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],
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},
"metadata": {}
}
],
"source": [
"visualize_network(network)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rfhhtNijX9qV"
},
"source": [
"The IEEE-118 network has 18 generators, of which:\n",
"- 2 hydro plants (cyan)\n",
"- 4 coal plants (dark gray)\n",
"- 4 solarfarms (yellow)\n",
"- 2 windmills (white)\n",
"- 6 gas plants (green)\n",
"\n",
"Moreover, the network has 100 load nodes (gray). Each node has the following parameters:\n",
"- `node_id`\n",
"- `d`: demand\n",
"- `p_min`: minimum production capacity\n",
"- `p_max`: maximum production capacity\n",
"- `c_var`: variable production costs\n",
"- `is_generator`: boolean value to indicate whether the node is a generator or not\n",
"- `energy_type`: the type of energy used for electricity production"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-s-TbCDsXp2m"
},
"source": [
"All generator nodes can filtered by the `is_generator` parameter. All generators have a zero demand $d_i=0$. For the renewable energy sources (i.e., hydro, solar and wind) there are no variable costs `c_var`. For solar and wind, the production is fixed, i.e., `p_min = p_max`, meaning that all available solar and wind energy must be produced."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.653899Z",
"start_time": "2022-03-23T18:11:17.653879Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 644
},
"id": "lvjm6z_RqIWM",
"outputId": "0a4841e0-ef9b-4972-b214-f27e61fbd7b9"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" d p_min p_max c_var is_generator energy_type\n",
"node_id \n",
"9 0.0 0.000000 400.000000 0.000000 True hydro\n",
"11 0.0 0.000000 200.000000 0.000000 True hydro\n",
"24 0.0 0.000000 397.800000 28.948321 True coal\n",
"25 0.0 0.000000 873.000000 22.220980 True coal\n",
"30 0.0 0.000000 612.000000 25.993982 True coal\n",
"45 0.0 0.000000 720.000000 24.202306 True coal\n",
"48 0.0 0.000000 0.000000 0.000000 True solar\n",
"53 0.0 0.000000 0.000000 0.000000 True solar\n",
"58 0.0 0.000000 0.000000 0.000000 True solar\n",
"60 0.0 0.000000 0.000000 0.000000 True solar\n",
"64 0.0 38.484861 38.484861 0.000000 True wind\n",
"65 0.0 180.602933 180.602933 0.000000 True wind\n",
"79 0.0 0.000000 916.200000 50.330000 True gas\n",
"86 0.0 0.000000 360.000000 50.330000 True gas\n",
"88 0.0 0.000000 1146.600000 50.330000 True gas\n",
"99 0.0 0.000000 1175.400000 50.330000 True gas\n",
"102 0.0 0.000000 194.400000 50.330000 True gas\n",
"110 0.0 0.000000 142.200000 50.330000 True gas"
],
"text/html": [
"\n",
"

\n",
"

\n"
]
},
"metadata": {},
"execution_count": 25
}
],
"source": [
"example_nodes = network[\"nodes\"]\n",
"example_nodes[example_nodes.is_generator]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uqSV2c7HKbKS"
},
"source": [
"For the load nodes, the only important parameter is the demand $d_i \\geq 0$. All other parameters are either zero, `False`, or an empty string `''`."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.655599Z",
"start_time": "2022-03-23T18:11:17.655580Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 454
},
"id": "_07xln5tKWfr",
"outputId": "15038322-3cc0-4fb5-902b-a77cafc98053"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" d p_min p_max c_var is_generator energy_type\n",
"node_id \n",
"0 28.186145 0.0 0.0 0.0 False \n",
"1 10.921628 0.0 0.0 0.0 False \n",
"2 22.884795 0.0 0.0 0.0 False \n",
"3 21.961682 0.0 0.0 0.0 False \n",
"4 0.000000 0.0 0.0 0.0 False \n",
"... ... ... ... ... ... ...\n",
"113 4.396000 0.0 0.0 0.0 False \n",
"114 11.864042 0.0 0.0 0.0 False \n",
"115 108.311328 0.0 0.0 0.0 False \n",
"116 10.703998 0.0 0.0 0.0 False \n",
"117 18.242759 0.0 0.0 0.0 False \n",
"\n",
"[100 rows x 6 columns]"
],
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" d | p_min | p_max | c_var | is_generator | energy_type | |
---|---|---|---|---|---|---|

node_id | ||||||

9 | 0.0 | 0.000000 | 400.000000 | 0.000000 | True | hydro |

11 | 0.0 | 0.000000 | 200.000000 | 0.000000 | True | hydro |

24 | 0.0 | 0.000000 | 397.800000 | 28.948321 | True | coal |

25 | 0.0 | 0.000000 | 873.000000 | 22.220980 | True | coal |

30 | 0.0 | 0.000000 | 612.000000 | 25.993982 | True | coal |

45 | 0.0 | 0.000000 | 720.000000 | 24.202306 | True | coal |

48 | 0.0 | 0.000000 | 0.000000 | 0.000000 | True | solar |

53 | 0.0 | 0.000000 | 0.000000 | 0.000000 | True | solar |

58 | 0.0 | 0.000000 | 0.000000 | 0.000000 | True | solar |

60 | 0.0 | 0.000000 | 0.000000 | 0.000000 | True | solar |

64 | 0.0 | 38.484861 | 38.484861 | 0.000000 | True | wind |

65 | 0.0 | 180.602933 | 180.602933 | 0.000000 | True | wind |

79 | 0.0 | 0.000000 | 916.200000 | 50.330000 | True | gas |

86 | 0.0 | 0.000000 | 360.000000 | 50.330000 | True | gas |

88 | 0.0 | 0.000000 | 1146.600000 | 50.330000 | True | gas |

99 | 0.0 | 0.000000 | 1175.400000 | 50.330000 | True | gas |

102 | 0.0 | 0.000000 | 194.400000 | 50.330000 | True | gas |

110 | 0.0 | 0.000000 | 142.200000 | 50.330000 | True | gas |

\n",
"

\n"
]
},
"metadata": {},
"execution_count": 26
}
],
"source": [
"example_nodes[~example_nodes.is_generator]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B1STSbdWG66p"
},
"source": [
"### Edges"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n-6c0gu0G6u6"
},
"source": [
"The IEEE-118 network has 179 edges. Each edge has the following parameters:\n",
"- `node_id1`: first node of a given edge\n",
"- `node_id2`: second node of a given edge\n",
"- `b`: the line susceptance, and\n",
"- `f_max`: the maximum edge capacity."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.657053Z",
"start_time": "2022-03-23T18:11:17.657034Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 454
},
"id": "wKwPFK-PKi7-",
"outputId": "569e85a7-b798-4fc7-af9f-596f8e5113a6"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" b f_max\n",
"node_id1 node_id2 \n",
"0 1 10.0100 271\n",
" 2 23.5849 271\n",
"3 4 125.3133 316\n",
"2 4 9.2593 315\n",
"4 5 18.5185 316\n",
"... ... ...\n",
"64 65 28.9059 1427\n",
"67 68 28.9059 1427\n",
"80 79 28.9059 1427\n",
"86 85 4.8216 253\n",
"67 115 246.9136 12992\n",
"\n",
"[179 rows x 2 columns]"
],
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" d | p_min | p_max | c_var | is_generator | energy_type | |
---|---|---|---|---|---|---|

node_id | ||||||

0 | 28.186145 | 0.0 | 0.0 | 0.0 | False | |

1 | 10.921628 | 0.0 | 0.0 | 0.0 | False | |

2 | 22.884795 | 0.0 | 0.0 | 0.0 | False | |

3 | 21.961682 | 0.0 | 0.0 | 0.0 | False | |

4 | 0.000000 | 0.0 | 0.0 | 0.0 | False | |

... | ... | ... | ... | ... | ... | ... |

113 | 4.396000 | 0.0 | 0.0 | 0.0 | False | |

114 | 11.864042 | 0.0 | 0.0 | 0.0 | False | |

115 | 108.311328 | 0.0 | 0.0 | 0.0 | False | |

116 | 10.703998 | 0.0 | 0.0 | 0.0 | False | |

117 | 18.242759 | 0.0 | 0.0 | 0.0 | False |

100 rows Ã— 6 columns

\n", "\n",
"

\n"
]
},
"metadata": {},
"execution_count": 27
}
],
"source": [
"edges_df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6mA0zSPEhd6P"
},
"source": [
"### Instances\n",
"Since the OPF problem is solved every 15 minutes in practice, you will be given `24 * 4 = 96` network instances that need to be solved, hence solving an entire day's worth of OPF problems. The first instance thus corresponds to the power generation within time window `[00:00, 00:15]`, the second instance corresponds to `[00:15, 00:30]`, and so on. The data takes into account a realistic demand and (renewable energy) generation pattern. We assume that the decisions in each time window are independent of the previous and subsequent time windows, so every 15 minutes a new OPF instance is solved independently.\n",
"\n",
"The network instances are stored in the variable `I` as a list and can be accessed using indexing, i.e., `I[0]` gives the first network instance and `I[95]` gives the 96th network instance."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0GVlpC14Vixu"
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Tejr2cdxtOWx"
},
"source": [
"# Solving OPF\n",
"Observe that the stated OPF problem contains absolute decision values. We first rewrite the OPF problem into a linear optimization problem.\n",
"\n",
"$$\\begin{align}\n",
"\\begin{array}{llll}\n",
"\\min & \\sum_{i \\in V} c_i p_i \\\\\n",
"\\text{s.t.} & \\sum_{j: (i, j) \\in E} f^+_{ij} - f^-_{ij} - \\sum_{j: (j, i) \\in E} f^+_{ji} - f_{ji}^- = p_i - d_i & \\forall \\, i \\in V,\\\\\n",
"& f^+_{ij} - f^-_{ij} = b_{ij}(\\theta_i - \\theta_j), & \\forall \\, (i, j) \\in E, \\\\\n",
" & f^+_{ij} + f^-_{ij} \\leq f_{ij}^{\\max} & \\forall (i, j) \\in E,\\\\\n",
" & p_{i}^{\\min } \\leq p_{i} \\leq p_{i}^{\\max } & \\forall i \\in V, \\\\\n",
" & p_i \\in \\mathbb{R}_{\\geq 0} & \\forall i \\in V, \\\\\n",
" & \\theta_i \\in \\mathbb{R} & \\forall i \\in V, \\\\\n",
" & f_{ij}^+, f_{ij}^- \\in \\mathbb{R} & \\forall (i, j) \\in E \\\\\n",
"\\end{array}\n",
"\\end{align}$$\n",
"\n",
"We then implement the model using `AMPL` and solve it for all instances `I[0]` to `I[95]`, reporting the average objective value across all the 96 instances."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "FQ2l_SNlaKOb",
"outputId": "13e32fac-9e43-4911-fcd9-c91558258469",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Overwriting opf1.mod\n"
]
}
],
"source": [
"%%writefile opf1.mod\n",
"\n",
"# Indexing sets\n",
"set NODES;\n",
"set EDGES within {NODES,NODES};\n",
"\n",
"# Parameters\n",
"param d{NODES};\n",
"param p_min{NODES};\n",
"param p_max{NODES};\n",
"param c_var{NODES};\n",
"param is_generator{NODES};\n",
"param energy_type{NODES} symbolic;\n",
"\n",
"param b{EDGES};\n",
"param f_max{EDGES};\n",
"\n",
"# Declare decision variables\n",
"var p{i in NODES} >= p_min[i], <= p_max[i];\n",
"var theta{NODES};\n",
"var fabs{(i,j) in EDGES} >= 0, <= f_max[i, j];\n",
"var fp{EDGES} >= 0;\n",
"var fm{EDGES} >= 0;\n",
"\n",
"# Declare objective value\n",
"minimize energy_cost: sum{i in NODES: is_generator[i] == 1} c_var[i] * p[i];\n",
"\n",
"# Auxiliary variables for incoming and outgoing flows\n",
"var incoming_flow{i in NODES} = sum{j in NODES: (j,i) in EDGES} (fp[j, i] - fm[j, i]);\n",
"var outgoing_flow{i in NODES} = sum{j in NODES: (i,j) in EDGES} (fp[i, j] - fm[i, j]);\n",
"\n",
"# Constraints\n",
"s.t. flow_conservation{i in NODES}:\n",
" outgoing_flow[i] - incoming_flow[i] == p[i] - d[i];\n",
"\n",
"s.t. susceptance{(i,j) in EDGES}:\n",
" fp[i, j] - fm[i, j] == b[i, j] * (theta[i] - theta[j]);\n",
"\n",
"s.t. abs_flow{(i,j) in EDGES}:\n",
" fabs[i, j] == fp[i, j] + fm[i, j];"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.662132Z",
"start_time": "2022-03-23T18:11:17.662094Z"
},
"id": "Ef3-7skltzLA"
},
"outputs": [],
"source": [
"def OPF1(network):\n",
" \"\"\"\n",
" Input:\n",
" - network: a dictionary containing:\n",
" - a dictionary of nodes with a dictionary of attributes\n",
" - a dictionary of edges with a dictionary of attributes\n",
"\n",
" Output:\n",
" - total energy dispatching cost\n",
" \"\"\"\n",
"\n",
" nodes = network[\"nodes\"]\n",
" edges = network[\"edges\"]\n",
"\n",
" m = AMPL()\n",
" m.read(\"opf1.mod\")\n",
"\n",
" m.set_data(nodes, \"NODES\")\n",
" m.set_data(edges, \"EDGES\")\n",
"\n",
" m.option[\"solver\"] = SOLVER\n",
" m.solve()\n",
"\n",
" return m.obj[\"energy_cost\"].value()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "55JG8fJ6aKOc",
"outputId": "0c897afa-07e8-40ba-dab0-73e61814e909",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25556.38586\n",
"527 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25291.14543\n",
"524 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25014.46578\n",
"517 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 24033.92447\n",
"547 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 24607.51391\n",
"526 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23894.80106\n",
"523 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23539.85465\n",
"522 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23344.3297\n",
"532 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 22863.0752\n",
"516 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21810.52787\n",
"521 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21900.80263\n",
"532 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21547.3871\n",
"526 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20462.85553\n",
"530 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20203.29637\n",
"525 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19631.87938\n",
"528 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19053.61942\n",
"514 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19052.65246\n",
"529 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19361.24172\n",
"530 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19838.31763\n",
"527 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20490.77796\n",
"527 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20961.18848\n",
"525 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21810.71275\n",
"538 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23204.42688\n",
"520 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23961.68452\n",
"560 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25710.80611\n",
"523 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 28528.05224\n",
"519 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 30606.15924\n",
"529 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 32931.97675\n",
"535 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 35933.96987\n",
"524 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 40878.89847\n",
"543 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 41272.13149\n",
"553 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 41625.78447\n",
"548 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 43657.80972\n",
"524 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 44523.08766\n",
"539 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46653.79048\n",
"528 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 48123.99084\n",
"542 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 49986.24726\n",
"526 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51594.08932\n",
"539 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 48767.34223\n",
"536 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46412.44208\n",
"555 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 45234.51683\n",
"563 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47192.70567\n",
"533 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 44287.62719\n",
"536 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 44509.58397\n",
"566 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 42934.44805\n",
"523 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 42869.82759\n",
"535 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 39555.8754\n",
"540 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 38670.71153\n",
"538 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 37154.52425\n",
"531 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36860.74842\n",
"537 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 38740.0701\n",
"543 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36354.1365\n",
"525 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36912.55781\n",
"551 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 37668.23898\n",
"545 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 37040.66645\n",
"546 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 34794.47952\n",
"527 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36325.10891\n",
"532 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36452.65775\n",
"537 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 35163.06581\n",
"536 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 35116.35087\n",
"537 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36112.04806\n",
"535 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 34697.75853\n",
"529 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 35180.60733\n",
"529 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 35021.77992\n",
"522 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36530.09028\n",
"531 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 37554.629\n",
"525 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 41679.08392\n",
"542 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46836.06918\n",
"543 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 54206.38573\n",
"541 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 57125.77445\n",
"522 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 59781.78181\n",
"554 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62049.83588\n",
"546 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 61861.88082\n",
"547 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62773.3659\n",
"569 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 63568.70959\n",
"548 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 63537.75177\n",
"544 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 65871.0868\n",
"554 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 64744.56999\n",
"562 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 64044.66726\n",
"549 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62795.39073\n",
"552 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 60679.41848\n",
"543 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 60587.38654\n",
"578 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 56395.11375\n",
"541 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 54159.88066\n",
"545 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51612.43852\n",
"553 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47266.24936\n",
"544 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 43900.49913\n",
"540 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 40067.91939\n",
"535 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36097.07305\n",
"551 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 34979.65852\n",
"519 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 34133.27621\n",
"516 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 33746.40968\n",
"511 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 32602.68668\n",
"514 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 31745.40199\n",
"520 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 30839.27015\n",
"528 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 29427.14541\n",
"529 simplex iterations\n",
"0 barrier iterations\n",
" \n",
"The average objective value over all instances is: 38507.23\n"
]
}
],
"source": [
"OPF1_results = [OPF1(instance) for instance in I]\n",
"print(f\"The average objective value over all instances is: {np.mean(OPF1_results):.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1oev1Tzdt8H7"
},
"source": [
"# Strict fossil fuel policy pt.1\n",
"Gas and coal plants emit CO2, while renewable energy sources are carbon neutral. For this reason, the Dutch government has decided to constrain the number of active fossil-fuel-based power plants for the generation of electricity. More specifically, a maximum of 2 gas plants and 1 coal plant may be *active* during a single OPF instance. Any plant that is set *inactive* for a specific instance cannot produce any electricity.\n",
"\n",
"We first write down the new model. To this end, we introduce new decision variables $x_i, i\\in V$ to the model, which indicate whether a generator $i$ is active or inactive.\n",
"\n",
"$$\\begin{align}\n",
"\\begin{array}{llll}\n",
"\\min & \\sum_{i \\in V} c_i p_i \\\\\n",
"\\text{s.t.} & \\sum_{j: (i, j) \\in E} f^+_{ij} - f^-_{ij} - \\sum_{j: (j, i) \\in E} f^+_{ji} - f_{ji}^- = p_i - d_i & \\forall \\, i \\in V,\\\\\n",
"& f^+_{ij} - f^-_{ij} = b_{ij}(\\theta_i - \\theta_j), & \\forall \\, (i, j) \\in E, \\\\\n",
"& f^{abs}_{ij} = f^+_{ij} + f^-_{ij}, & \\forall \\, (i, j) \\in E, \\\\\n",
" & f_{ij}^{abs} \\leq f_{ij}^{\\max} & \\forall (i, j) \\in E,\\\\\n",
" & p_{i}^{\\min } x_i \\leq p_{i} \\leq p_{i}^{\\max } x_i & \\forall i \\in V, \\\\\n",
" & \\sum_{i \\in \\mathcal{G}_{\\text{gas}}} x_i \\leq 2 & \\\\\n",
" & \\sum_{i \\in \\mathcal{G}_{\\text{coal}}} x_i \\leq 1 & \\\\\n",
" & p_i \\in \\mathbb{R}_{\\geq 0} & \\forall i \\in V, \\\\\n",
" & \\theta_i \\in \\mathbb{R} & \\forall i \\in V, \\\\\n",
" & f_{ij} \\in \\mathbb{R} & \\forall (i, j) \\in E \\\\\n",
" & x_i \\in \\{0, 1\\} & \\forall i \\in V\\\\\n",
"\\end{array}\n",
"\\end{align}$$\n",
"\n",
"We then implement the new model using `AMPL` and solve it for all instances `I[0]` to `I[95]`, reporting the average objective value across the instances."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "bymwZJFwaKOe",
"outputId": "61aa9949-56b8-4f5c-daca-06aa89b4c70a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Overwriting opf2.mod\n"
]
}
],
"source": [
"%%writefile opf2.mod\n",
"\n",
"# Indexing sets\n",
"set NODES;\n",
"set EDGES within {NODES,NODES};\n",
"\n",
"# Parameters\n",
"param d{NODES};\n",
"param p_min{NODES};\n",
"param p_max{NODES};\n",
"param c_var{NODES};\n",
"param is_generator{NODES};\n",
"param energy_type{NODES} symbolic;\n",
"\n",
"param b{EDGES};\n",
"param f_max{EDGES};\n",
"\n",
"param max_gas_plants;\n",
"param max_coal_plants;\n",
"\n",
"# Declare decision variables\n",
"var p{i in NODES} >= 0;\n",
"var theta{NODES};\n",
"var x{NODES} binary;\n",
"var fabs{(i,j) in EDGES} >= 0, <= f_max[i, j];\n",
"var fp{EDGES} >= 0;\n",
"var fm{EDGES} >= 0;\n",
"\n",
"# Declare objective value\n",
"minimize energy_cost: sum{i in NODES: is_generator[i] == 1} c_var[i] * p[i];\n",
"\n",
"# Auxiliary variables for incoming and outgoing flows\n",
"var incoming_flow{i in NODES} = sum{j in NODES: (j,i) in EDGES} (fp[j, i] - fm[j, i]);\n",
"var outgoing_flow{i in NODES} = sum{j in NODES: (i,j) in EDGES} (fp[i, j] - fm[i, j]);\n",
"\n",
"# Constraints\n",
"s.t. flow_conservation{i in NODES}:\n",
" outgoing_flow[i] - incoming_flow[i] == p[i] - d[i];\n",
"\n",
"s.t. susceptance{(i,j) in EDGES}:\n",
" fp[i, j] - fm[i, j] == b[i, j] * (theta[i] - theta[j]);\n",
"\n",
"s.t. abs_flow{(i,j) in EDGES}:\n",
" fabs[i, j] == fp[i, j] + fm[i, j];\n",
"\n",
"s.t. generation_upper_bound{i in NODES}: p[i] <= p_max[i] * x[i];\n",
"s.t. generation_lower_bound{i in NODES}: p[i] >= p_min[i] * x[i];\n",
"\n",
"s.t. gas_plants_limit: sum{i in NODES: energy_type[i] == 'gas'} x[i] <= max_gas_plants;\n",
"s.t. coal_plants_limit: sum{i in NODES: energy_type[i] == 'coal'} x[i] <= max_coal_plants;"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.666581Z",
"start_time": "2022-03-23T18:11:17.666565Z"
},
"id": "26xrwlV9spHR"
},
"outputs": [],
"source": [
"def OPF2(network, max_gas_plants, max_coal_plants):\n",
" \"\"\"\n",
" Input:\n",
" - network: a dictionary containing:\n",
" - a dictionary of nodes with a dictionary of attributes\n",
" - a dictionary of edges with a dictionary of attributes\n",
"\n",
" Output:\n",
" - total energy dispatching cost\n",
" \"\"\"\n",
"\n",
" nodes = network[\"nodes\"]\n",
" edges = network[\"edges\"]\n",
"\n",
" m = AMPL()\n",
" m.read(\"opf2.mod\")\n",
"\n",
" m.set_data(nodes, \"NODES\")\n",
" m.set_data(edges, \"EDGES\")\n",
"\n",
" m.param[\"max_gas_plants\"] = max_gas_plants\n",
" m.param[\"max_coal_plants\"] = max_coal_plants\n",
"\n",
" m.option[\"solver\"] = SOLVER\n",
" m.solve()\n",
"\n",
" return m.obj[\"energy_cost\"].value()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.668379Z",
"start_time": "2022-03-23T18:11:17.668356Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gyHAm3pnb4sn",
"outputId": "8694d6cb-30d3-48b1-c5d3-80af2df930c6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 32014.85844\n",
"578 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 31513.92689\n",
"576 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 30885.43208\n",
"569 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 28881.74194\n",
"558 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 30037.14784\n",
"553 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 28659.49442\n",
"730 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 28098.31029\n",
"706 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 27643.13369\n",
"737 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 26906.38333\n",
"666 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25000.0886\n",
"788 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25172.05815\n",
"774 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 24597.59628\n",
"773 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 22704.40086\n",
"810 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 22272.96622\n",
"802 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21282.46505\n",
"794 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20149.63104\n",
"768 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20299.75691\n",
"747 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20796.63521\n",
"766 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21606.25852\n",
"820 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 22744.74502\n",
"831 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23499.3076\n",
"835 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25010.50854\n",
"770 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 27655.6242\n",
"725 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 28739.85416\n",
"674 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 32306.05117\n",
"570 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 38259.59372\n",
"592 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 42596.56579\n",
"572 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47474.46445\n",
"587 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 52182.27728\n",
"549 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 57946.94606\n",
"549 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 58983.93317\n",
"556 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 59611.09337\n",
"562 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62318.4829\n",
"610 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 63377.649\n",
"553 simplex iterations\n",
"1 branching nodes\n",
"absmipgap=7.27596e-12, relmipgap=1.14803e-16\n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 66355.25327\n",
"648 simplex iterations\n",
"1 branching nodes\n",
"absmipgap=2.91038e-11, relmipgap=4.38606e-16\n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 67718.3282\n",
"559 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 70199.13353\n",
"601 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 72074.24181\n",
"574 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 69357.83301\n",
"587 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 66541.23238\n",
"554 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 65359.14645\n",
"580 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 67603.72994\n",
"551 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 64424.45835\n",
"543 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 64578.61294\n",
"550 simplex iterations\n",
"1 branching nodes\n",
"absmipgap=8.73115e-11, relmipgap=1.35202e-15\n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62683.24322\n",
"544 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62712.94063\n",
"537 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 58810.9962\n",
"539 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 57847.89312\n",
"533 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 54468.61779\n",
"534 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 54196.02103\n",
"534 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 56244.40675\n",
"538 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 52907.81025\n",
"515 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 53769.82573\n",
"558 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 55263.93141\n",
"549 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 54378.86264\n",
"545 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 50680.88639\n",
"535 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 53087.4615\n",
"541 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 52929.63938\n",
"539 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51552.75812\n",
"524 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51058.5326\n",
"534 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 52840.59651\n",
"545 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 50645.90154\n",
"522 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51995.97075\n",
"554 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51721.08012\n",
"537 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 54156.67505\n",
"542 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 55970.40312\n",
"544 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 61124.14254\n",
"537 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 67438.51907\n",
"557 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 75846.37597\n",
"606 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 79266.53736\n",
"570 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 82505.30565\n",
"580 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 85139.05154\n",
"577 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 84584.98267\n",
"590 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 85269.31644\n",
"592 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 86348.42938\n",
"573 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 86331.61954\n",
"568 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 88598.84186\n",
"630 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 87298.4441\n",
"595 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 86180.15407\n",
"560 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 84762.76321\n",
"569 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 82124.08662\n",
"549 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 82071.14498\n",
"575 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 77315.23968\n",
"564 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 74464.39753\n",
"582 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 71600.93405\n",
"609 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 66396.59052\n",
"621 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62377.56686\n",
"564 simplex iterations\n",
"1 branching nodes\n",
"absmipgap=1.16415e-10, relmipgap=1.8663e-15\n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 57665.26521\n",
"562 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 53248.29462\n",
"553 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51707.5513\n",
"547 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 50039.81283\n",
"581 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 49235.30303\n",
"584 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46856.8694\n",
"595 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 45074.09967\n",
"569 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 43164.18792\n",
"577 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 40195.99971\n",
"560 simplex iterations\n",
"1 branching nodes\n",
" \n",
"The average objective value over all instances is: 53120.81\n"
]
}
],
"source": [
"max_gas_plants = 2\n",
"max_coal_plants = 1\n",
"OPF2_results = [OPF2(instance, max_gas_plants, max_coal_plants) for instance in I]\n",
"print(f\"The average objective value over all instances is: {np.mean(OPF2_results):.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VY8Kj9ZWc__P"
},
"source": [
"# Strict fossil fuel policy pt.2\n",
"The restriction on the number of gas and coal plants may pose a threat to the availability of electricity production when renewable energy sources fail to deliver. For this reason, the grid operators have decided to slightly change the constraint that was introduced for OPF2. If the total production of energy from renewable energy sources (i.e., solar, wind and hydro) is above 1000, then the number of gas and coal plants is restricted to 2 and 1, respectively. Otherwise, the restriction on the number of gas and coal plants is lifted. These constraints can be modeled as *either-or* constraints.\n",
" \n",
"We first write down the new model, using big-$M$ constraints to model the either-or constraint.\n",
"\n",
"$$\\begin{align}\n",
"\\begin{array}{llll}\n",
"\\min & \\sum_{i \\in V} c_i p_i \\\\\n",
"\\text{s.t.} & \\sum_{j: (i, j) \\in E} f^+_{ij} - f^-_{ij} - \\sum_{j: (j, i) \\in E} f^+_{ji} - f_{ji}^- = p_i - d_i & \\forall \\, i \\in V,\\\\\n",
"& f^+_{ij} - f^-_{ij} = b_{ij}(\\theta_i - \\theta_j), & \\forall \\, (i, j) \\in E, \\\\\n",
"& f^{abs}_{ij} = f^+_{ij} + f^-_{ij}, & \\forall \\, (i, j) \\in E, \\\\\n",
" & f_{ij}^{abs} \\leq f_{ij}^{\\max} & \\forall (i, j) \\in E,\\\\\n",
" & p_{i}^{\\min } x_i \\leq p_{i} \\leq p_{i}^{\\max } x_i & \\forall i \\in V, \\\\\n",
" & \\sum_{i \\in \\mathcal{G}_{\\text{gas}}} x_i \\leq 2 + (1-y)M_0 & \\\\\n",
" & \\sum_{i \\in \\mathcal{G}_{\\text{coal}}} x_i \\leq 1 + (1-y)M_1 & \\\\\n",
" & \\sum_{i \\in \\mathcal{G}_\\text{solar}} p_i + \\sum_{i \\in \\mathcal{G}_\\text{wind}} p_i + \\sum_{i \\in \\mathcal{G}_\\text{hydro}} p_i \\leq 1000 + yM_2&\\\\\n",
" & p_i \\in \\mathbb{R}_{\\geq 0} & \\forall i \\in V, \\\\\n",
" & \\theta_i \\in \\mathbb{R} & \\forall i \\in V, \\\\\n",
" & f_{ij} \\in \\mathbb{R} & \\forall (i, j) \\in E \\\\\n",
" & x_i \\in \\{0, 1\\} & \\forall i \\in V\\\\\n",
" & y \\in \\{0, 1\\} &\\\\\n",
"\\end{array}\n",
"\\end{align}$$\n",
"\n",
"We now implement the new model using `AMPL` and solve it for all instances `I[0]` to `I[95]`, reporting the average objective value across the instances."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"id": "ATbqm4nJaKOg",
"outputId": "dcc998b6-9a29-4399-df16-714c159b0201",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Overwriting opf3.mod\n"
]
}
],
"source": [
"%%writefile opf3.mod\n",
"\n",
"# Indexing sets\n",
"set NODES;\n",
"set EDGES within {NODES,NODES};\n",
"\n",
"# Parameters\n",
"param d{NODES};\n",
"param p_min{NODES};\n",
"param p_max{NODES};\n",
"param c_var{NODES};\n",
"param is_generator{NODES};\n",
"param energy_type{NODES} symbolic;\n",
"\n",
"param b{EDGES};\n",
"param f_max{EDGES};\n",
"\n",
"param max_gas_plants;\n",
"param max_coal_plants;\n",
"\n",
"# Big-Ms\n",
"param M0;\n",
"param M1;\n",
"param M2;\n",
"\n",
"# Declare decision variables\n",
"var p{i in NODES} >= 0;\n",
"var theta{NODES};\n",
"var x{NODES} binary;\n",
"var fabs{(i,j) in EDGES} >= 0, <= f_max[i, j];\n",
"var fp{EDGES} >= 0;\n",
"var fm{EDGES} >= 0;\n",
"var y binary;\n",
"\n",
"# Declare objective value\n",
"minimize energy_cost: sum{i in NODES: is_generator[i] == 1} c_var[i] * p[i];\n",
"\n",
"# Auxiliary variables for incoming and outgoing flows\n",
"var incoming_flow{i in NODES} = sum{j in NODES: (j,i) in EDGES} (fp[j, i] - fm[j, i]);\n",
"var outgoing_flow{i in NODES} = sum{j in NODES: (i,j) in EDGES} (fp[i, j] - fm[i, j]);\n",
"\n",
"# Constraints\n",
"s.t. flow_conservation{i in NODES}:\n",
" outgoing_flow[i] - incoming_flow[i] == p[i] - d[i];\n",
"\n",
"s.t. susceptance{(i,j) in EDGES}:\n",
" fp[i, j] - fm[i, j] == b[i, j] * (theta[i] - theta[j]);\n",
"\n",
"s.t. abs_flow{(i,j) in EDGES}:\n",
" fabs[i, j] == fp[i, j] + fm[i, j];\n",
"\n",
"s.t. generation_upper_bound{i in NODES}: p[i] <= p_max[i] * x[i];\n",
"s.t. generation_lower_bound{i in NODES}: p[i] >= p_min[i] * x[i];\n",
"\n",
"s.t. gas_plants_limit: sum{i in NODES: energy_type[i] == 'gas'} x[i] <= max_gas_plants + (1 - y) * M0;\n",
"\n",
"s.t. coal_plants_limit: sum{i in NODES: energy_type[i] == 'coal'} x[i] <= max_coal_plants + (1 - y) * M1;\n",
"\n",
"s.t. renewable_energy_production:\n",
" sum{i in NODES: energy_type[i] in {'solar', 'wind', 'hydro'}} p[i] <= 1000 + y * M2;"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.670681Z",
"start_time": "2022-03-23T18:11:17.670630Z"
},
"id": "JSWgoI5SsjgR"
},
"outputs": [],
"source": [
"def OPF3(network, max_gas_plants, max_coal_plants):\n",
" \"\"\"\n",
" Input:\n",
" - network: a dictionary containing:\n",
" - a dictionary of nodes with a dictionary of attributes\n",
" - a dictionary of edges with a dictionary of attributes\n",
"\n",
" Output:\n",
" - total energy dispatching cost\n",
" \"\"\"\n",
"\n",
" nodes = network[\"nodes\"]\n",
" edges = network[\"edges\"]\n",
"\n",
" m = AMPL()\n",
" m.read(\"opf3.mod\")\n",
"\n",
" m.set_data(nodes, \"NODES\")\n",
" m.set_data(edges, \"EDGES\")\n",
"\n",
" m.param[\"max_gas_plants\"] = max_gas_plants\n",
" m.param[\"max_coal_plants\"] = max_coal_plants\n",
"\n",
" m.param[\"M0\"] = 4\n",
" m.param[\"M1\"] = 3\n",
" m.param[\"M2\"] = nodes[\"p_max\"].sum()\n",
"\n",
" m.option[\"solver\"] = SOLVER\n",
" m.solve()\n",
"\n",
" return m.obj[\"energy_cost\"].value()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.672765Z",
"start_time": "2022-03-23T18:11:17.672746Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "v9S9D--ONiTU",
"outputId": "d9f0afae-afef-486e-9b42-bab6dd3ddc42"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25556.38586\n",
"542 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25291.14543\n",
"541 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25014.46578\n",
"532 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 24033.92447\n",
"532 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 24607.51391\n",
"527 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23894.80106\n",
"548 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23539.85465\n",
"531 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23344.3297\n",
"555 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 22863.0752\n",
"538 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21810.52787\n",
"535 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21900.80263\n",
"531 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21547.3871\n",
"524 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20462.85553\n",
"543 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20203.29637\n",
"534 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19631.87938\n",
"534 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19053.61942\n",
"535 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19052.65246\n",
"539 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19361.24172\n",
"535 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 19838.31763\n",
"539 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20490.77796\n",
"531 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 20961.18848\n",
"531 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 21810.71275\n",
"536 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23204.42688\n",
"535 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 23961.68452\n",
"533 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 25710.80611\n",
"521 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 28528.05224\n",
"533 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 30606.15924\n",
"540 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 32931.97675\n",
"518 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 35933.96987\n",
"563 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 40878.89847\n",
"540 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 41272.13149\n",
"551 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 41625.78447\n",
"530 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 43657.80972\n",
"556 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 44523.08766\n",
"522 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46653.79048\n",
"531 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 48517.02462\n",
"610 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51469.76795\n",
"793 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 53735.57811\n",
"667 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 52478.34218\n",
"792 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51082.21976\n",
"789 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 50820.70479\n",
"650 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 52284.09621\n",
"607 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51770.03428\n",
"722 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 52556.32959\n",
"1132 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 52063.08667\n",
"934 simplex iterations\n",
"9 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 50973.40711\n",
"968 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 49101.46232\n",
"1292 simplex iterations\n",
"7 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47859.57387\n",
"1175 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47812.22458\n",
"1084 simplex iterations\n",
"10 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47735.03354\n",
"1065 simplex iterations\n",
"7 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 49249.56069\n",
"1073 simplex iterations\n",
"10 branching nodes\n",
"absmipgap=2.11363, relmipgap=4.29168e-05\n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47079.99596\n",
"1159 simplex iterations\n",
"11 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47106.46001\n",
"1103 simplex iterations\n",
"11 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47698.24825\n",
"1418 simplex iterations\n",
"8 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47740.93595\n",
"1170 simplex iterations\n",
"10 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 45854.74172\n",
"1287 simplex iterations\n",
"14 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46738.37993\n",
"1057 simplex iterations\n",
"11 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46817.47832\n",
"1160 simplex iterations\n",
"14 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 45851.54466\n",
"1257 simplex iterations\n",
"14 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46189.99144\n",
"1222 simplex iterations\n",
"14 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46339.28074\n",
"1165 simplex iterations\n",
"14 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 45723.75827\n",
"1106 simplex iterations\n",
"14 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 44508.52721\n",
"1083 simplex iterations\n",
"12 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 44574.67924\n",
"903 simplex iterations\n",
"12 branching nodes\n",
"absmipgap=0.500375, relmipgap=1.12255e-05\n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46015.12654\n",
"1007 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 46672.0612\n",
"793 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 49744.16264\n",
"1167 simplex iterations\n",
"7 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 54261.30821\n",
"631 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 59898.76421\n",
"605 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62696.04055\n",
"587 simplex iterations\n",
"1 branching nodes\n",
"absmipgap=1.00408e-09, relmipgap=1.60151e-14\n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 63259.92706\n",
"747 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 65614.15585\n",
"689 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 64099.97548\n",
"730 simplex iterations\n",
"3 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 63826.39248\n",
"584 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 63624.76669\n",
"580 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 63537.75177\n",
"570 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 65871.0868\n",
"554 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 64744.56999\n",
"564 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 64044.66726\n",
"549 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 62795.39073\n",
"551 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 60679.41848\n",
"543 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 60587.38654\n",
"535 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 56395.11375\n",
"546 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 54159.88066\n",
"567 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 51612.43852\n",
"552 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 47266.24936\n",
"536 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 43900.49913\n",
"535 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 40067.91939\n",
"529 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 36097.07305\n",
"527 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 34979.65852\n",
"522 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 34133.27621\n",
"524 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 33746.40968\n",
"544 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 32602.68668\n",
"521 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 31745.40199\n",
"547 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 30839.27015\n",
"533 simplex iterations\n",
"1 branching nodes\n",
" \n",
"HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 29427.14541\n",
"535 simplex iterations\n",
"1 branching nodes\n",
" \n",
"The average objective value over all instances is: 41608.73\n"
]
}
],
"source": [
"OPF3_results = [OPF3(instance, max_gas_plants, max_coal_plants) for instance in I]\n",
"print(f\"The average objective value over all instances is: {np.mean(OPF3_results):.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_k1ovuJ3M6cj"
},
"source": [
"# Comparing the three models\n",
"For all three implemented models, we plot the objective values for all instances and explain the differences between the objective values in view of the different feasible regions."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"id": "7vX1pcQxaKOi"
},
"outputs": [],
"source": [
"objs = [OPF1_results, OPF2_results, OPF3_results]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-23T18:11:17.676081Z",
"start_time": "2022-03-23T18:11:17.676034Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"id": "EWdEKlqUoOD1",
"outputId": "c1bf545d-b021-44f8-e954-81b93103c439"
},
"outputs": [
{
"output_type": "display_data",
"data": {
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" b | f_max | ||
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node_id1 | node_id2 | ||

0 | 1 | 10.0100 | 271 |

2 | 23.5849 | 271 | |

3 | 4 | 125.3133 | 316 |

2 | 4 | 9.2593 | 315 |

4 | 5 | 18.5185 | 316 |

... | ... | ... | ... |

64 | 65 | 28.9059 | 1427 |

67 | 68 | 28.9059 | 1427 |

80 | 79 | 28.9059 | 1427 |

86 | 85 | 4.8216 | 253 |

67 | 115 | 246.9136 | 12992 |

179 rows Ã— 2 columns

\n", "