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{
"cells": [
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"import pypsa\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"from pathlib import Path\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(\"ggplot\")\n",
"import pycountry\n",
"import json\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"available_models = {\n",
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" \"model_1\": \"elec_s_37_ec_lv1.0_.nc\", \n",
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" \"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",
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" \"model_4\": \"elec_s_37_ec_lv1.0_Ep.nc\", \n",
" \"model_5\": \"elec_s_37_ec_lv1.0_Ep_new.nc\", \n",
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"}\n",
"\n",
"data_path = Path.cwd() / \"..\" / \"..\"\n",
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"model_path = data_path / available_models[\"model_5\"]\n",
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"\n",
"with open(data_path / \"generation_data\" / \"generation_mapper_pypsa.json\", \"r\") as f:\n",
" pypsa_generation_mapper = json.load(f)"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"n = pypsa.Network(str(model_path))"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"def intersection(alist, blist):\n",
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" total_list = list()\n",
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" for val in alist:\n",
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" if val in blist:\n",
" total_list.append(val)\n",
" return total_list "
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"pypsa_generation_mapper"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"countries = set([col[:2] for col in n.generators_t.p.columns])\n",
"gen = set([col[6:] for col in n.generators_t.p.columns])\n",
"\n",
"for i, country in enumerate(countries):\n",
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"\n",
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" 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 = n.generators.loc[n.generators.bus.str.contains(country)]\n",
"\n",
" for key, gens in pypsa_generation_mapper.items():\n",
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"\n",
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" # curr_gen = country_generation.loc[\n",
" # (country_generation.carrier.str.contains(tech) for tech in gens).astype(bool)].index\n",
" curr_gen = country_generation.loc[\n",
" country_generation.carrier.apply(lambda carr: carr in gens)\n",
" ].index\n",
"\n",
" if len(curr_gen):\n",
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" df[key] = n.generators_t.p[curr_gen].mean(axis=1) \n",
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" else:\n",
" df[key] = np.zeros(len(df))\n",
"\n",
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" df.to_csv(data_path / \"pypsa_data\" / (country+\".csv\"))\n",
" "
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"import seaborn as sns\n",
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"from sklearn.metrics import mean_absolute_error\n",
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"\n",
"\n",
"for num, country in enumerate(os.listdir(data_path / \"pypsa_data\")):\n",
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"\n",
" # country = \"GR.csv\"\n",
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" cc = country[:2]\n",
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"\n",
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" country_buses = np.unique(n.generators.loc[n.generators.bus.str.contains(cc)].bus.values)\n",
" print(f\"Buses for country {country[:-4]}: \", country_buses)\n",
"\n",
" if not len(country_buses) == 1:\n",
" print(\"Current implementation is for one bus per country\")\n",
" print(f\"Skipping!\")\n",
" continue\n",
"\n",
" bus = country_buses[0]\n",
"\n",
" \"\"\" \n",
" pypsa_df = pd.read_csv(data_path / \"pypsa_data\" / country, parse_dates=True, index_col=0)\n",
" \"\"\" \n",
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" try:\n",
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" entsoe_df = pd.read_csv(data_path / \"harmonised_generation_data\" / (\"prepared_\"+country),\n",
" parse_dates=True,\n",
" index_col=0)\n",
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"\n",
" entsoe_df.columns = [col[:-6] for col in entsoe_df.columns]\n",
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" entsoe_df = entsoe_df.iloc[1:]\n",
" entsoe_df = entsoe_df.multiply(1e-3)\n",
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" except FileNotFoundError:\n",
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" continue \n",
" \n",
" fig, axs = plt.subplots(4, 3, figsize=(20, 20))\n",
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"\n",
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" axs[0,0].set_title(pycountry.countries.get(alpha_2=country[:2]).name)\n",
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"\n",
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" start = pd.Timestamp(\"2019-01-01\") # for small time frame\n",
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" end = pd.Timestamp(\"2019-01-14\")\n",
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" coarse_freq = \"3d\"\n",
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"\n",
" num_techs_shown = 6\n",
"\n",
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" energy_inflow = pd.DataFrame(index=n.loads_t.p_set.index)\n",
" energy_outflow = pd.DataFrame(index=n.loads_t.p_set.index)\n",
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"\n",
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" # add generation\n",
" country_gen = n.generators.loc[n.generators.bus == bus]\n",
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"\n",
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" 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",
" energy_inflow[tech] = n.generators_t.p[gens].sum(axis=1)\n",
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"\n",
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" # add inflows from lines\n",
" lines0 = n.lines.loc[n.lines.bus0 == bus].index\n",
" lines1 = n.lines.loc[n.lines.bus1 == bus].index\n",
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"\n",
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" lines_flow = np.zeros(energy_inflow.shape[0])\n",
" if not lines0.empty:\n",
" lines_flow = - n.lines_t.p0[lines0].sum(axis=1)\n",
" \n",
" if not lines1.empty:\n",
" lines_flow -= n.lines_t.p1[lines1].sum(axis=1)\n",
" \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",
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"\n",
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" # add inflows from links\n",
" links0 = n.links.loc[n.links.bus0 == bus].index\n",
" links1 = n.links.loc[n.links.bus1 == bus].index\n",
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"\n",
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" links_flow = np.zeros(energy_inflow.shape[0])\n",
" if not links0.empty:\n",
" links_flow = - n.links_t.p0[links0].multiply(n.links.loc[links0, \"efficiency\"]).sum(axis=1)\n",
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"\n",
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" if not links1.empty:\n",
" links_flow -= n.links_t.p1[links1].multiply(n.links.loc[links1, \"efficiency\"]).sum(axis=1)\n",
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"\n",
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" 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",
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"\n",
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" storage = n.storage_units.loc[n.storage_units.bus == bus].index\n",
" if not storage.empty:\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[\"Hydro\"] = energy_inflow[\"Hydro\"].values + np.maximum(np.zeros_like(links_flow), storage_p)\n",
" energy_outflow[\"Storage Charge\"] = np.minimum(np.zeros_like(links_flow), storage_p)\n",
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"\n",
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" energy_inflow = energy_inflow.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",
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"\n",
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" show_techs = energy_inflow.sum().sort_values(ascending=False).iloc[:num_techs_shown].index.tolist()\n",
" others = energy_inflow.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",
" show_techs = intersection(show_techs, entsoe_df.columns.tolist())\n",
" entsoe_df[\"Others\"] = entsoe_df.drop(columns=show_techs).sum(axis=1)\n",
"\n",
" # entsoe_df[show_techs + [\"Others\"]].loc[start:end].plot.area(ax=axs[0,0])\n",
" index = load.loc[start:end].index\n",
"\n",
" entsoe_df.index = load.index\n",
"\n",
"\n",
" energy_inflow[\"Others\"] = energy_inflow.drop(columns=show_techs).sum(axis=1)\n",
" \n",
" # plot timeframe\n",
" axs[0,0].plot(index, load.loc[index].values, linestyle=\"--\", color=\"k\", linewidth=2, label=\"PyPSA Load\")\n",
" axs[0,1].plot(index, load.loc[index].values, linestyle=\"--\", color=\"k\", linewidth=2)\n",
"\n",
" axs[0,1].stackplot(index, *[energy_inflow[col].loc[index].values for col in show_techs + [\"Others\"]])\n",
" axs[0,1].stackplot(index, *[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",
" axs[0,0].stackplot(index, *[entsoe_df[col].loc[index].values for col in show_techs + [\"Others\"]], labels=show_techs+[\"Others\"])\n",
" \n",
" axs[0,1].plot(index,\n",
" energy_inflow.loc[index][show_techs + [\"Others\"]].sum(axis=1).values + energy_outflow.loc[index].sum(axis=1).values,\n",
" color=\"brown\", linestyle=\":\", linewidth=2, label=\"Accum Gen\")\n",
"\n",
" axs[0,0].legend()\n",
" axs[0,1].legend()\n",
"\n",
" \n",
" # plot whole year\n",
"\n",
" index = load.resample(coarse_freq).mean().index\n",
"\n",
" axs[1,0].plot(index, load.resample(coarse_freq).mean().values, linestyle=\"--\", color=\"k\", linewidth=2, label=\"PyPSA Load\")\n",
" axs[1,1].plot(index, load.resample(coarse_freq).mean().values, linestyle=\"--\", color=\"k\", linewidth=2)\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(index, *[energy_outflow[col].resample(coarse_freq).mean().values for col in energy_outflow.columns],\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n",
" labels=energy_outflow.columns\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",
"\n",
" axs[1,1].plot(index,\n",
" energy_inflow.resample(coarse_freq).mean()[show_techs + [\"Others\"]].sum(axis=1).values + \n",
" energy_outflow.resample(coarse_freq).mean().sum(axis=1).values,\n",
" color=\"brown\", linestyle=\":\", linewidth=2, label=\"Accum Gen\")\n",
"\n",
"\n",
" axs[1,0].legend()\n",
" axs[1,1].legend()\n",
"\n",
"\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",
" \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[1,0].set_ylabel(\"ENTSOE Gen and PyPSA 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[2,1].set_ylabel(\"PyPSA Gen and Load [GW]\")\n",
"\n",
" # -------------------------- electricity prices comparison ----------------------------------\n",
" prices_col = [col for col in n.buses_t.marginal_price.columns if col.startswith(country[:2])]\n",
" pypsa_prices = n.buses_t.marginal_price[prices_col].mean(axis=1)\n",
"\n",
" full_index = pypsa_prices.index\n",
" \n",
" coarse_pypsa_prices = pypsa_prices.resample(coarse_freq).mean() \n",
" pypsa_prices = pypsa_prices.loc[start:end]\n",
"\n",
" axs[0,2].plot(pypsa_prices.index, pypsa_prices.values, label=\"PyPSA prices\", color=\"royalblue\")\n",
" axs[1,2].plot(coarse_pypsa_prices.index, coarse_pypsa_prices.values, label=\"PyPSA prices\", color=\"royalblue\")\n",
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"\n",
" try:\n",
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" entsoe_prices = pd.read_csv(data_path / \"price_data\" / country,\n",
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" index_col=0,\n",
" parse_dates=True,\n",
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" ).iloc[:-1]\n",
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" def make_tz_time(time):\n",
" return pd.Timestamp(time).tz_convert(\"utc\")\n",
"\n",
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" # entsoe_prices.index = pd.Series(entsoe_prices.index).apply(lambda time: make_tz_time(time))\n",
" entsoe_prices.index = full_index\n",
" mean_abs_error = mean_absolute_error(entsoe_prices.values,\n",
" n.buses_t.marginal_price[prices_col].mean(axis=1).values)\n",
"\n",
" coarse_prices = entsoe_prices.resample(coarse_freq).mean()\n",
" entsoe_prices = entsoe_prices.loc[start:end]\n",
"\n",
" axs[0,2].plot(entsoe_prices.index, entsoe_prices.values, label=\"ENTSOE prices\", color=\"darkred\")\n",
" axs[1,2].plot(coarse_prices.index, coarse_prices.values, label=\"ENTSOE prices\", color=\"darkred\")\n",
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"\n",
" except FileNotFoundError:\n",
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" mean_abs_error = None\n",
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" pass\n",
"\n",
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" upper_lim = pd.concat((entsoe_prices, pypsa_prices), axis=0).max().max() \n",
" for ax in axs[:2,2]:\n",
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" ax.set_ylim(0, upper_lim)\n",
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" ax.set_ylabel(\"Electricty Prices [Euro/MWh]\")\n",
" ax.legend()\n",
" \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",
" \n",
" # remaining_cols = energy_inflow.drop(columns=show_techs+[\"Others\"]).columns.tolist()\n",
" # axs[1,0].set_title(f\"Others: {remaining_cols}\")\n",
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"\n",
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" # ------------------------------- duration curves ------------------------------\n",
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"\n",
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" entsoe_ddf = entsoe_df[show_techs + [\"Others\"]].reset_index(drop=True)\n",
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"\n",
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" entsoe_ddf = pd.concat([\n",
" entsoe_ddf[col].sort_values(ascending=False).reset_index(drop=True) for col in entsoe_ddf.columns\n",
" ], axis=1)\n",
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"\n",
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" axs[2,0].stackplot(range(len(entsoe_ddf)), *[entsoe_ddf[col].values for col in entsoe_ddf.columns],\n",
" labels=entsoe_ddf.columns)\n",
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"\n",
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" pypsa_ddf = energy_inflow[show_techs + [\"Others\"]].reset_index(drop=True)\n",
" pypsa_ddf = pd.concat([\n",
" pypsa_ddf[col].sort_values(ascending=False).reset_index(drop=True) for col in pypsa_ddf.columns\n",
" ], axis=1)\n",
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"\n",
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" axs[2,1].stackplot(range(len(pypsa_ddf)), *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
" labels=pypsa_ddf.columns)\n",
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"\n",
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" ylim_max = max([pypsa_ddf.max(axis=0).sum(), entsoe_ddf.max(axis=0).sum()])\n",
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"\n",
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" pypsa_ddf = energy_outflow.reset_index(drop=True)\n",
" pypsa_ddf = pd.concat([\n",
" pypsa_ddf[col].sort_values(ascending=True).reset_index(drop=True) for col in pypsa_ddf.columns\n",
" ], axis=1)\n",
"\n",
" axs[2,1].stackplot(range(len(pypsa_ddf)), *[pypsa_ddf[col].values for col in pypsa_ddf.columns],\n",
" colors=[\"seagreen\", \"royalblue\", \"gold\"],\n",
" labels=energy_outflow.columns)\n",
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"\n",
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" ylim_min = energy_outflow.min(axis=0).sum()\n",
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"\n",
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" for ax in axs[2,:2]:\n",
" ax.legend()\n",
" ax.set_ylim(ylim_min, ylim_max)\n",
"\n",
" pypsa_totals = pd.concat([energy_inflow[show_techs + [\"Others\"]], energy_outflow], axis=1).sum() \n",
"\n",
" entsoe_totals = entsoe_df.sum()\n",
" totals = pd.DataFrame(index=pypsa_totals.index) \n",
" \n",
" for tech in pypsa_totals.index:\n",
" if tech not in entsoe_totals.index:\n",
" entsoe_totals.loc[tech] = 0.\n",
"\n",
" totals[\"Pypsa\"] = pypsa_totals\n",
" totals[\"Entsoe\"] = entsoe_totals\n",
" totals[\"Technology\"] = totals.index\n",
"\n",
" totals = pd.concat([\n",
" pd.DataFrame({\"Source\": [\"PyPSA\" for _ in range(len(pypsa_totals))],\n",
" \"Technology\": pypsa_totals.index,\n",
" \"Total Generation\": pypsa_totals.values,\n",
" }),\n",
" pd.DataFrame({\"Source\": [\"ENTSO-E\" for _ in range(len(entsoe_totals))],\n",
" \"Technology\": entsoe_totals.index,\n",
" \"Total Generation\": entsoe_totals.values,\n",
" }),], axis=0\n",
" )\n",
"\n",
" sns.barplot(data=totals, x=\"Technology\", y=\"Total Generation\", hue=\"Source\", ax=axs[2,2],\n",
" palette=\"dark\", alpha=.6, edgecolor=\"k\")\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(axs[2,2].get_xticks(), axs[2,2].get_xticklabels(), rotation=45, ha='right')\n",
"\n",
" corrs = (\n",
" energy_inflow\n",
" .corrwith(entsoe_df)\n",
" .drop(index=\"Others\")\n",
" .dropna()\n",
" .sort_values(ascending=False)\n",
" )\n",
"\n",
" for col, ax in zip(corrs.index[:2].tolist() + [corrs.index[-1]], axs[3]):\n",
" ax.scatter(entsoe_df[col].values,\n",
" energy_inflow[col].values,\n",
" color=\"darkred\",\n",
" alpha=0.5,\n",
" s=20,\n",
" edgecolor=\"k\" \n",
" )\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_ylabel(\"PyPSA-Eur Generation [GW]\")\n",
" \n",
" for ax in axs[:2].flatten():\n",
" ax.set_xlabel(\"Datetime\")\n",
"\n",
" \n",
" plt.tight_layout()\n",
" plt.show()"
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]
}
],
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