359 lines
12 KiB
Plaintext
359 lines
12 KiB
Plaintext
{
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"cells": [
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{
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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": [
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"import pypsa\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import os\n",
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"from pathlib import Path\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.style.use(\"ggplot\")\n",
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"import pycountry\n",
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"import json\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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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": [
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"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",
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" \"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",
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" \"model_5\": \"elec_s_37_ec_lv1.0_Ep_new.nc\",\n",
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"}\n",
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"\n",
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"data_path = Path.cwd() / \"..\" / \"..\"\n",
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"model_path = data_path / available_models[\"model_5\"]\n",
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"\n",
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"with open(data_path / \"generation_data\" / \"generation_mapper_pypsa.json\", \"r\") as f:\n",
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" pypsa_generation_mapper = json.load(f)"
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]
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},
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{
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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": [
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"n = pypsa.Network(str(model_path))"
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]
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},
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{
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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": [
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"def intersection(alist, blist):\n",
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" for val in alist:\n",
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" if val not in blist:\n",
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" alist.remove(val)\n",
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" return alist"
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]
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},
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{
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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": [
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"pypsa_generation_mapper"
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]
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},
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{
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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": [
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"countries = set([col[:2] for col in n.generators_t.p.columns])\n",
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"gen = set([col[6:] for col in n.generators_t.p.columns])\n",
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"\n",
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"for i, country in enumerate(countries):\n",
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" df = pd.DataFrame(index=n.generators_t.p.index)\n",
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" # country_generation = [col for col in n.generators_t.p.columns if col.startswith(country)]\n",
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" country_generation = n.generators.loc[n.generators.bus.str.contains(country)]\n",
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"\n",
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" for key, gens in pypsa_generation_mapper.items():\n",
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" # curr_gen = country_generation.loc[\n",
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" # (country_generation.carrier.str.contains(tech) for tech in gens).astype(bool)].index\n",
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" curr_gen = country_generation.loc[\n",
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" country_generation.carrier.apply(lambda carr: carr in gens)\n",
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" ].index\n",
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"\n",
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" 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",
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" df[key] = np.zeros(len(df))\n",
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"\n",
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" df.to_csv(data_path / \"pypsa_data\" / (country + \".csv\"))"
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]
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},
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{
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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": [
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"import seaborn as sns\n",
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"\n",
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"\n",
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"for num, country in enumerate(os.listdir(data_path / \"pypsa_data\")):\n",
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" # country = \"DE.csv\"\n",
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" cc = country[:2]\n",
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"\n",
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" pypsa_df = pd.read_csv(\n",
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" data_path / \"pypsa_data\" / country, parse_dates=True, index_col=0\n",
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" )\n",
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" try:\n",
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" entsoe_df = pd.read_csv(\n",
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" data_path / \"harmonised_generation_data\" / (\"prepared_\" + country),\n",
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" parse_dates=True,\n",
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" index_col=0,\n",
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" )\n",
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"\n",
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" entsoe_df.columns = [col[:-6] for col in entsoe_df.columns]\n",
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" except FileNotFoundError:\n",
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" continue\n",
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"\n",
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" fig, axs = plt.subplots(3, 3, figsize=(20, 15))\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 = \"d\"\n",
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"\n",
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" num_techs_shown = 6\n",
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"\n",
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" links = n.links.loc[\n",
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" (n.links.bus0.str.contains(cc) + n.links.bus1.str.contains(cc)).astype(bool)\n",
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" ]\n",
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" links = links.loc[links.carrier == \"DC\"].sum(axis=1)\n",
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"\n",
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" from_here = n.links.loc[links.index].bus0.str.contains(cc)\n",
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" to_here = n.links.loc[links.index].bus1.str.contains(cc)\n",
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"\n",
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" pypsa_df[\"Import Export\"] = pd.concat(\n",
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" (n.links_t.p0[from_here.index], n.links_t.p0[to_here.index]), axis=1\n",
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" ).sum(axis=1)\n",
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"\n",
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" # show_techs = pypsa_df.sum().sort_values(ascending=False).iloc[:num_techs_shown].index.tolist()\n",
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" show_techs = (\n",
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" entsoe_df.sum()\n",
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" .sort_values(ascending=False)\n",
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" .iloc[:num_techs_shown]\n",
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" .index.tolist()\n",
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" )\n",
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"\n",
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" entsoe_df[intersection(show_techs, entsoe_df.columns.tolist())].loc[start:end].plot(\n",
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" ax=axs[0, 0]\n",
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" )\n",
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" pypsa_df[show_techs].loc[start:end].plot(ax=axs[0, 1], legend=False)\n",
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"\n",
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" pypsa_load = n.loads_t.p_set\n",
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" pypsa_load = pypsa_load[\n",
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" [col for col in pypsa_load.columns if col.startswith(country[:2])]\n",
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" ].mean(axis=1)\n",
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"\n",
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" pypsa_load.loc[start:end].plot(ax=axs[0, 2])\n",
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"\n",
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" axs[0, 0].set_ylabel(\"ENTSOE Generation\")\n",
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" axs[0, 1].set_ylabel(\"PyPSA Generation\")\n",
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" axs[0, 2].set_ylabel(\"PyPSA Load\")\n",
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"\n",
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" upper_lim = pd.concat((pypsa_df, entsoe_df), axis=0).max().max()\n",
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" for ax in axs[0, :2]:\n",
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" ax.set_ylim(0, upper_lim)\n",
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"\n",
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" for ax in axs[0, :2]:\n",
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" ax.legend()\n",
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"\n",
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" # entsoe_df[[col+\" (MWh)\" for col in pypsa_df.columns]].loc[start:end].plot(ax=axs[0])\n",
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" entsoe_df[intersection(show_techs, entsoe_df.columns.tolist())].resample(\n",
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" coarse_freq\n",
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" ).mean().plot(ax=axs[1, 0])\n",
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" pypsa_df[show_techs].resample(coarse_freq).mean().plot(ax=axs[1, 1], legend=False)\n",
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"\n",
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" pypsa_load = n.loads_t.p_set\n",
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" pypsa_load = pypsa_load[\n",
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" [col for col in pypsa_load.columns if col.startswith(country[:2])]\n",
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" ].mean(axis=1)\n",
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" pypsa_load.resample(coarse_freq).sum().plot(ax=axs[1, 2])\n",
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"\n",
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" # pypsa_df[show_techs].resample(coarse_freq).mean().sum(axis=1).plot(ax=axs[1,2], legend=False)\n",
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"\n",
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" axs[1, 0].set_ylabel(\"ENTSOE Generation\")\n",
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" axs[1, 1].set_ylabel(\"PyPSA Generation\")\n",
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" axs[1, 2].set_ylabel(\"PyPSA Load\")\n",
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"\n",
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" upper_lim = (\n",
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" pd.concat(\n",
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" (\n",
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" pypsa_df.resample(coarse_freq).mean(),\n",
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" entsoe_df.resample(coarse_freq).mean(),\n",
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" ),\n",
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" axis=0,\n",
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" )\n",
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" .max()\n",
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" .max()\n",
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" )\n",
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" for ax in axs[1, :2]:\n",
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" ax.set_ylim(0, upper_lim)\n",
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"\n",
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" for ax in axs[1, :2]:\n",
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" ax.legend()\n",
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"\n",
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" try:\n",
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" entsoe_prices = pd.read_csv(\n",
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" data_path / \"price_data\" / country,\n",
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" index_col=0,\n",
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" parse_dates=True,\n",
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" )\n",
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"\n",
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" def make_tz_time(time):\n",
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" return pd.Timestamp(time).tz_convert(\"utc\")\n",
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"\n",
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" entsoe_prices.index = pd.Series(entsoe_prices.index).apply(\n",
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" lambda time: make_tz_time(time)\n",
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" )\n",
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" entsoe_prices.resample(\"3d\").mean().plot(ax=axs[2, 0])\n",
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"\n",
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" except FileNotFoundError:\n",
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" pass\n",
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"\n",
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" prices_col = [\n",
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" col for col in n.buses_t.marginal_price.columns if col.startswith(country[:2])\n",
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" ]\n",
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" pypsa_prices = n.buses_t.marginal_price[prices_col].mean(axis=1)\n",
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" pypsa_prices.resample(\"3d\").mean().plot(ax=axs[2, 1])\n",
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"\n",
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" upper_lim = pd.concat((entsoe_prices, pypsa_prices), axis=0).max().max()\n",
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" for ax in axs[2, :2]:\n",
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" ax.set_ylim(0, upper_lim)\n",
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"\n",
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" axs[2, 0].set_ylabel(\"ENTSOE Day Ahead Prices\")\n",
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" axs[2, 1].set_ylabel(\"PyPSA Shadow Prices\")\n",
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"\n",
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" pypsa_totals = pypsa_df.sum()\n",
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"\n",
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" entsoe_totals = entsoe_df.sum()\n",
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" totals = pd.DataFrame(index=pypsa_totals.index)\n",
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"\n",
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" totals[\"pypsa\"] = pypsa_totals\n",
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" totals[\"entsoe\"] = entsoe_totals\n",
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" entsoe_totals.loc[\"Import Export\"] = 0.0\n",
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" totals[\"tech\"] = totals.index\n",
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"\n",
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" totals = pd.concat(\n",
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" [\n",
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" pd.DataFrame(\n",
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" {\n",
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" \"kind\": [\"pypsa\" for _ in range(len(pypsa_totals))],\n",
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" \"tech\": pypsa_totals.index,\n",
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" \"total generation\": pypsa_totals.values,\n",
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" }\n",
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" ),\n",
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" pd.DataFrame(\n",
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" {\n",
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" \"kind\": [\"entsoe\" for _ in range(len(entsoe_totals))],\n",
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" \"tech\": entsoe_totals.index,\n",
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" \"total generation\": entsoe_totals.values,\n",
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" }\n",
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" ),\n",
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" ],\n",
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" axis=0,\n",
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" )\n",
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"\n",
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" sns.barplot(\n",
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" data=totals,\n",
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" x=\"tech\",\n",
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" y=\"total generation\",\n",
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" hue=\"kind\",\n",
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" ax=axs[2, 2],\n",
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" palette=\"dark\",\n",
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" alpha=0.6,\n",
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" edgecolor=\"k\",\n",
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" )\n",
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" axs[2, 2].set_ylabel(\"Total Generation\")\n",
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" axs[2, 2].set_xticks(\n",
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" axs[2, 2].get_xticks(), axs[2, 2].get_xticklabels(), rotation=45, ha=\"right\"\n",
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" )\n",
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"\n",
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" plt.tight_layout()\n",
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" plt.show()\n",
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"\n",
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" if num == 7:\n",
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" break\n",
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"\n",
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"\n",
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"# df.to_csv(data_path / \"pypsa_data\" / (country+\".csv\"))"
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]
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},
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{
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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": [
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"links = n.links.loc[\n",
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" (n.links.bus0.str.contains(\"DE\") + n.links.bus1.str.contains(\"DE\")).astype(bool)\n",
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"]\n",
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"links = links.loc[links.carrier == \"DC\"].sum(axis=1)\n",
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"n.links_t.p0[links.index.tolist()].resample(\"w\").sum().plot()"
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]
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},
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{
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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": [
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"german_origin = n.links.loc[links.index].bus0.str.contains(\"DE\")\n",
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"german_destin = n.links.loc[links.index].bus1.str.contains(\"DE\")\n",
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"\n",
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"net_impexp = pd.concat(\n",
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" (n.links_t.p0[german_origin.index], n.links_t.p0[german_destin.index]), axis=1\n",
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").sum(axis=1)\n",
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"\n",
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"net_impexp.iloc[:200].plot()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "",
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"language": "python",
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"name": ""
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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