Tutorial 3 -  ii) Power requirements from DFT analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3 -  ii) Power requirements from DFT analysis\n",
    "## Step 1: Import FANFARE source code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "submodule \"power_class\" imported\n",
      "fanfare submodule \"aux\" imported\n",
      "fanfare module import complete\n"
     ]
    }
   ],
   "source": [
    "import source as sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Load data\n",
    "Here we load a dataset downloaded from Energidataservice.dk (see Tutorial 0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading previously stored dataset\n",
      "Could not estimate residual load, one of the following columns were missing: \n",
      "GrossCon, WindPowerProd, SolarPowerProd\n",
      "(if data_type = \"energinet\", this is fine, ResLoad_DK, ResLoad_DK1, ResLoad_DK2 should be there)\n"
     ]
    }
   ],
   "source": [
    "data_ob = sc.PowerData(load=True,file_name='energinet_all',year='2018')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Decide on frequency setup\n",
    "Determine the frequency intervals in terms of timescales in hours, and write an equal number of labels to go with them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_cuts = [[0.25,5],[5,24],[24,7*24],[7*24,3*30*24],[3*30*24,1e6]] # hours\n",
    "labels = ['< 5 hrs',\\\n",
    "          '5 - 24 hrs',\\\n",
    "          '24 hrs - 1 week',\\\n",
    "          '1 week - 3 months',\\\n",
    "          '> 3 months']\n",
    "labels_verbose = ['Hourly','Intra-daily','Daily','Weekly','Seasonally']\n",
    "data_ob.SetFrequencySetup(duration_cuts=duration_cuts,labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Derive power requirements and plot as box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_req = data_ob.GetPowerReqFromiFFT(col_names=['ResLoad_DK'],verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_ob.PlotPowerReqFromiFFT(width=0.13,\\\n",
    "        matrices=[power_req],\\\n",
    "        col_names=['ResLoad_DK'],colors=['k'],\\\n",
    "        ls=['-'],\\\n",
    "        labels=['DK in 2018, real data'],\\\n",
    "        xlabels=labels_verbose,ylab='Residual Load, $L^{res}$ [MW]',\\\n",
    "        fig_name='power_req_DK_2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}