{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Einfaches Beispiel \"Strom und Wärme\"\n",
    "\n",
    "Eine einfache Illustration, in der wir verschiedene Anlagen verbinden:\n",
    "* KWK Anlage\n",
    "* P2H (z.B. Wärmepumpe, Geothermie)\n",
    "* Wärmespeicher\n",
    "\n",
    "... plus (festem) Wärmebedarf und Strompreise\n",
    "\n",
    "Das Wärmenetz besteht aus \"Nord\", \"Süd\" plus Verbindung\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ein paar Vorbereitungen in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import eaopack as eao\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Definition der \"Knoten\", also der grundsätzlichen Struktur\n",
    "\n",
    "Knoten bilden den virtuellen Punkt, in dem Wärme- und Stromanlagen angeschlossen sind. Die Knoten können durch \"Transporte\" verbunden werden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition der phys. Einheiten\n",
    "unit_power      = eao.assets.Unit(volume = 'MWh(el)', flow = 'MW(el)')\n",
    "unit_heat       = eao.assets.Unit(volume = 'MWh(th)', flow = 'MW(th)')\n",
    "# Definition der Knoten\n",
    "node_power      = eao.assets.Node(name = 'strom'      , unit = unit_power)\n",
    "node_heat_nord  = eao.assets.Node(name = 'waerme_nord', unit = unit_heat)\n",
    "node_heat_sued  = eao.assets.Node(name = 'waerme_sued', unit = unit_heat)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Definition der einzelnen Anlagen und Verträge\n",
    "\n",
    "Notiz: Wir vermeiden eine grundsätzliche Unterscheidung zwischen phys. Anlagen und Verträgen. Z.B. \n",
    "* der Wärmebedarf kann auch als zu erfüllender Absatzvertrag gesehen werden\n",
    "* es könnten auch Strom-Terminverträge eingebunden werden (so physisch erfüllt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# (1) Wärmespeicher\n",
    "storage = eao.assets.Storage(name       = 'speicher', \n",
    "                            nodes       = node_heat_nord, \n",
    "                            cap_out     = .5, \n",
    "                            cap_in      = .5, \n",
    "                            size        = 3., \n",
    "                            start_level = 0.25, \n",
    "                            end_level   = 0.25,\n",
    "                            eff_in      = 0.9, \n",
    "                            block_size  = 'd')     # Speicher soll täglich neu optimiert werden (nicht \"leer hinterlassen\")\n",
    "\n",
    "# (2) Power to Heat Anlage (die Wärmepumpe). Hier vereinfacht abgebildet\n",
    "#     Wir können zu einem gegebenen Wirkungsgrad \"Strom in Wärme tauschen\"\n",
    "power2heat   = eao.assets.MultiCommodityContract(name = 'P2H',                           # Wärmepumpe (z.B. Geothermie)\n",
    "                                                 min_cap = 0, \n",
    "                                                 max_cap = 2, \n",
    "                                                 nodes = [node_power, node_heat_nord],   # Lokalisierung: Knoten Nord für Wärme  -  plus Stromknoten\n",
    "                                                 factors_commodities=[-1, 2])            # Wirkundsgrad: 1 MWh Strom zu 2 MWh Wärme\n",
    "\n",
    "# (3) Kraft-Wärme-Kopplung (KWK). Hier als GuD abgebildet\n",
    "#     Achtung - hier sind einige Parameter als Standardwerte gesetzt, die je nach Anwendungsfall angepasst werden sollten\n",
    "KWK    = eao.assets.CHPAsset(name = \"KWK\", \n",
    "                                   min_cap     = 0, \n",
    "                                   max_cap     = 5,                             # max. Leistung Strom\n",
    "                                   nodes       = [node_power, node_heat_sued],  # Erzeugung von Strom und Wärme in die entsprechenden Netze, bei Bedarf auch Gas\n",
    "                                   start_costs = 0,                             # im Beispiel keine Startkosten\n",
    "                                   extra_costs = 25,                            # Brennstoffkosten in €/MWh, natürlich auch als Zeitreihe möglich\n",
    "                                   conversion_factor_power_heat=0.5,            # Verhältnis Produktion Wärme zu Strom 2:1, d.h. 1MW Stromverlust bei 2MW Wärmeproduktion\n",
    "                                   max_share_heat = .5)                         # max. Anteil der Wärme an Gesamtproduktion (in MWh)\n",
    "                                   ### ..viele weitere Parameter möglich, siehe Dokumentation. Z.B. Rampen, Mindestlaufzeiten, etc.\n",
    "\n",
    "# (4) Wärmebedarf\n",
    "#     Der Wärmebedarf muss immer gedeckt werden. Hier als \"Contract\" abgebildet; Maximum = Minimum bedeutet \"keine Flexibilität\"\n",
    "bedarf_nord  = eao.assets.Contract(name = \"bedarf_nord\",  \n",
    "                                   min_cap  = \"waerme_bedarf_nord\", # Referenziert auf Bezeichnung in Datenquelle\n",
    "                                   max_cap  = \"waerme_bedarf_nord\", \n",
    "                                   nodes    = node_heat_nord)\n",
    "bedarf_sued  = eao.assets.Contract(name     = \"bedarf_sued\",\n",
    "                                   min_cap  = \"waerme_bedarf_sued\", \n",
    "                                   max_cap  = \"waerme_bedarf_sued\", \n",
    "                                   nodes = node_heat_sued)\n",
    "# (5) Wärmeübertragungsnetz. Hier als \"Transport\" Asset abgebildet\n",
    "#     Generell wird der Fluss immer in eine Richtung definiert. Daher im Beispiel 2 einzelne Richtungen Süd->Nord und Nord->Süd\n",
    "netz_nord_sued  = eao.assets.Transport(name = 'netz_ns', \n",
    "                                       max_cap = 2,                 # max. Wärmeübertragungskapazität\n",
    "                                       nodes = [node_heat_nord, node_heat_sued])\n",
    "netz_sued_nord  = eao.assets.Transport(name = 'netz_sn', \n",
    "                                       max_cap = 1.5,\n",
    "                                       nodes = [node_heat_sued, node_heat_nord])\n",
    "\n",
    "# (3) Strommarkt\n",
    "#     Wir können praktisch unbegrenzt Strom kaufen und verkaufen - zum vorgegebenen Preis\n",
    "market       = eao.assets.SimpleContract(name = 'strommarkt', price='strompreis', min_cap= -1000, max_cap=1000, nodes = node_power)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Portfolio zusammensetzen\n",
    "Die Anlagen und Verträge bilden die Details ab. Mit der Information zu den Knoten können sie einfach im Portfolio zusammengefasst werden\n",
    "* Szenarioanalyse mit und ohne Anlagen\n",
    "* auch viele Einzel-Portfolien möglich, falls nur Teile modelliert werden\n",
    "* Teil-Portfolien können bei Bedarf als eine Anlage im Gesamtmodell zusammengefasst werden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "portf        = eao.portfolio.Portfolio([storage, \n",
    "                                        power2heat, \n",
    "                                        KWK, \n",
    "                                        market, \n",
    "                                        bedarf_nord, \n",
    "                                        bedarf_sued, \n",
    "                                        netz_nord_sued, \n",
    "                                        netz_sued_nord])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graphische Darstellung\n",
    "Automatisch generiert. Im GUI kann die Darstellung angepasst werden.\n",
    "* Gelb: Knoten\n",
    "* Grau: Assets\n",
    "* Pfeile: Anbindung der Assets an Knoten oder Transport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eao.network_graphs.create_graph(portf = portf, title = 'Beispiel: Wärmenetz mit KWK, Wärmepumpe und Speicher')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Randbemerkung\n",
    "Das EAO ist so aufgesetzt, dass es einfach an eigene Datenbanken und Systeme angebunden werden kann.\n",
    "Z.B. können alle Daten einfach in JSON exportiert (und eingelesen) werden. So auch im GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## in JSON File speichern zur weiteren Verwendung, z.B. in eigener Datenbank, dem GUI, etc. Genauso für Assets, etc.\n",
    "eao.serialization.to_json(portf, 'portf.json')      # Gesamtbeispiel\n",
    "eao.serialization.to_json(storage, 'speicher.json')\n",
    "# ... etc. für alle Assets möglich'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Daten laden\n",
    "Hier: Preise und Wärmebedarf aus Excel Datei laden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strompreis</th>\n",
       "      <th>waerme_bedarf_nord</th>\n",
       "      <th>waerme_bedarf_sued</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datum</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00</th>\n",
       "      <td>41.88</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 01:00:00</th>\n",
       "      <td>38.60</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 02:00:00</th>\n",
       "      <td>36.55</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 03:00:00</th>\n",
       "      <td>32.32</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 04:00:00</th>\n",
       "      <td>30.85</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     strompreis  waerme_bedarf_nord  waerme_bedarf_sued\n",
       "datum                                                                  \n",
       "2020-01-01 00:00:00       41.88               -2.11               -0.31\n",
       "2020-01-01 01:00:00       38.60               -2.17               -0.39\n",
       "2020-01-01 02:00:00       36.55               -2.20               -0.49\n",
       "2020-01-01 03:00:00       32.32               -2.20               -0.59\n",
       "2020-01-01 04:00:00       30.85               -2.17               -0.70"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel(\"2020_sample_daten.xlsx\")\n",
    "data.set_index('datum', inplace=True)\n",
    "data.round(2).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimierung durchführen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Start = dt.date(2020,1,1)\n",
    "End   = dt.date(2020,1,3)\n",
    "tg    = eao.assets.Timegrid(Start, End, freq = 'h')     # hier: stündlich optimieren\n",
    "out   = eao.optimize(portf = portf, timegrid = tg, data = data)\n",
    "### Solver nach Geschmack leicht austauschbar. Z.B. Gurobi, CPLEX, freie Solver: SCIP, HIGHS ...\n",
    "# out = eao.optimize(portf = portf, timegrid = tg, data = data, solver = 'SCIP')\n",
    "### Split problem\n",
    "out   = eao.optimize(portf = portf, timegrid = tg, data = data)\n",
    "# out   = eao.optimize(portf = portf, timegrid = tg, data = data, split_interval_size='MS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Randnotiz \"Performance und Größenbeschränkung\":\n",
    "\n",
    "* MIP und LP: Kann das Portfolio als \"LP\" gelöst werden, ist die Rechenzeit deutlich schneller, als bei einem MIP. Hier kann über den Parameter \"make_soft_problem\" in der Optimierung gesteuert werden\n",
    "* Solver sind nach Geschmack leicht austauschbar. Z.B. Gurobi, CPLEX, freie Solver: SCIP, HIGHS ...\n",
    "* Generell erzeugen Speicher in der Optimierung leicht schwer lösbare Probleme. Hier empfehlen wir, die \"block_size\" z.B. auf täglich/wöchentlich zu setzen. So wird der Speicher nur über innerhalb jeden Tages / jeder Woche optimiert\n",
    "* \"Split Optimization\": Für die meisten Probleme ist es eine gute Näherung, das Problem für jede Woche/ jeden Monat etc. zu lösen. In der entsprechenden Variante setzt EAO die Lösung automatisch zusammen. Randbedingungen wie Speicher Level oder Mindestmengen werden heruntergebrochen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Resultate analysieren\n",
    "Vereinfachte Darstellung. Im Normalfall Export nach Excel oder in eine Schnittstelle in Datenbanken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parameter</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>1267.861623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Values\n",
       "Parameter             \n",
       "status      successful\n",
       "value      1267.861623"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out['summary'] # Zusammenfassung. Gesantwert in € (Deckungsbeitrag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2020-01-01 00:00:00</th>\n",
       "      <th>2020-01-01 01:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>speicher</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P2H</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KWK</th>\n",
       "      <td>-125.00</td>\n",
       "      <td>-125.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strommarkt</th>\n",
       "      <td>164.02</td>\n",
       "      <td>143.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedarf_nord</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedarf_sued</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netz_ns</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netz_sn</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             2020-01-01 00:00:00  2020-01-01 01:00:00\n",
       "speicher                    0.00                 0.00\n",
       "P2H                         0.00                 0.00\n",
       "KWK                      -125.00              -125.00\n",
       "strommarkt                164.02               143.65\n",
       "bedarf_nord                 0.00                 0.00\n",
       "bedarf_sued                 0.00                 0.00\n",
       "netz_ns                     0.00                 0.00\n",
       "netz_sn                     0.00                 0.00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out['DCF'].transpose().iloc[:,0:2].round(2) # Detailsicht auf die Cash Flows (auch diskontierte Optimierung möglich (DCF / Maximierung des NPV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           2020-01-01 00:00:00  2020-01-01 01:00:00\n",
      "speicher (waerme_nord)                    0.25                 0.00\n",
      "P2H (strom)                              -0.73                -0.90\n",
      "P2H (waerme_nord)                         1.46                 1.80\n",
      "KWK (strom)                               4.65                 4.62\n",
      "KWK (waerme_sued)                         0.70                 0.76\n",
      "strommarkt (strom)                       -3.92                -3.72\n",
      "bedarf_nord (waerme_nord)                -2.11                -2.17\n",
      "bedarf_sued (waerme_sued)                -0.31                -0.39\n",
      "netz_ns (waerme_nord)                    -0.60                -0.61\n",
      "netz_ns (waerme_sued)                     0.60                 0.61\n",
      "netz_sn (waerme_sued)                    -0.99                -0.98\n",
      "netz_sn (waerme_nord)                     0.99                 0.98\n"
     ]
    }
   ],
   "source": [
    "# Detailsicht auf die Betriebsweise der Anlagen (in MWh)\n",
    "print(out['dispatch'].transpose().iloc[:,0:2].round(2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2020-01-01 00:00:00</th>\n",
       "      <th>2020-01-01 01:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nodal price: waerme_nord</th>\n",
       "      <td>20.94</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nodal price: strom</th>\n",
       "      <td>41.88</td>\n",
       "      <td>38.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nodal price: waerme_sued</th>\n",
       "      <td>20.94</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input data: strompreis</th>\n",
       "      <td>41.88</td>\n",
       "      <td>38.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input data: waerme_bedarf_nord</th>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input data: waerme_bedarf_sued</th>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                2020-01-01 00:00:00  2020-01-01 01:00:00\n",
       "nodal price: waerme_nord                      20.94                19.30\n",
       "nodal price: strom                            41.88                38.60\n",
       "nodal price: waerme_sued                      20.94                19.30\n",
       "input data: strompreis                        41.88                38.60\n",
       "input data: waerme_bedarf_nord                -2.11                -2.17\n",
       "input data: waerme_bedarf_sued                -0.31                -0.39"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detailsicht auf die EIngangsgrößen (in €/MWh bzw. MW)  [hier vor Allem die vorgegebenen Preise, andere Analysen möglich --> Kosten für die Wärme pro Stunde]\n",
    "out['prices'].transpose().iloc[:,0:2].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Darstellung in Graphiken -- zur Diskussion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.merge(out['dispatch'], out['prices'], left_index=True, right_index=True, how='left')\n",
    "\n",
    "heat = ['P2H (waerme_nord)',\n",
    "        'KWK (waerme_sued)',\n",
    "        'speicher (waerme_nord)']\n",
    "power = ['P2H (strom)',\n",
    "         'KWK (strom)']\n",
    "\n",
    "fig, ax = plt.subplots(1,3, tight_layout = True, figsize=(12,4))\n",
    "df[heat].plot(ax = ax[0], style = '-')\n",
    "df[power].plot(ax = ax[1], style = '-')\n",
    "df['input data: strompreis'].plot(ax = ax[2], style = '-')\n",
    "ax[0].set_title('Wärme')\n",
    "ax[1].set_title('Strom')\n",
    "ax[2].set_title('Strompreis')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "my_env",
   "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": "3.12.9"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}