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@ -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\")"
] ]
}, },
@ -26,15 +28,15 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"available_models = {\n", "available_models = {\n",
" \"model_1\": \"elec_s_37_ec_lv1.0_.nc\", \n", " \"model_1\": \"elec_s_37_ec_lv1.0_.nc\",\n",
" \"model_2\": \"elec_s_37_ec_lv1.0_3H_withUC.nc\",\n", " \"model_2\": \"elec_s_37_ec_lv1.0_3H_withUC.nc\",\n",
" \"model_3\": \"elec_s_37_ec_lv1.0_Co2L-noUC-noCo2price.nc\",\n", " \"model_3\": \"elec_s_37_ec_lv1.0_Co2L-noUC-noCo2price.nc\",\n",
" \"model_4\": \"elec_s_37_ec_lv1.0_Ep.nc\", \n", " \"model_4\": \"elec_s_37_ec_lv1.0_Ep.nc\",\n",
" \"model_5\": \"elec_s_37_ec_lv1.0_Ep_new.nc\", \n", " \"model_5\": \"elec_s_37_ec_lv1.0_Ep_new.nc\",\n",
"}\n", "}\n",
"\n", "\n",
"data_path = Path.cwd() / \"..\" / \"..\"\n", "data_path = Path.cwd() / \"..\" / \"..\"\n",
"model_path = data_path / available_models[\"model_5\"]\n", "model_path = data_path / available_models[\"model_5\"]\n",
"\n", "\n",
"with open(data_path / \"generation_data\" / \"generation_mapper_pypsa.json\", \"r\") as f:\n", "with open(data_path / \"generation_data\" / \"generation_mapper_pypsa.json\", \"r\") as f:\n",
" pypsa_generation_mapper = json.load(f)" " pypsa_generation_mapper = json.load(f)"
@ -60,7 +62,7 @@
" for val in alist:\n", " for val in alist:\n",
" if val in blist:\n", " if val in blist:\n",
" total_list.append(val)\n", " total_list.append(val)\n",
" return total_list " " return total_list"
] ]
}, },
{ {
@ -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",
@ -96,12 +96,11 @@
" ].index\n", " ].index\n",
"\n", "\n",
" if len(curr_gen):\n", " if len(curr_gen):\n",
" df[key] = n.generators_t.p[curr_gen].mean(axis=1) \n", " df[key] = n.generators_t.p[curr_gen].mean(axis=1)\n",
" 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",
@ -131,23 +131,25 @@
"\n", "\n",
" \"\"\" \n", " \"\"\" \n",
" 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",
" parse_dates=True,\n", " data_path / \"harmonised_generation_data\" / (\"prepared_\" + country),\n",
" index_col=0)\n", " parse_dates=True,\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",
" entsoe_df = entsoe_df.multiply(1e-3)\n", " entsoe_df = entsoe_df.multiply(1e-3)\n",
" except FileNotFoundError:\n", " except FileNotFoundError:\n",
" continue \n", " continue\n",
" \n", "\n",
" fig, axs = plt.subplots(4, 3, figsize=(20, 20))\n", " fig, axs = plt.subplots(4, 3, figsize=(20, 20))\n",
"\n", "\n",
" axs[0,0].set_title(pycountry.countries.get(alpha_2=country[:2]).name)\n", " axs[0, 0].set_title(pycountry.countries.get(alpha_2=country[:2]).name)\n",
"\n", "\n",
" start = pd.Timestamp(\"2019-01-01\") # for small time frame\n", " start = pd.Timestamp(\"2019-01-01\") # for small time frame\n",
" end = pd.Timestamp(\"2019-01-14\")\n", " end = pd.Timestamp(\"2019-01-14\")\n",
" coarse_freq = \"3d\"\n", " coarse_freq = \"3d\"\n",
"\n", "\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",
@ -169,11 +173,11 @@
"\n", "\n",
" lines_flow = np.zeros(energy_inflow.shape[0])\n", " lines_flow = np.zeros(energy_inflow.shape[0])\n",
" if not lines0.empty:\n", " if not lines0.empty:\n",
" lines_flow = - n.lines_t.p0[lines0].sum(axis=1)\n", " lines_flow = -n.lines_t.p0[lines0].sum(axis=1)\n",
" \n", "\n",
" if not lines1.empty:\n", " if not lines1.empty:\n",
" lines_flow -= n.lines_t.p1[lines1].sum(axis=1)\n", " lines_flow -= n.lines_t.p1[lines1].sum(axis=1)\n",
" \n", "\n",
" energy_inflow[\"Inflow Lines\"] = np.maximum(np.zeros_like(lines_flow), lines_flow)\n", " energy_inflow[\"Inflow Lines\"] = np.maximum(np.zeros_like(lines_flow), lines_flow)\n",
" energy_outflow[\"Outflow Lines\"] = np.minimum(np.zeros_like(lines_flow), lines_flow)\n", " energy_outflow[\"Outflow Lines\"] = np.minimum(np.zeros_like(lines_flow), lines_flow)\n",
"\n", "\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",
@ -214,114 +238,196 @@
"\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",
" load.loc[index].values,\n",
" linestyle=\"--\",\n",
" color=\"k\",\n",
" linewidth=2,\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",
" axs[0,1].stackplot(index, *[energy_inflow[col].loc[index].values for col in show_techs + [\"Others\"]])\n", " axs[0, 1].stackplot(\n",
" axs[0,1].stackplot(index, *[energy_outflow[col].loc[index].values for col in energy_outflow.columns],\n", " index,\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n", " *[energy_inflow[col].loc[index].values for col in show_techs + [\"Others\"]],\n",
" labels=energy_outflow.columns\n", " )\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[0,0].stackplot(index, *[entsoe_df[col].loc[index].values for col in show_techs + [\"Others\"]], labels=show_techs+[\"Others\"])\n", " axs[0, 0].stackplot(\n",
" \n", " index,\n",
" axs[0,1].plot(index,\n", " *[entsoe_df[col].loc[index].values for col in show_techs + [\"Others\"]],\n",
" energy_inflow.loc[index][show_techs + [\"Others\"]].sum(axis=1).values + energy_outflow.loc[index].sum(axis=1).values,\n", " labels=show_techs + [\"Others\"],\n",
" color=\"brown\", linestyle=\":\", linewidth=2, label=\"Accum Gen\")\n", " )\n",
"\n", "\n",
" axs[0,0].legend()\n", " axs[0, 1].plot(\n",
" axs[0,1].legend()\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",
" axs[0, 0].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",
" load.resample(coarse_freq).mean().values,\n",
" linestyle=\"--\",\n",
" color=\"k\",\n",
" linewidth=2,\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",
" axs[1,1].stackplot(index, *[energy_inflow[col].resample(coarse_freq).mean().values for col in show_techs + [\"Others\"]])\n", " axs[1, 1].stackplot(\n",
" axs[1,1].stackplot(index, *[energy_outflow[col].resample(coarse_freq).mean().values for col in energy_outflow.columns],\n", " index,\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n", " *[\n",
" labels=energy_outflow.columns\n", " energy_inflow[col].resample(coarse_freq).mean().values\n",
" )\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[1,0].stackplot(index, *[entsoe_df[col].resample(coarse_freq).mean().values for col in show_techs + [\"Others\"]], labels=show_techs+[\"Others\"])\n", " axs[1, 0].stackplot(\n",
" index,\n",
" *[\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(index,\n", " axs[1, 1].plot(\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", " energy_inflow.resample(coarse_freq)\n",
" color=\"brown\", linestyle=\":\", linewidth=2, label=\"Accum Gen\")\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, 1].legend()\n",
"\n", "\n",
" axs[1,0].legend()\n", " y_min = pd.concat([energy_outflow.sum(axis=1)]).min()\n",
" axs[1,1].legend()\n", " y_max = pd.concat(\n",
" [energy_inflow.sum(axis=1), entsoe_df.sum(axis=1)], ignore_index=True\n",
" ).max()\n",
"\n", "\n",
"\n", " for ax in axs[:2, :2].flatten():\n",
" y_min = pd.concat([\n",
" energy_outflow.sum(axis=1)]).min()\n",
" y_max = pd.concat([\n",
" energy_inflow.sum(axis=1), entsoe_df.sum(axis=1)], ignore_index=True).max()\n",
"\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",
" ax.set_ylim(y_min, y_max)\n", " ax.set_ylim(y_min, y_max)\n",
" \n", "\n",
" axs[0,0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n", " axs[0, 0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
" axs[0,1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n", " axs[0, 1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
" axs[1,0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n", " axs[1, 0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
" axs[1,1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n", " axs[1, 1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
" axs[2,0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n", " axs[2, 0].set_ylabel(\"ENTSOE Gen and PyPSA Load [GW]\")\n",
" 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",
" \n", "\n",
" 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",
" pass\n", " pass\n",
"\n", "\n",
" upper_lim = pd.concat((entsoe_prices, pypsa_prices), axis=0).max().max() \n", " upper_lim = pd.concat((entsoe_prices, pypsa_prices), axis=0).max().max()\n",
" for ax in axs[:2,2]:\n", " for ax in axs[:2, 2]:\n",
" ax.set_ylim(0, upper_lim)\n", " ax.set_ylim(0, upper_lim)\n",
" ax.set_ylabel(\"Electricty Prices [Euro/MWh]\")\n", " ax.set_ylabel(\"Electricty Prices [Euro/MWh]\")\n",
" ax.legend()\n", " ax.legend()\n",
" \n", "\n",
" if not mean_abs_error is None:\n", " if not mean_abs_error is None:\n",
" axs[1,-1].set_title(f\"Mean Abs Error: {np.around(mean_abs_error, decimals=2)}\") \n", " axs[1, -1].set_title(f\"Mean Abs Error: {np.around(mean_abs_error, decimals=2)}\")\n",
" \n", "\n",
" # remaining_cols = energy_inflow.drop(columns=show_techs+[\"Others\"]).columns.tolist()\n", " # remaining_cols = energy_inflow.drop(columns=show_techs+[\"Others\"]).columns.tolist()\n",
" # axs[1,0].set_title(f\"Others: {remaining_cols}\")\n", " # axs[1,0].set_title(f\"Others: {remaining_cols}\")\n",
"\n", "\n",
@ -329,94 +435,135 @@
"\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",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n", " range(len(pypsa_ddf)),\n",
" labels=energy_outflow.columns)\n", " *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\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",
" for ax in axs[2,:2]:\n", " for ax in axs[2, :2]:\n",
" 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",
" \"Technology\": pypsa_totals.index,\n", " pd.DataFrame(\n",
" \"Total Generation\": pypsa_totals.values,\n", " {\n",
" }),\n", " \"Source\": [\"PyPSA\" for _ in range(len(pypsa_totals))],\n",
" pd.DataFrame({\"Source\": [\"ENTSO-E\" for _ in range(len(entsoe_totals))],\n", " \"Technology\": pypsa_totals.index,\n",
" \"Technology\": entsoe_totals.index,\n", " \"Total Generation\": pypsa_totals.values,\n",
" \"Total Generation\": entsoe_totals.values,\n", " }\n",
" }),], axis=0\n", " ),\n",
" pd.DataFrame(\n",
" {\n",
" \"Source\": [\"ENTSO-E\" for _ in range(len(entsoe_totals))],\n",
" \"Technology\": entsoe_totals.index,\n",
" \"Total Generation\": entsoe_totals.values,\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",
" \n", " x=\"Technology\",\n",
" axs[2,0].set_xlabel(\"Hours\")\n", " y=\"Total Generation\",\n",
" axs[2,1].set_xlabel(\"Hours\")\n", " hue=\"Source\",\n",
" axs[2,2].set_ylabel(\"Total Generation [GWh]\")\n", " ax=axs[2, 2],\n",
" axs[2,2].set_xticks(axs[2,2].get_xticks(), axs[2,2].get_xticklabels(), rotation=45, ha='right')\n", " palette=\"dark\",\n",
" alpha=0.6,\n",
" edgecolor=\"k\",\n",
" )\n",
"\n",
" axs[2, 0].set_xlabel(\"Hours\")\n",
" axs[2, 1].set_xlabel(\"Hours\")\n",
" axs[2, 2].set_ylabel(\"Total Generation [GWh]\")\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",
" energy_inflow[col].values,\n", " entsoe_df[col].values,\n",
" color=\"darkred\",\n", " energy_inflow[col].values,\n",
" alpha=0.5,\n", " color=\"darkred\",\n",
" s=20,\n", " alpha=0.5,\n",
" edgecolor=\"k\" \n", " s=20,\n",
" )\n", " edgecolor=\"k\",\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",
" ax.set_ylabel(\"PyPSA-Eur Generation [GW]\")\n", " ax.set_ylabel(\"PyPSA-Eur Generation [GW]\")\n",
" \n", "\n",
" 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": {