{ "cells": [ { "cell_type": "markdown", "id": "e590d662", "metadata": { "Collapsed": "false" }, "source": [ "(best-profit-basic-example)=\n", "# Marginal costs and optimal uploading" ] }, { "cell_type": "markdown", "id": "26eaefac", "metadata": { "Collapsed": "false" }, "source": [ "The model setup for this example is available in the following formats:\n", "\n", "- pyshop\n", " - [](bp-py)\n", " \n", "# Introduction\n", "\n", "The best profit (BP) functionality is the modern marginal cost and optimal [](plant) uploading functionality in SHOP. The theory is described in more detail in [the BP tutorial](best-profit)." ] }, { "cell_type": "code", "execution_count": 1, "id": "34372631", "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "#Necessary imports used in all examples\n", "import pandas as pd\n", "from pyshop import ShopSession\n", "import plotly.graph_objs as go\n", "pd.options.plotting.backend = \"plotly\"\n", "\n", "#Functions used in this example for building a tunnel model, adding a gate to a tunnel and running the optimization\n", "from bp import build_model, run_model" ] }, { "cell_type": "markdown", "id": "8e50c223", "metadata": {}, "source": [ "# Run a standard optimization\n", "\n", "First, a SHOP model with one [](plant) with two equal [generators](generator) and a second plant with one large and three small generators is built and optimized in the regular way:" ] }, { "cell_type": "code", "execution_count": 2, "id": "a09b25fc", "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "%3\n", "\n", "\n", "\n", "reservoir_Reservoir1\n", "\n", "Reservoir1\n", "\n", "\n", "\n", "plant_Plant1\n", "\n", "Plant1\n", "\n", "\n", "\n", "reservoir_Reservoir1->plant_Plant1\n", "\n", "\n", "\n", "\n", "reservoir_Reservoir2\n", "\n", "Reservoir2\n", "\n", "\n", "\n", "plant_Plant2\n", "\n", "Plant2\n", "\n", "\n", "\n", "reservoir_Reservoir2->plant_Plant2\n", "\n", "\n", "\n", "\n", "plant_Plant1->reservoir_Reservoir2\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "variable=Plant1_G1
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Create a standard ShopSession\n", "shop=ShopSession()\n", "#Build a simple model\n", "build_model(shop)\n", "#Optimize model by calling \"run_model\" in tunnel_model.py\n", "run_model(shop)\n", "#Display topology to the screen\n", "display(shop.model.build_connection_tree())\n", "#Display results for generator production\n", "pd.DataFrame([obj.production.get().rename(obj.get_name()) for obj in shop.model.generator]).transpose().plot.bar(title=\"Generator production\")" ] }, { "cell_type": "markdown", "id": "3277e52e", "metadata": {}, "source": [ "# Calculate best profit curves\n", "\n", "After the optimization is done, the [create bp_curves](create_bp_curves) command is used to create marginal cost, average cost, and best profit curves for both plants. The calculation is done for the 72 first hours, specified in the call to the command. The option \"production\" is given to signal that the production results from the optimization should be used as the reference production when calculating the BP curve." ] }, { "cell_type": "code", "execution_count": 3, "id": "1b74fcd1", "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "shop.create_bp_curves([\"production\"],[\"0\",\"71\"])" ] }, { "cell_type": "markdown", "id": "bbcd0176", "metadata": {}, "source": [ "The BP curves for each time step has now been calculated and saved to the [best_profit_mc](plant:best_profit_mc) attribute. The attribute is a [XYT](datatype:xyt) that holds the best profit curve for each time step where the x-values are the production levels and the y-values are the prices. The [ref_prod](plant:ref_prod) TXY shows the reference production for the plant in each time step. \n", "\n", "Several [](unit_combination) objects have been automatically created to hold the [marginal cost (MC) curves](unit_combination:marginal_cost) and [average cost (AC) curves](unit_combination:average_cost) of each generator combination that has been considered in the best profit calculation. Note that this includes the empty combination of no units running. The unit_combination objects are automatically connected to the plant object, and can be iterated over in pyshop as shown in the code below.\n", "\n", "The BP curves for each plant in all hours are shown in the figures below, in addition to the MC and AC curves of each unit combination. Use the slider to change which hour to display. The BP curves follow the MC curves for different unit_combinations and jumps between them given certain criteria of always having a positive regulation profit, see the [BP tutorial](best-profit)." ] }, { "cell_type": "code", "execution_count": 4, "id": "ee59e3ac", "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "width": 8 }, "mode": "lines+markers", "name": "BP curve", "type": "scatter", "x": [ 0.0, 99.99999999999986, 100.60606060606133, 101.21212121212139, 101.81818181818144, 102.42424242424292, 103.03030303030297, 103.63636363636445, 104.2424242424245, 104.84848484848456, 105.45454545454604, 106.06060606060609, 106.66666666666757, 188.3796525040452, 188.3796525040452, 189.3163600725941, 190.25306764114302, 191.18977520969193, 192.12648277823942, 193.06319034678833, 193.99989791533724, 194.93660548388615, 195.87331305243364, 196.81002062098256, 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