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Python notebooks Description Python notebooks available in the GitHub repository python-notebooks.
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unitcommitment_indicators.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **Solving an electricity generation problem using indicator constraints**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***unitcommitment_indicators.ipynb***\n",
"\n",
"This example shows how to model and solve an electricity generation problem typically found in power markets (see [Garver (1963)](https://ieeexplore.ieee.org/document/4501405)), showcasing the use of indicator constraints to model change state constraints when generators are turned on/off.\n",
"\n",
"© Copyright 2025 Fair Isaac Corporation\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.\n",
" \n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n",
"\n",
"This example uses FICO® Xpress software. By running it, you agree to the Community License terms of the [Xpress Shrinkwrap License Agreement](https://community.fico.com/s/contentdocument/06980000002h0i5AAA) with respect to the FICO® Xpress software. See the [licensing options](https://www.fico.com/en/fico-xpress-trial-and-licensing-options) overview for additional details and information about obtaining a paid license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install the xpress package\n",
"%pip install -q xpress"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Four types of power generators are available to meet daily electricity demand and a security reserve of at least 20% above the estimated demand. Each type of generator has a set minimum and maximum power output.\n",
"A generator can only be started or stopped at the beginning of a time period. The objective is to determine which generators should be used, and at which power level, in each period so that total daily cost is minimized.\n",
"\n",
"The length and anticipated daily electricity demand for each of the seven planning periods of uneven length are as follows:\n",
"\n",
"| Time Period | Length (hours) | Demand (MW) |\n",
"| --- | --- | --- |\n",
"| 00h to 06h | 6 | 12000 |\n",
"| 06h to 09h | 3 | 32000 |\n",
"| 09h to 12h | 3 | 25000 |\n",
"| 12h to 14h | 2 | 36000 |\n",
"| 14h to 18h | 4 | 25000 |\n",
"| 18h to 22h | 4 | 30000 |\n",
"| 22h to 00h | 2 | 18000 |\n",
"\n",
"The characteristics of power generators are described in the table below, per generator type:\n",
"\n",
"| Type | No. units | Min. power | Max. power | Start-up cost | Hourly cost at min. output | Hourly cost per MW above min. output|\n",
"| --- | --- | --- | --- | --- | --- | --- |\n",
"| A | 10 | 750 | 1750 | 5000 | 2250 | 2.7 |\n",
"| B | 4 | 1000 | 1500 | 1600 | 1800 | 2.2 |\n",
"| C | 8 | 1200 | 2000 | 2400 | 3750 | 1.8 |\n",
"| D | 3 | 1800 | 3500 | 1200 | 4800 | 3.8 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model formulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Three arrays of binary variables are required to determine when to 'start' and 'stop' generators, and decide which ones are set to 'work' in each time period. Another set of real variables are introduced to represent the additonal energy production ('padd') of each working unit type above its minimum output level. Variable 'start' is the difference between 'work' in a time period and 'work' in any preceding period, and 'stop' is the difference between 'work' in any previous time period and the current one.\n",
"\n",
"This unit commitment problem can be mathematically formulated as follows:\n",
"$$\\min \\sum_{u \\in UNITS} \\sum_{t \\in PERIODS} COST_{S_u}^{\\rm start} \\cdot start_{u,t} + LEN_t \\cdot (COST_{S_u}^{\\rm min} \\cdot work_{u,t} + COST_{S_u}^{\\rm add} \\cdot padd_{u,t}) \n",
"+ \\sum_{u \\in UNITS} \\sum_{t \\in PERIODS} stop_{u,t} \\cdot PEN $$\n",
"\n",
"Subject to:\n",
"\n",
"$$\n",
"\\begin{array}{llll}\n",
"& \\hbox{If generator starts in period:} \\\\\n",
"& \\qquad start_{u,t} \\geq work_{u,t} - work_{u,n} , \\qquad \\forall u \\in UNITS, \\forall t \\in PERIODS, n = (NT+t-1) \\hbox{ \\% } NT \\\\\n",
"& \\qquad start_{u,t} \\leq work_{u,t} , \\qquad \\forall u \\in UNITS, \\forall t \\in PERIODS \\\\\n",
"& \\hbox{If generator stops in period:} \\\\\n",
"& \\qquad stop_{u,t} \\geq work_{u,n} - work_{u,t} , \\qquad \\forall u \\in UNITS, \\forall t \\in PERIODS, n = (NT+t-1) \\hbox{ \\% } NT \\\\\n",
"& \\qquad stop_{u,t} \\leq 1 - work_{u,t}, \\qquad \\forall u \\in UNITS, \\forall t \\in PERIODS \\\\\n",
"& \\hbox{Limit on power production above minimum level:} \\\\\n",
"& \\qquad padd_{u,t}\\leq (P_{S_u}^{\\rm max}-P_{S_u}^{\\rm min}) \\cdot work_{u,t}, \\qquad \\forall u \\in UNITS, \\forall t \\in PERIODS \\\\\n",
"& \\hbox{Satisfy daily electricity demand:} \\\\\n",
"& \\qquad \\sum_{u \\in UNITS} P_{S_u}^{\\rm min} \\cdot work_{u,t} + padd_{u,t} \\geq D_t, \\qquad \\forall t \\in PERIODS \\\\\n",
"& \\hbox{Ensure security reserve of 20\\%:} \\\\\n",
"& \\qquad \\sum_{u \\in UNITS} P_{S_u}^{\\rm max} \\cdot work_{u,t} \\geq 1.20 \\cdot D_t, \\qquad \\forall t \\in PERIODS \\\\\n",
"\\end{array}\n",
"$$\n",
"\n",
"Where:\n",
"* $NT$ = number of time periods ($NT = |PERIODS|$)\n",
"* $PERIODS$ = set of time periods ($t \\in PERIODS$)\n",
"* $TYPES$ = set of generator types ($s \\in TYPES$)\n",
"* $UNITS$ = set of unit generators to be committed ($u \\in UNITS$)\n",
"* $AVAIL_s$ = available number of type $s$ generators ($|UNITS| = \\sum_{s \\in TYPES} AVAIL_s$)\n",
"* $LEN_t$ = length of time period $t$\n",
"* $DEM_t$ = demand in time period $t$\n",
"* $TYPE_u$ = type of generator $u$\n",
"* $COST_{s}^{\\rm start}$ = start-up cost of generator type $s$\n",
"* $COST_{s}^{\\rm min}$ = hourly cost of operating generator $s$ at minimum output\n",
"* $COST_{s}^{\\rm add}$ = cost/hour/MW of prod. above min. level\n",
"* $POW_{s}^{\\rm min}$,$POW_{s}^{\\rm max}$ = minimum and maximum power output of a generator type $s$\n",
"* $PEN$ = penalty to ensure that stop variables are 0 when not needed\n",
"\n",
"The decision variables are:\n",
"* $start_{u,t}$ = 1 if generator $u$ is started in period $t$, 0 otherwise\n",
"* $stop_{u,t}$ = 1 if generator $u$ is stopped in period $t$, 0 otherwise\n",
"* $work_{u,t}$ = 1 if generator $u$ is working during period $t$, 0 otherwise\n",
"* $padd_{u,t}$ = production level (MW) of generator $u$ above the minimum level $P_{S_u}^{\\rm min}$ in period $t$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation of the basic model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code cell below demonstrates the implementation of the above model formulation and prints the results using the Xpress Python interface."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import xpress as xp\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Time periods\n",
"LEN = [6, 3, 3, 2, 4, 4, 2]\n",
"DEM = [12000, 32000, 25000, 36000, 25000, 30000, 18000]\n",
"\n",
"# Power plants\n",
"PMIN = [750, 1000, 1200, 1800] # minimum output (MW) per generator type\n",
"PMAX = [1750, 1500, 2000, 3500] # maximum output (MW) per generator type\n",
"CSTART = [5000, 1600, 2400, 1200] # start-up cost per generator type\n",
"CMIN = [2250, 1800, 3750, 4800] # hourly cost of operating generator type at minimum output (see PMIN)\n",
"CADD = [2.7, 2.2, 1.8, 3.8] # cost/hour/MW of prod. above min. level per generator type\n",
"AVAIL = [10, 4, 8, 3] # number of units per type\n",
"\n",
"NT = 7 # number of time periods\n",
"PERIODS = range(NT) # set of time periods\n",
"TYPES = range(4) # power generator types\n",
"UNITS = range(sum(AVAIL)) # power generation units\n",
"TYPE = [i for i in TYPES for p in range(AVAIL[i])] # associating units with types\n",
"PEN = 0.1 # penalty associated with stopping units\n",
"\n",
"# Create problem\n",
"p = xp.problem(\"Unit commitment\")\n",
"\n",
"# Create decision variables\n",
"start = p.addVariables(UNITS, PERIODS, vartype=xp.binary, name='start')\n",
"stop = p.addVariables(UNITS, PERIODS, vartype=xp.binary, name='stop')\n",
"work = p.addVariables(UNITS, PERIODS, vartype=xp.binary, name='work')\n",
"padd = p.addVariables(UNITS, PERIODS, name='padd')\n",
"\n",
"# If generator starts in period\n",
"p.addConstraint(start[u,t] >= work[u,t] - work[u,(NT+t-1) % NT] for u in UNITS for t in PERIODS)\n",
"p.addConstraint(start[u,t] <= work[u,t] for u in UNITS for t in PERIODS)\n",
"\n",
"# If generator stops before period\n",
"p.addConstraint(stop[u,t] >= work[u,(NT+t-1) % NT] - work[u,t] for u in UNITS for t in PERIODS)\n",
"p.addConstraint(stop[u,t] <= 1 - work[u,t] for u in UNITS for t in PERIODS)\n",
"\n",
"# Limit on power production above minimum level\n",
"p.addConstraint(padd[u,t] <= (PMAX[TYPE[u]]-PMIN[TYPE[u]]) * work[u,t] for u in UNITS for t in PERIODS)\n",
"\n",
"# Satisfy demands\n",
"p.addConstraint(xp.Sum(PMIN[TYPE[u]]*work[u,t] + padd[u,t] for u in UNITS) >= DEM[t] for t in PERIODS)\n",
"\n",
"# Security reserve of 20%\n",
"p.addConstraint(xp.Sum(PMAX[TYPE[u]]*work[u,t] for u in UNITS) >= 1.20 * DEM[t] for t in PERIODS)\n",
"\n",
"# Create and add the oObjective function of the problem (hint: compute 'daily cost' and 'penalty' separately)\n",
"Cost = xp.Sum(CSTART[TYPE[u]] * start[u,t] +\n",
" LEN[t] * (CMIN[TYPE[u]] * work[u,t] + CADD[TYPE[u]] * padd[u,t]) for u in UNITS for t in PERIODS)\n",
"\n",
"Penalty = PEN * xp.Sum(stop[u,t] for u in UNITS for t in PERIODS)\n",
"p.setObjective(Cost + Penalty)\n",
"\n",
"# Optimize the problem and print the daily cost, penalty and total objective value\n",
"p.optimize()\n",
"print(\"Daily cost:\", round(p.getSolution(Cost),2))\n",
"print(\"Penalty:\", round(p.getSolution(Penalty),2))\n",
"print(\"Objective value:\", round(p.attributes.objval,2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we define a function to print the solution in a user-friendly way."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time period 0- 6 6- 9 9-12 12-14 14-18 18-22 22-24\n",
" \n",
"Unit 1 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 750 750 750 750 750 750\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 2 Working off off off on off off off\n",
" Status change - - - start stop - -\n",
" Total output 0 0 0 750 0 0 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 3 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 750 750 1750 750 750 750\n",
" of which add. 0 0 0 1000 0 0 0\n",
" \n",
"Unit 4 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 1750 750 1750 750 1100 750\n",
" of which add. 0 1000 0 1000 0 350 0\n",
" \n",
"Unit 5 Working off off off on off off off\n",
" Status change - - - start stop - -\n",
" Total output 0 0 0 1100 0 0 0\n",
" of which add. 0 0 0 350 0 0 0\n",
" \n",
"Unit 6 Working off off off on off off off\n",
" Status change - - - start stop - -\n",
" Total output 0 0 0 750 0 0 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 7 Working off on on on off off off\n",
" Status change - start - - stop - -\n",
" Total output 0 1350 750 1750 0 0 0\n",
" of which add. 0 600 0 1000 0 0 0\n",
" \n",
"Unit 8 Working off off off off off off off\n",
" Status change - - - - - - -\n",
" Total output 0 0 0 0 0 0 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 9 Working off off off off off off off\n",
" Status change - - - - - - -\n",
" Total output 0 0 0 0 0 0 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 10 Working off off off off off off off\n",
" Status change - - - - - - -\n",
" Total output 0 0 0 0 0 0 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 11 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1500 1500 1200 1500 1500 1500 1500\n",
" of which add. 500 500 200 500 500 500 500\n",
" \n",
"Unit 12 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1500 1500 1000 1500 1450 1500 1500\n",
" of which add. 500 500 0 500 450 500 500\n",
" \n",
"Unit 13 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1500 1500 1000 1500 1000 1500 1500\n",
" of which add. 500 500 0 500 0 500 500\n",
" \n",
"Unit 14 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1250 1500 1000 1500 1000 1500 1450\n",
" of which add. 250 500 0 500 0 500 450\n",
" \n",
"Unit 15 Working off on on on on on off\n",
" Status change - start - - - - stop\n",
" Total output 0 2000 2000 2000 2000 2000 0\n",
" of which add. 0 800 800 800 800 800 0\n",
" \n",
"Unit 16 Working off on on on on on off\n",
" Status change - start - - - - stop\n",
" Total output 0 2000 2000 2000 2000 2000 0\n",
" of which add. 0 800 800 800 800 800 0\n",
" \n",
"Unit 17 Working off on on on on on off\n",
" Status change - start - - - - stop\n",
" Total output 0 2000 2000 2000 2000 2000 0\n",
" of which add. 0 800 800 800 800 800 0\n",
" \n",
"Unit 18 Working off on on on on on off\n",
" Status change - start - - - - stop\n",
" Total output 0 2000 2000 2000 2000 2000 0\n",
" of which add. 0 800 800 800 800 800 0\n",
" \n",
"Unit 19 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 2000 2000 2000 2000 2000 2000 2000\n",
" of which add. 800 800 800 800 800 800 800\n",
" \n",
"Unit 20 Working off on on on on on on\n",
" Status change stop start - - - - -\n",
" Total output 0 2000 2000 2000 2000 2000 2000\n",
" of which add. 0 800 800 800 800 800 800\n",
" \n",
"Unit 21 Working off on on on on on on\n",
" Status change stop start - - - - -\n",
" Total output 0 2000 2000 2000 2000 2000 2000\n",
" of which add. 0 800 800 800 800 800 800\n",
" \n",
"Unit 22 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 2000 2000 2000 2000 2000 2000 2000\n",
" of which add. 800 800 800 800 800 800 800\n",
" \n",
"Unit 23 Working off on on on on on off\n",
" Status change - start - - - - stop\n",
" Total output 0 1800 1800 1800 1800 1800 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 24 Working off on off on off on off\n",
" Status change - start stop start stop start stop\n",
" Total output 0 1800 0 1800 0 1800 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 25 Working off on off on off on on\n",
" Status change stop start stop start stop start -\n",
" Total output 0 1800 0 1800 0 1800 1800\n",
" of which add. 0 0 0 0 0 0 0"
]
}
],
"source": [
"# Display solution\n",
"def print_sol(prob):\n",
" ct = 0\n",
" print(f\"Time period \", end=\"\")\n",
" for t in PERIODS:\n",
" print('{:5d}-{:2}'.format(ct,ct+LEN[t]), end=\"\")\n",
" ct += LEN[t]\n",
"\n",
" for u in UNITS:\n",
" print(\"\\n\",f\"\\nUnit {u+1} Working \", end=\"\")\n",
" for t in PERIODS:\n",
" print(\" off\" if prob.getSolution(work[u,t]) == 0 else \" on\", end=\"\")\n",
" print(\"\\n Status change\", end=\"\")\n",
" for t in PERIODS:\n",
" if prob.getSolution(stop[u,t]) > 0.5: print(\" stop\", end=\"\")\n",
" else:\n",
" if prob.getSolution(start[u,t]) > 0.5: print(\" start\", end=\"\")\n",
" else: print(\" -\", end=\"\")\n",
" print(\"\\n Total output \", end=\"\")\n",
" for t in PERIODS:\n",
" print('{:8d}'.format(round(prob.getSolution(PMIN[TYPE[u]]*work[u,t] + padd[u,t]))), end=\"\")\n",
" print(\"\\n of which add.\", end=\"\")\n",
" for t in PERIODS:\n",
" print('{:8d}'.format(round(prob.getSolution(padd[u,t]))), end=\"\")\n",
"\n",
"print_sol(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we use the *matplotlib* pacakge to define a function that plots a bar chart with stacked bars of hourly power output per generator type per time period, the total power output and the total demand in each planning period. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_sol(prob):\n",
"\n",
" # Get the total power output per generator type per planning period\n",
" outputs = [[0 for _ in PERIODS] for _ in TYPES]\n",
" reserve = [0 for _ in PERIODS]\n",
" for i in TYPES:\n",
" for t in PERIODS:\n",
" for u in UNITS:\n",
" if TYPE[u] == i and prob.getSolution(work[u,t]) > 0.5:\n",
" power_output = prob.getSolution(PMIN[TYPE[u]]*work[u,t] + padd[u,t])\n",
" outputs[i][t] += power_output\n",
" reserve[t] += PMAX[TYPE[u]] - power_output\n",
" \n",
" # Labels for the unit types\n",
" labels = [f\"Unit type {i + 1}\" for i in TYPES]\n",
"\n",
" # Create a stacked bar chart\n",
" fig, ax = plt.subplots()\n",
"\n",
" # Plot output for each unit type per planning period\n",
" for i in range(len(outputs)):\n",
" ax.bar(range(1,NT+1), outputs[i], label=labels[i], bottom=np.sum(outputs[:i], axis=0))\n",
"\n",
" # Plot the total reserve per planning period\n",
" ax.bar(range(1,NT+1), reserve, label='Total reserve', bottom=np.sum(outputs, axis=0), fill=False, edgecolor='black', linestyle='--')\n",
"\n",
" # Plot the demand data as a line\n",
" ax.plot(range(1,NT+1), DEM, color='black', marker='o', label='Demand (MW)')\n",
"\n",
" # Label and size the axes\n",
" ax.set_xlabel('Planning period', fontsize=13)\n",
" ax.set_ylabel('Total output (MW)', fontsize=13)\n",
"\n",
" # Add a legend\n",
" ax.legend(loc='upper left', bbox_to_anchor=(1,1), fontsize=13)\n",
"\n",
" # Show the plot\n",
" plt.show()\n",
"\n",
"plot_sol(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding indicator constraints"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In real-world applications, generators must often remain for some time in a certain state after they have been switched to that state, i.e. if a generator is turned ON/OFF, it must remain in that state for at least $ON_s^{\\rm min}$/$OFF_s^{\\rm min}$ periods, respectively. Such state change constraints can be formulated with the help of so-called **indicator constraints** as their enforcement depends on the value of a binary variable, called the 'indicator'.\n",
"\n",
"The indicator constraints for this problem can be formulated as follows:\n",
"\n",
"$$\n",
"\\begin{align*}\n",
"& \\hbox{Can only switch OFF at least $ON_s^{\\rm min}$ periods after it has been turned ON:} \\\\\n",
"& \\qquad \\sum_{j=t+1}^{t+ON_s^{\\rm min}-1} stop_{u,(j \\hbox{ \\% } NT)} \\leq 0, \\qquad \\forall u \\in \\mathcal{U}, \\forall t \\in \\mathcal{T}: start_{u,t} = 1 \\\\\n",
"& \\hbox{Can only switch ON at least $OFF_s^{\\rm min}$ periods after it has been turned OFF:} \\\\\n",
"& \\qquad \\sum_{j=t+1}^{t+OFF_s^{\\rm min}-1} start_{u,(j \\hbox{ \\% } NT)} \\leq 0, \\qquad \\forall u \\in \\mathcal{U}, \\forall t \\in \\mathcal{T}: stop_{u,t} = 1 \\\\\n",
"\\end{align*}\n",
"$$\n",
"\n",
"where $ON_s^{\\rm min}$,$OFF_s^{\\rm min}$ = minimum time intervals a generator type $s$ must be ON/OFF once it has switched to that state, respectively.\n",
"\n",
"Indicator constraints can conveniently be added by using the [problem.addIndicator()](https://www.fico.com/fico-xpress-optimization/docs/latest/solver/optimizer/python/HTML/chModeling.html?scroll=secModelingIndicator) method (note the \"==\" as the symbol for the equality on the indicator).\n",
"\n",
"The code cell below adds the indicator constraints to the model, triggers the optimization again and prints the solution. Comparing the outcomes with those from the previous code cells, we can verify that there is an extra cost by introducing the new constraints, as now units are not allowed to be turned ON and then OFF within three time periods. Also note that the indicators make the problem more difficult (hence slower) to solve."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Daily cost: 1505300.0\n",
"Penalty: 1.1\n",
"Objective value: 1505301.1\n",
"Time period 0- 6 6- 9 9-12 12-14 14-18 18-22 22-24\n",
" \n",
"Unit 1 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 1750 750 750 750 750 750\n",
" of which add. 0 1000 0 0 0 0 0\n",
" \n",
"Unit 2 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 1750 750 750 750 750 750\n",
" of which add. 0 1000 0 0 0 0 0\n",
" \n",
"Unit 3 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 750 750 750 750 750 750\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 4 Working off on on on off off off\n",
" Status change - start - - stop - -\n",
" Total output 0 750 750 750 0 0 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 5 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 750 750 750 750 1400 750\n",
" of which add. 0 0 0 0 0 650 0\n",
" \n",
"Unit 6 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 1650 750 750 1400 1750 1750\n",
" of which add. 0 900 0 0 650 1000 1000\n",
" \n",
"Unit 7 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 1750 750 750 750 1750 750\n",
" of which add. 0 1000 0 0 0 1000 0\n",
" \n",
"Unit 8 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 1750 750 850 750 1750 750\n",
" of which add. 0 1000 0 100 0 1000 0\n",
" \n",
"Unit 9 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 1750 750 1750 750 1750 750\n",
" of which add. 0 1000 0 1000 0 1000 0\n",
" \n",
"Unit 10 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 750 1750 750 750 750 1750 1200\n",
" of which add. 0 1000 0 0 0 1000 450\n",
" \n",
"Unit 11 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1000 1500 1500 1500 1500 1500 1500\n",
" of which add. 0 500 500 500 500 500 500\n",
" \n",
"Unit 12 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1000 1500 1500 1500 1500 1500 1500\n",
" of which add. 0 500 500 500 500 500 500\n",
" \n",
"Unit 13 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1000 1500 1500 1500 1500 1500 1500\n",
" of which add. 0 500 500 500 500 500 500\n",
" \n",
"Unit 14 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1000 1500 1400 1500 1500 1500 1500\n",
" of which add. 0 500 400 500 500 500 500\n",
" \n",
"Unit 15 Working off off off on on on off\n",
" Status change - - - start - - stop\n",
" Total output 0 0 0 2000 2000 2000 0\n",
" of which add. 0 0 0 800 800 800 0\n",
" \n",
"Unit 16 Working off on on on off off off\n",
" Status change - start - - stop - -\n",
" Total output 0 2000 2000 2000 0 0 0\n",
" of which add. 0 800 800 800 0 0 0\n",
" \n",
"Unit 17 Working off on on on off off off\n",
" Status change - start - - stop - -\n",
" Total output 0 2000 2000 2000 0 0 0\n",
" of which add. 0 800 800 800 0 0 0\n",
" \n",
"Unit 18 Working off on on on off off off\n",
" Status change - start - - stop - -\n",
" Total output 0 2000 2000 2000 0 0 0\n",
" of which add. 0 800 800 800 0 0 0\n",
" \n",
"Unit 19 Working off off off on on on on\n",
" Status change stop - - start - - -\n",
" Total output 0 0 0 2000 2000 2000 2000\n",
" of which add. 0 0 0 800 800 800 800\n",
" \n",
"Unit 20 Working off off off on on on off\n",
" Status change - - - start - - stop\n",
" Total output 0 0 0 2000 2000 2000 0\n",
" of which add. 0 0 0 800 800 800 0\n",
" \n",
"Unit 21 Working off off off on on on off\n",
" Status change - - - start - - stop\n",
" Total output 0 0 0 2000 2000 2000 0\n",
" of which add. 0 0 0 800 800 800 0\n",
" \n",
"Unit 22 Working off on on on off off off\n",
" Status change - start - - stop - -\n",
" Total output 0 2000 2000 2000 0 0 0\n",
" of which add. 0 800 800 800 0 0 0\n",
" \n",
"Unit 23 Working off on on on off off off\n",
" Status change - start - - stop - -\n",
" Total output 0 1800 1800 1800 0 0 0\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 24 Working on on on on on on on\n",
" Status change - - - - - - -\n",
" Total output 1800 1800 1800 1800 1800 1800 1800\n",
" of which add. 0 0 0 0 0 0 0\n",
" \n",
"Unit 25 Working off off off on on on off\n",
" Status change - - - start - - stop\n",
" Total output 0 0 0 1800 1800 1800 0\n",
" of which add. 0 0 0 0 0 0 0"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Minimum time\n",
"ONMIN = [3, 3, 3, 3] # minimum time intervals a generator type $s$ must be ON once switched to that state\n",
"DWMIN = [3, 3, 3, 3] # minimum time intervals a generator type $s$ must be OFF once switched to that state\n",
"\n",
"# Indicator constraints\n",
"for u in UNITS:\n",
" for t in PERIODS:\n",
" # Can only switch off at least ONMIN periods later\n",
" p.addIndicator(start[u,t] == 1, xp.Sum(stop[u,j % NT] for j in range(t+1,t+ONMIN[TYPE[u]])) <= 0)\n",
" # Can only switch on at least DWMIN periods later\n",
" p.addIndicator(stop[u,t] == 1, xp.Sum(start[u,j % NT] for j in range(t+1,t+DWMIN[TYPE[u]])) <= 0)\n",
"\n",
"# Re-optimize the problem and print the daily cost, penalty and total objective value\n",
"p.controls.outputlog = 0\n",
"p.optimize()\n",
"print(\"Daily cost:\", round(p.getSolution(Cost),2))\n",
"print(\"Penalty:\", round(p.getSolution(Penalty),2))\n",
"print(\"Objective value:\", round(p.attributes.objval,2))\n",
"\n",
"print_sol(p)\n",
"plot_sol(p)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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