{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Time-varying bid problems with [PowerSimulations.jl](https://github.com/NREL-SIIP/PowerSimulations.jl)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Originally Contributed by**: Sourabh Dalvi"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Introduction"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "PowerSimulations.jl supports the construction of Operations problems in power system\n",
    "with three part cost bids for each time step. MarketBidCost allows the user to pass a\n",
    "time-series of variable cost for energy and ancillary services jointly.\n",
    "This example shows how to build a Operations problem with MarketBidCost and how to add\n",
    "the time-series data to the devices."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Dependencies\n",
    "### Modeling Packages"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using SIIPExamples\n",
    "using PowerSystems\n",
    "using PowerSimulations\n",
    "const PSI = PowerSimulations"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data management packages"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using PowerSystemCaseBuilder\n",
    "using Dates\n",
    "using DataFrames\n",
    "using TimeSeries"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Optimization packages"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using HiGHS #solver"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data\n",
    "Create a `System` from RTS-GMLC data"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "sys = build_system(PSITestSystems, \"modified_RTS_GMLC_DA_sys\")"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Creating the Time Series data for Energy bid"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "MultiDay = collect(\n",
    "    DateTime(\"2020-01-01T00:00:00\"):Hour(1):(DateTime(\"2020-01-01T00:00:00\") + Hour(8783)),\n",
    ");"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Adding a MarketBidCost time series\n",
    "Here we add the energy bid\n",
    "time series to the system. The TimeSeriesData that holds the energy bid data can be of any\n",
    "type (i.e. `SingleTimeSeries` or `Deterministic`), but it has to be consistent with the existing\n",
    "data in the `sys`. So, we'll first remove the existing `DeterministicSingleTimeSeries`, then add\n",
    "the bid time series as `SingleTimeSeries`, then re-transform all of the time series in `sys`."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "remove_time_series!(sys, DeterministicSingleTimeSeries)\n",
    "\n",
    "for gen in get_components(ThermalGen, sys)\n",
    "    varcost = get_operation_cost(gen)\n",
    "    data = TimeArray(MultiDay, repeat([get_cost(get_variable(varcost))], 8784))\n",
    "    _time_series = SingleTimeSeries(\"variable_cost\", data)\n",
    "    add_time_series!(sys, gen, _time_series)\n",
    "    #set_variable_cost!(sys, gen, _time_series)\n",
    "end"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Transforming SingleTimeSeries into Deterministic"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "transform_single_time_series!(sys, 24, Dates.Hour(24))"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "In the [OperationsProblem example](https://nbviewer.jupyter.org/github/NREL-SIIP/SIIPExamples.jl/blob/master/notebook/3_PowerSimulations_examples/01_operations_problems.ipynb)\n",
    "we defined a unit-commitment problem with a copper plate representation of the network.\n",
    "Here, we want do define unit-commitment problem  with ThermalMultiStartUnitCommitment\n",
    "formulation for thermal device representation."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "For now, let's just choose a standard UC formulation."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "uc_template = template_unit_commitment()"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "And adjust the thermal generator formulation to use `ThermalMultiStartUnitCommitment`"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "set_device_model!(uc_template, ThermalMultiStart, ThermalMultiStartUnitCommitment)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now we can build a 4-hour economic dispatch problem with the RTS data."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "solver = optimizer_with_attributes(HiGHS.Optimizer, \"mip_rel_gap\" => 0.5)\n",
    "\n",
    "problem = DecisionModel(uc_template, sys, horizon = 4, optimizer = solver)\n",
    "build!(problem, output_dir = mktempdir())"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "And solve it ..."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "solve!(problem)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "---\n",
    "\n",
    "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*"
   ],
   "metadata": {}
  }
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