{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 14.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fundamentals of Solar Cells and Photovoltaic Systems Engineering**\n",
    "\n",
    "**Solutions Manual**\n",
    "\n",
    "**Problem 14.3**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Select one country and one city within the country. Plot the duration curve of capacity factor (CF) time series for solar PV and wind power in both cases and discuss the results. You can download solar PV and wind time series from https://model.energy/.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we downloaded solar PV and wind power time series for Spain and Madrid from https://model.energy/ and saved it in the data folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the packages [pandas](https://pandas.org/) and [matplotlib.pyplot](https://matplotlib.org/stable/index.html) to plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as  plt\n",
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we retrieve the time series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>solar</th>\n",
       "      <th>onwind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     solar  onwind\n",
       "time                              \n",
       "2011-01-01 00:00:00    0.0   0.003\n",
       "2011-01-01 01:00:00    0.0   0.000\n",
       "2011-01-01 02:00:00    0.0   0.000\n",
       "2011-01-01 03:00:00    0.0   0.001\n",
       "2011-01-01 04:00:00    0.0   0.001"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CF_city=pd.read_csv('data/time-series-MADRID.csv',\n",
    "                  sep=',', index_col=0)\n",
    "\n",
    "CF_country=pd.read_csv('data/time-series-SPAIN.csv',\n",
    "                  sep=',', index_col=0)\n",
    "CF_city.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by plotting the duration curve for solar PV capacity factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CF solar PV')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "ax0 = plt.subplot(gs[0,0])\n",
    "ax0.plot(CF_city['solar'].sort_values(ascending=False).values, \n",
    "             color='orange',\n",
    "             linewidth=2,\n",
    "             label='Madrid')\n",
    "\n",
    "ax0.plot(CF_country['solar'].sort_values(ascending=False).values, \n",
    "             color='orange',\n",
    "             linewidth=2,\n",
    "             linestyle='--',\n",
    "             label='Spain')\n",
    "ax0.legend(fancybox=False, fontsize=12, loc='best', facecolor='white', ncol=1, frameon=True)\n",
    "ax0.set_xlabel('hours')\n",
    "ax0.set_ylabel('CF solar PV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot now the duration curve for onshore wind capacity factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CF onshore wind')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "ax0 = plt.subplot(gs[0,0])\n",
    "ax0.plot(CF_city['onwind'].sort_values(ascending=False).values, \n",
    "             color='blue',\n",
    "             linewidth=2,\n",
    "             label='Madrid')\n",
    "\n",
    "ax0.plot(CF_country['onwind'].sort_values(ascending=False).values, \n",
    "             color='blue',\n",
    "             linewidth=2,\n",
    "             linestyle='--',\n",
    "             label='Spain')\n",
    "ax0.legend(fancybox=False, fontsize=12, loc='best', facecolor='white', ncol=1, frameon=True)\n",
    "ax0.set_xlabel('hours')\n",
    "ax0.set_ylabel('CF onshore wind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The wind power duration curve for the country shows less extreme maximum and minimum values since wind power gets smoothed by regional integration. This strategy is more effective for wind than for solar due to the shorter correlation length of wind. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}