acud_python_102_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# acud Python session demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are mainly two ways to run an acud simulation:\n",
    "- through the command line, or\n",
    "- from within a Python interpreter session/Python script by importing acud as a Python package\n",
    "\n",
    "This notebook emulates using acud interactively through the Python interpreter to query an acud solution. With minimal edits,\n",
    "the code contained here could also be used as a Python script.\n",
    "\n",
    "Text included in the notebook refers to the cell(s) immediately below. Using the pandas function *head*,\n",
    "only the first rows are shown for time series that are too long to display. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from math import ceil, floor\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy import array as ndarray\n",
    "from pandas import Series, DataFrame, MultiIndex, Index\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.3'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below and then restart the kernel if matplotlib import above fails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import acud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acud.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List all functions and attributes exposed by the acud Python interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir(acud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 102\n",
    "\n",
    "Toy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('102')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On-drive data sourcing for post processing\n",
    "\n",
    "Raw solution results are read from files stored on disk and loaded in memory into an *xarray Dataset* object; further transformations are then applied using this *Dataset* object as output data source.\n",
    "\n",
    "The acud *read_solution* also includes reporting capability, so when using this function there is no need to perform low-level post-processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol = acud.read_solution('output')\n",
    "sol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solution query with a mix of xarray and pandas syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.prices.to_dataframe().unstack(level=0).xs('Bus1', level='bus_id', axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two ways of using *sel* xarray method to run the same query "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFrame(sol.prices.sel(bus_id='Bus1').to_dataframe().prices).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.prices.sel(bus_id='Bus1', drop=('bus_id')).to_dataframe().head(24).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.use.sel(bus_id='Bus1').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.use.sel(bus_id='Bus1').plot();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.dispatch.sum(dim='gen_id').plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.dispatch.sel(gen_id='Gen1').plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.dispatch.to_dataframe().unstack(level=0).plot(legend=None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.dispatch.to_dataframe().unstack(level=0).head(24).plot.bar(legend=None, stacked=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sol.dispatch.to_dataframe().dispatch.unstack(level=0)\n",
    "df.head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.flow.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = sol.prices.sel(bus_id='Bus1').to_dataframe().prices.to_frame()\n",
    "prices.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating units net revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "netrevenue = sol.netrevenues.sum(dim='period').to_dataframe().sort_values(by=['netrevenues'], ascending=True)\n",
    "netrevenue.plot(kind='barh', color='green');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating units generation costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(sol.gencosts.sum(dim='period').to_dataframe()\n",
    " .sort_values(by=['gencosts'], ascending=False)\n",
    " .iloc[0:10].plot(kind='bar'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aggregated generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.dispatch.sum(dim='gen_id').to_dataframe().plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bids by period and generating unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bids = sol.bids.to_dataframe()\n",
    "bids.unstack(level=0).iloc[:,0:9].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Capacity factor by period and generating unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "capfactor = sol.generation / sol.icapacity\n",
    "capfactor.name = 'capacity factor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "capfactor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interactive plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports for interactive plotting\n",
    "\n",
    "import plotly\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "py.init_notebook_mode(connected=True)\n",
    "\n",
    "print plotly.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generation stack for a given day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "genstack = sol.generation.sel(period='2012-01-01').to_dataframe()\n",
    "genstack = (genstack.loc[genstack.generation > 0]\n",
    "            .unstack(level=0)\n",
    "            .generation)\n",
    "\n",
    "data = [go.Bar(\n",
    "    x=genstack.index,\n",
    "    y=genstack[gen],\n",
    "    name=gen\n",
    ")  for gen in genstack.columns]\n",
    "\n",
    "layout = go.Layout(\n",
    "    barmode='stack',\n",
    "    xaxis=dict(\n",
    "        showticklabels=False\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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