n",
" ax.set_title(f\"{col}; Pearson Corr {np.around(corrs.loc[col], decimals=4)}\")\n", " ax.set_title(f\"{col}; Pearson Corr {np.around(corrs.loc[col], decimals=4)}\")\n",
" ax.set_xlabel(\"ENTSO-E Generation [GW]\")\n", " ax.set_xlabel(\"ENTSO-E Generation [GW]\")\n",
@ -416,7 +564,6 @@
" for ax in axs[:2].flatten():\n", " for ax in axs[:2].flatten():\n",
" ax.set_xlabel(\"Datetime\")\n", " ax.set_xlabel(\"Datetime\")\n",
"\n", "\n",
" \n",
" plt.tight_layout()\n", " plt.tight_layout()\n",
" plt.show()" " plt.show()"
] ]
@ -424,9 +571,9 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "pypsa-eur", "display_name": "",
"language": "python", "language": "python",
"name": "python3" "name": ""
}, },
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {