UnitCommitment(single_period).ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNeCMAq569svbnw6PFfqRda",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/salvapineda/notebooks/blob/main/UnitCommitment(single_period).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIEfHxwno5u5"
      },
      "source": [
        "# Unit Commitment (single period)\n",
        "\n",
        "This Jupyter Notebook solves an instance of the Unit Commitment Problem for a single period described in XXX"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1vOQpFioj3O"
      },
      "source": [
        "## Requirements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHOKuwI6UisB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64094514-0599-4faf-af4e-1189718c9d42"
      },
      "source": [
        "# PYOMO\n",
        "!pip install pyomo \n",
        "import pyomo.environ as pe\n",
        "# NEOS\n",
        "import os\n",
        "os.environ['NEOS_EMAIL'] = 'xxx@gmail.com'\n",
        "# OTHER\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyomo\n",
            "  Downloading Pyomo-6.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.0/11.0 MB\u001b[0m \u001b[31m49.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ply\n",
            "  Downloading ply-3.11-py2.py3-none-any.whl (49 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 KB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ply, pyomo\n",
            "Successfully installed ply-3.11 pyomo-6.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o4jSX0peop7j"
      },
      "source": [
        "## Input Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLj2JfYhTWyT"
      },
      "source": [
        "gen = pd.DataFrame({\n",
        "       'unit': [1,   2], \n",
        "       'cost': [10,  20], \n",
        "       'pmin': [60,  20], \n",
        "       'pmax': [100, 100]})\n",
        "dem = 50\n",
        "ngen = len(gen)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HS_EfbXuowM9"
      },
      "source": [
        "## Solving the Unit Commitment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdyhOqpGWMfC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1848477-df49-4640-85e8-a61a7b0028f0"
      },
      "source": [
        "# Model\n",
        "m = pe.ConcreteModel()\n",
        "# Sets\n",
        "m.g = pe.Set(initialize=list(range(ngen)))\n",
        "# Variables\n",
        "m.p = pe.Var(m.g,within=pe.NonNegativeReals)\n",
        "m.u = pe.Var(m.g,within=pe.Binary)\n",
        "def obj_rule(m):\n",
        "  return sum(gen.loc[g,'cost']*m.p[g] for g in m.g)\n",
        "m.obj = pe.Objective(rule=obj_rule)\n",
        "# Energy balance\n",
        "def bal_rule(m):\n",
        "  return sum(m.p[g] for g in m.g) == dem\n",
        "m.bal = pe.Constraint(rule=bal_rule)\n",
        "# Minimum generation\n",
        "def min_gen_rule(m,g):\n",
        "  return m.p[g] >= m.u[g]*gen.loc[g,'pmin']\n",
        "m.min_gen = pe.Constraint(m.g, rule=min_gen_rule)\n",
        "# Maximum generation\n",
        "def max_gen_rule(m,g):\n",
        "  return m.p[g] <= m.u[g]*gen.loc[g,'pmax']\n",
        "m.max_gen = pe.Constraint(m.g, rule=max_gen_rule)\n",
        "# We solve the optimization problem using GLPK\n",
        "pe.SolverManagerFactory('neos').solve(m,opt='bonmin').write()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# ==========================================================\n",
            "# = Solver Results                                         =\n",
            "# ==========================================================\n",
            "# ----------------------------------------------------------\n",
            "#   Problem Information\n",
            "# ----------------------------------------------------------\n",
            "Problem: \n",
            "- Lower bound: -inf\n",
            "  Upper bound: inf\n",
            "  Number of objectives: 1\n",
            "  Number of constraints: 0\n",
            "  Number of variables: 4\n",
            "  Sense: unknown\n",
            "# ----------------------------------------------------------\n",
            "#   Solver Information\n",
            "# ----------------------------------------------------------\n",
            "Solver: \n",
            "- Status: ok\n",
            "  Message: bonmin\\x3a Optimal\n",
            "  Termination condition: optimal\n",
            "  Id: 3\n",
            "# ----------------------------------------------------------\n",
            "#   Solution Information\n",
            "# ----------------------------------------------------------\n",
            "Solution: \n",
            "- number of solutions: 0\n",
            "  number of solutions displayed: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zEAqcOEo0NV"
      },
      "source": [
        "## Solution Output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSp-Sn1AmVxd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b999b5b5-2980-4827-d096-da9cf309188e"
      },
      "source": [
        "# Print results\n",
        "print('obj =',m.obj())\n",
        "print('p1 =',m.p[0].value)\n",
        "print('p2 =',m.p[1].value)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "obj = 999.9999999044458\n",
            "p1 = 9.555419965740217e-09\n",
            "p2 = 49.99999999044458\n"
          ]
        }
      ]
    }
  ]
}