Add example for pv-household in ipython notebook

src/oemof/tabular/examples/scripting/pv_storage_household.ipynb

@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PV + Storage modelling for household "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pkg_resources as pkg\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "from oemof.solph import EnergySystem, Model, Bus\n",
+    "import oemof.tabular.tools.postprocessing as pp\n",
+    "import oemof.tabular.facades as fc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# datapath for input data from the oemof tabular pacakge\n",
+    "datapath = pkg.resource_filename(\"oemof.tabular\", \"examples/data/data.xls\")\n",
+    "\n",
+    "# results path for output\n",
+    "results_path = os.path.join(\n",
+    "    os.path.expanduser(\"~\"), \"oemof-results\", \"pv-storage-household\", \"output\"\n",
+    ")\n",
+    "\n",
+    "if not os.path.exists(results_path):\n",
+    "    os.makedirs(results_path)\n",
+    "\n",
+    "timeseries = pd.read_excel(\n",
+    "    datapath, sheet_name=\"timeseries\", index_col=[0], parse_dates=True\n",
+    ")\n",
+    "timeseries.index.freq = \"1H\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Energy System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "es = EnergySystem(timeindex=timeseries.index)\n",
+    "\n",
+    "bus = Bus(label=\"household\")\n",
+    "es.add(bus)\n",
+    "\n",
+    "es.add(\n",
+    "    fc.Volatile(\n",
+    "        label=\"pv\",\n",
+    "        carrier=\"solar\",\n",
+    "        tech=\"pv\",\n",
+    "        capacity=10,\n",
+    "        bus=bus,\n",
+    "        profile=timeseries[\"pv\"],\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "es.add(\n",
+    "    fc.Storage(\n",
+    "        label=\"storage\",\n",
+    "        bus=bus,\n",
+    "        carrier=\"lithium\",\n",
+    "        tech=\"battery\",\n",
+    "        capacity=3,\n",
+    "        storage_capacity=12,\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "es.add(\n",
+    "    fc.Shortage(\n",
+    "        label=\"grid_buy\",\n",
+    "        bus=bus,\n",
+    "        carrier=\"electricity\",\n",
+    "        tech=\"grid\",\n",
+    "        capacity=100,\n",
+    "        marginal_cost=0.3,\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "es.add(\n",
+    "    fc.Excess(\n",
+    "        label=\"grid_sell\",\n",
+    "        bus=bus,\n",
+    "        carrier=\"electricity\",\n",
+    "        tech=\"grid\",\n",
+    "        marginal_cost=-0.1,\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "es.add(fc.Load(label=\"load\", bus=bus, amount=20e3, profile=timeseries[\"load\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create model and solve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the model using the energy system with its components (see: es.nodes)\n",
+    "m = Model(es)\n",
+    "\n",
+    "# solve model using cbc solver\n",
+    "m.solve(\"cbc\")\n",
+    "\n",
+    "# write back results\n",
+    "m.results = m.results()\n",
+    "\n",
+    "supply = pp.supply_results(bus=[\"household\"], es=es, results=m.results)\n",
+    "demand = pp.demand_results(bus=[\"household\"], es=es, results=m.results)\n",
+    "# pd.concat([supply, demand], axis=1).to_csv('results.csv')\n",
+    "\n",
+    "pp.write_results(m, results_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "s=1000\n",
+    "e=1048\n",
+    "\n",
+    "fig = plt.figure(figsize=(20,10))\n",
+    "ax = plt.subplot(111)\n",
+    "\n",
+    "supply.iloc[s:e].plot(ax=ax)\n",
+    "demand.iloc[s:e].plot(ax=ax)\n",
+    "\n",
+    "ax.legend(loc='upper right')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}