{ "cells": [ { "cell_type": "markdown", "id": "1c96cae7", "metadata": {}, "source": [ "(discrete-droop)=\n", "# Discretization of droop\n", "\n", "The model setup for the iterative discretization of droop results presented below is available in the following formats:\n", "\n", "- pyshop\n", " - [discrete_droop.py](discrete-droop-py)\n", "- YAML\n", " - [model.yaml](discrete-droop-model-yaml)\n", " - [reserve_obligation.yaml](discrete-droop-reserve-obligation-yaml)\n", " - [discrete_droop_input.yaml](discrete-droop-input-yaml)\n", "- ASCII\n", " - [model.ascii](discrete-droop-model-ascii)\n", " - [reserve_obligation.ascii](discrete-droop-reserve-obligation-ascii) \n", " - [discrete_droop_input.ascii](discrete-droop-input-ascii)" ] }, { "cell_type": "markdown", "id": "6557f7db", "metadata": {}, "source": [ "## Iterative droop discretization\n", "A simple system with 6 generators located on two separate plants will be used to illustrate the basic features of the droop discretization heuristic in SHOP. The droop variable is a continuous variable in SHOP, but there is usually a set of discrete droop values that can be implemented in the real world. The discrete droop functionality in SHOP enables the user to specify a set of discrete legal values that the droop variable in SHOP will be rounded and fixed to in the next iteration(s). This is a heuristic approach to the problem, since the droop variables are not formulated as discrete variables in the optimization problem. This would require the use of extra binary variables that will impact the tractability and calculation time of SHOP. Note that the droop discretization functionality requires the **SHOP_DISCRETE_DROOP** license. It is also possible to use the droop discretization functionality in combination with the **SHOP_SEPARATE_DROOP** license, which allows individual units to have separate droop settings for FCR-N, FCR-D-up, and FCR-D-down. \n", "\n", "First, we create and run the basic model without any droop discretization:" ] }, { "cell_type": "code", "execution_count": 1, "id": "f8011ec2", "metadata": {}, "outputs": [], "source": [ "#Necessary imports used in all examples\n", "import pandas as pd\n", "import plotly.graph_objs as go\n", "from pyshop import ShopSession\n", "\n", "#Functions used in this example for building and solving a simple model with cuts\n", "from discrete_droop import build_model, run_model, get_gen_droop" ] }, { "cell_type": "code", "execution_count": 2, "id": "a5550502", "metadata": {}, "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" } ], "source": [ "#Create a standard ShopSession\n", "shop=ShopSession()\n", "#Build a simple model with two reservoirs, two plants, and 6 generators.\n", "build_model(shop)\n", "#Display topology to the screen\n", "display(shop.model.build_connection_tree())\n", "\n", "#Add FCR_N obligation in both directions where all generators can participate\n", "fcr_group = shop.model.reserve_group.add_object(\"fcr_group\")\n", "fcr_group.fcr_n_up_obligation.set(20)\n", "fcr_group.fcr_n_down_obligation.set(20) \n", "\n", "for gen in shop.model.generator:\n", " fcr_group.connect_to(gen) \n", "\n", "#Run an optimization without any droop discretization\n", "run_model(shop)" ] }, { "cell_type": "markdown", "id": "21b24739", "metadata": {}, "source": [ "The resulting droop values can now be plotted. Note that the plotting below removes the time information form the droop values and plots them for the same x value:" ] }, { "cell_type": "code", "execution_count": 3, "id": "eb0f3604", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "black", "dash": "dash", "width": 0.5 }, "mode": "lines", "showlegend": false, "type": "scatter", "x": [ "Plant1_G1", "Plant1_G2", "Plant2_G1", "Plant2_G2", "Plant2_G3", "Plant2_G4" ], "y": [ 1, 1, 1, 1, 1, 1 ] }, { "line": { "color": "black", "dash": "dash", "width": 0.5 }, "mode": "lines", "showlegend": false, "type": "scatter", "x": [ "Plant1_G1", "Plant1_G2", "Plant2_G1", "Plant2_G2", "Plant2_G3", "Plant2_G4" ], "y": [ 2, 2, 2, 2, 2, 2 ] }, { "line": { "color": "black", "dash": "dash", "width": 0.5 }, "mode": "lines", "showlegend": false, "type": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Display the resulting droop values from the optimization\n", "\n", "#Retrieve the generator droop results\n", "gen_droop = get_gen_droop(shop)\n", "\n", "gen_names = [gen.get_name() for gen in shop.model.generator]\n", "\n", "fig = go.Figure(layout={'title':\"Droop values\",'xaxis_title':\"Generator\",'yaxis_title':\"Droop [%]\"})\n", "\n", "#Add dashed lines to show the integer values between 1 and 12\n", "for i in range(1,13):\n", " fig.add_trace(go.Scatter(showlegend=False,x=gen_names,y=[i]*len(gen_names),mode='lines', line={'color': \"black\", 'width': 0.5,'dash':\"dash\"})) \n", " \n", "for gen_name, droop in gen_droop.items():\n", " fig.add_trace(go.Scatter(showlegend=False,x=[gen_name]*len(droop),y=droop,mode='markers')) \n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "73ffe925", "metadata": {}, "source": [ "The optimized droop values shown above are not necessarily legal or practical to implement in the real world. It is possible to round down and fix the droop values to the closest legal discrete value in SHOP. Each unit can have its own list of legal droop values specified by the double_array attribute [discrete_droop_values](generator:discrete_droop_values). If no list is defined, the integers are assumed to be the legal values for the unit. The command [set droop_discretization_limit ](set_droop_discretization_limit), or the equivalent double attribute [droop_discretization_limit](global_settings:droop_discretization_limit) on the [global_settings object](global_settings), is used to tell SHOP to round *down* all droop values *below* the specified limit to their closest legal value, and fix the droop variable to this value in the next iteration. Since the values are always rounded down, the generators are forced to deliver more (or equal) frequency response compared to the optimal solution. This can lead to a more costly solution, but will not cause infeasibility issues. If a unit has no legal discrete values below the given droop_discretization_limit, the droop will not be fixed. The droop variables will also never be fixed to a value outside its upper and lower bounds. These bounds have default values of 1 and 12, respectively, and can be changed by specifying the [droop_min](generator:droop_min) and [droop_max](generator:droop_max) TXY unit attributes. If the rounded droop value is outside the defined limits, it is fixed to the boundary instead.\n", "\n", "Note that new attributes such as [fcr_d_down_discrete_droop_values](generator:fcr_d_down_discrete_droop_values) are available when using the separate droop functionality in combination with the discrete droop functionality. Building of separate droop variables for FCR-N, FCR-D-up, and FCR-D-down is activated by setting the integer attribute [separate_droop](generator:separate_droop) to 1 for the generator or pump.\n", "\n", "Now we create an identical SHOP model but add specified legal discrete droop values for the generators in Plant2. By gradually increasing the droop_discretization_limit between the incremental iterations, the droop values are iteratively fixed to legal values." ] }, { "cell_type": "code", "execution_count": 4, "id": "8ecbb487", "metadata": {}, "outputs": [], "source": [ "#Create a standard ShopSession\n", "shop=ShopSession()\n", "#Build a simple model with two reservoirs, two plants, and 6 generators.\n", "build_model(shop)\n", "\n", "#Add FCR_N obligation in both directions where all generators can participate\n", "fcr_group = shop.model.reserve_group.add_object(\"fcr_group\")\n", "fcr_group.fcr_n_up_obligation.set(20)\n", "fcr_group.fcr_n_down_obligation.set(20) \n", "\n", "for gen in shop.model.generator:\n", " fcr_group.connect_to(gen) \n", "\n", "#Set specific legal discrete droop values for generators in Plant2 (1,1.5,2,2.5,...,12)\n", "plant2 = shop.model.plant.Plant2\n", "for gen in plant2.generators:\n", " gen.discrete_droop_values.set([1+0.5*i for i in range(23)])\n", " \n", "#Run the full iterations and the first incremental iteration without any fixing and discretization\n", "shop.start_sim([], ['3'])\n", "shop.set_code(['incremental'], [])\n", "shop.start_sim([], ['1'])\n", "\n", "#Save the droop results before any fixing and discretization\n", "gen_droop = get_gen_droop(shop)\n", "droop_results = [gen_droop]\n", "\n", "#Gradually fix droop values for each following iteration\n", "for d in [3,6,9,12]:\n", " shop.set_droop_discretization_limit([],[d])\n", " shop.start_sim([], ['1'])\n", " \n", " #Save the droop results for each generator after each iteration\n", " gen_droop = get_gen_droop(shop) \n", " droop_results.append(gen_droop)" ] }, { "cell_type": "markdown", "id": "4f82fc56", "metadata": {}, "source": [ "Now we can plot the evolution of the droop results for each generator for the four final incremental iterations:" ] }, { "cell_type": "code", "execution_count": 5, "id": "c1970e8d", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": 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"white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Droop values fixed below 12" }, "xaxis": { "title": { "text": "Generator" } }, "yaxis": { "title": { "text": "Droop [%]" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for desc,gen_droop in zip([\"before fixing\",\"fixed below 3\",\"fixed below 6\",\"fixed below 9\",\"fixed below 12\"], droop_results):\n", "\n", " fig = go.Figure(layout={'title':f\"Droop values {desc}\",'xaxis_title':\"Generator\",'yaxis_title':\"Droop [%]\"})\n", "\n", " for i in range(1,13):\n", " fig.add_trace(go.Scatter(showlegend=False,x=gen_names,y=[i]*len(gen_names),mode='lines', line={'color': \"black\", 'width': 0.5,'dash':\"dash\"})) \n", "\n", " for i in range(11):\n", " fig.add_trace(go.Scatter(showlegend=False,x=[\"Plant2_G1\",\"Plant2_G4\"],y=[1.5+i,1.5+i],mode='lines', line={'color': \"red\", 'width': 0.5,'dash':\"dash\"})) \n", "\n", " \n", " for gen_name,droop_values in gen_droop.items():\n", " fig.add_trace(go.Scatter(showlegend=False,x=[gen_name]*len(droop_values),y=droop_values,mode='markers')) \n", "\n", " fig.show()" ] }, { "cell_type": "markdown", "id": "e00bb6d3", "metadata": {}, "source": [ "Since the default upper bound for the droop is 12 in SHOP, all droop values are rounded and fixed when the discretization limit is set to 12 before the final iteration. The droop values of the generators in Plant1 are all fixed to integer values in the end, while the generators in Plant2 are allowed to take values defined earlier with the discrete_droop_values attribute." ] }, { "cell_type": "markdown", "id": "7fa789db", "metadata": {}, "source": [ "(discrete-droop-py)=\n", "## discrete_droop.py" ] }, { "cell_type": "code", "execution_count": 6, "id": "07a5ea9f", "metadata": { "Collapsed": "false", "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "import pandas as pd\n", "import numpy as np\n", "\n", "def build_model(shop):\n", " starttime = pd.Timestamp('2018-01-23 00:00:00')\n", " endtime = pd.Timestamp('2018-01-26')\n", " shop.set_time_resolution(starttime=starttime, endtime=endtime, timeunit=\"hour\", timeresolution=pd.Series(index=[starttime],data=[1]))\n", " \n", " rsv1 = shop.model.reservoir.add_object('Reservoir1')\n", " rsv1.max_vol.set(39)\n", " rsv1.lrl.set(860)\n", " rsv1.hrl.set(905)\n", " rsv1.vol_head.set(pd.Series([860, 906, 907], index=[0, 39, 41.66], name=0)) \n", "\n", " rsv2 = shop.model.reservoir.add_object('Reservoir2')\n", " rsv2.max_vol.set(97.5) \n", " rsv2.lrl.set(650) \n", " rsv2.hrl.set(679) \n", " rsv2.vol_head.set(pd.Series([650, 679, 680], index=[0, 97.5, 104.15], name=0))\n", " \n", " plant1 = shop.model.plant.add_object('Plant1')\n", " plant1.outlet_line.set(672)\n", " plant1.main_loss.set([0])\n", " plant1.penstock_loss.set([0.001])\n", " plant1.mip_flag.set(1)\n", " for gen_no in range(2):\n", " gen=shop.model.generator.add_object(f\"{plant1.get_name()}_G{str(gen_no+1)}\")\n", " gen.connect_to(plant1)\n", " gen.penstock.set(1)\n", " gen.p_min.set(60)\n", " gen.p_max.set(120)\n", " gen.p_nom.set(120)\n", " gen.startcost.set(300)\n", " gen.gen_eff_curve.set(pd.Series([100, 100], index=[60, 120]))\n", " gen.turb_eff_curves.set([pd.Series([85.8733, 87.0319, 88.0879, 89.0544, 89.9446, 90.7717, 91.5488, 92.2643, 92.8213, 93.1090, 93.2170, 93.0390, 92.6570, 92.1746],\n", " index=[28.12, 30.45, 32.78, 35.11, 37.45, 39.78, 42.11, 44.44, 46.77, 49.10, 51.43, 53.76, 56.10, 58.83],\n", " name=170),\n", " pd.Series([86.7321, 87.9022, 88.9688, 89.9450, 90.8441, 91.6794, 92.4643, 93.1870, 93.7495, 94.0401, 94.1492, 93.9694, 93.5836, 93.0964],\n", " index=[28.12, 30.45, 32.78, 35.11, 37.45, 39.78, 42.11, 44.44, 46.77, 49.10, 51.43, 53.76, 56.10, 58.83],\n", " name=200),\n", " pd.Series([87.5908, 88.7725, 89.8497, 90.8355, 91.7435, 92.5871, 93.3798, 94.1096, 94.6777, 94.9712, 95.0813, 94.8998, 94.5101, 94.0181],\n", " index=[28.12, 30.45, 32.78, 35.11, 37.45, 39.78, 42.11, 44.44, 46.77, 49.10, 51.43, 53.76, 56.10, 58.83],\n", " name=230)])\n", "\n", " plant2 = shop.model.plant.add_object('Plant2')\n", " plant2.outlet_line.set(586)\n", " plant2.main_loss.set([0])\n", " plant2.penstock_loss.set([0.0001,0.0002])\n", " plant2.mip_flag.set(1)\n", " for gen_no in range(4):\n", " gen=shop.model.generator.add_object(f\"{plant2.get_name()}_G{str(gen_no+1)}\")\n", " gen.connect_to(plant2)\n", " if gen_no == 0:\n", " gen.penstock.set(1)\n", " gen.p_min.set(100)\n", " gen.p_max.set(180)\n", " gen.p_nom.set(180)\n", " gen.startcost.set(300)\n", " gen.gen_eff_curve.set(pd.Series([100, 100], index=[100, 180]))\n", " gen.turb_eff_curves.set([pd.Series([92.7201, 93.2583, 93.7305, 94.1368, 94.4785, 94.7525, 94.9606, 95.1028, 95.1790, 95.1892, 95.1335, 95.0118, 94.8232, 94.5191],\n", " index=[126.54, 137.03, 147.51, 158.00, 168.53, 179.01, 189.50, 199.98, 210.47, 220.95, 231.44, 241.92, 252.45, 264.74],\n", " name=60)])\n", " else:\n", " gen.penstock.set(2)\n", " gen.p_min.set(30)\n", " gen.p_max.set(55)\n", " gen.p_nom.set(55)\n", " gen.startcost.set(300)\n", " gen.gen_eff_curve.set(pd.Series([100, 100], index=[30, 55]))\n", " gen.turb_eff_curves.set([pd.Series([83.8700, 85.1937, 86.3825, 87.4362, 88.3587, 89.1419, 89.7901, 90.3033, 90.6815, 90.9248, 91.0331, 91.0063, 90.8436, 90.4817],\n", " index=[40.82, 44.20, 47.58, 50.97, 54.36, 57.75, 61.13, 64.51, 67.89, 71.27, 74.66, 78.04, 81.44, 85.40],\n", " name=60)]) \n", " \n", " rsv1.connect_to(plant1)\n", " plant1.connect_to(rsv2)\n", " rsv2.connect_to(plant2)\n", " \n", " rsv1.start_head.set(900)\n", " rsv2.start_head.set(670) \n", " \n", " rsv1.energy_value_input.set(30)\n", " rsv2.energy_value_input.set(10)\n", " \n", " rsv2.inflow.set(pd.Series([60], [starttime]))\n", " \n", " da = shop.model.market.add_object('Day_ahead')\n", " da.sale_price.set(pd.DataFrame([32.992,31.122,29.312,28.072,30.012,33.362,42.682,74.822,77.732,62.332,55.892,46.962,42.582,40.942,39.212,39.142,41.672,46.922,37.102,32.992,31.272,29.752,28.782,28.082,27.242,26.622,25.732,25.392,25.992,27.402,28.942,32.182,33.082,32.342,30.912,30.162,30.062,29.562,29.462,29.512,29.672,30.072,29.552,28.862,28.412,28.072,27.162,25.502,26.192,25.222,24.052,23.892,23.682,26.092,28.202,30.902,31.572,31.462,31.172,30.912,30.572,30.602,30.632,31.062,32.082,36.262,34.472,32.182,31.492,30.732,29.712,28.982], \n", " index=[starttime + pd.Timedelta(hours=i) for i in range(0,72)]))\n", " da.max_sale.set(pd.Series([9999], [starttime]))\n", " da.buy_price.set(da.sale_price.get()+0.002)\n", " da.max_buy.set(pd.Series([9999], [starttime]))\n", "\n", " settings = shop.model.global_settings.global_settings\n", " settings.mipgap_rel.set(0)\n", " settings.mipgap_abs.set(0)\n", "\n", "def run_model(shop):\n", " shop.start_sim([], ['3'])\n", " shop.set_code(['incremental'], [])\n", " shop.start_sim([], ['5'])\n", "\n", "\n", "def get_gen_droop(shop):\n", "\n", " gen_droop = {}\n", " for gen in shop.model.generator:\n", " droop = gen.droop_result.get() \n", " gen_droop[gen.get_name()] = droop.values\n", " \n", " return gen_droop\n" ] } ], "source": [ "with open('discrete_droop.py', 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "id": "ceafd616", "metadata": {}, "source": [ "(discrete-droop-model-yaml)=\n", "## model.yaml" ] }, { "cell_type": "code", "execution_count": 7, "id": "032a480e", "metadata": { "Collapsed": "false", "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time:\n", " starttime: 2018-01-23 00:00:00\n", " endtime: 2018-01-26 00:00:00\n", " timeunit: hour\n", " timeresolution:\n", " 2018-01-23 00:00:00: 1\n", "model:\n", " global_settings:\n", " global_settings:\n", " mipgap_rel: 0\n", " mipgap_abs: 0\n", " reservoir:\n", " Reservoir1:\n", " max_vol: 39\n", " lrl: 860\n", " hrl: 905\n", " vol_head:\n", " ref: 0\n", " x:\n", " - 0\n", " - 39\n", " - 41.66\n", " y:\n", " - 860\n", " - 906\n", " - 907\n", " start_head: 900\n", " energy_value_input: 30\n", " Reservoir2:\n", " max_vol: 97.5\n", " lrl: 650\n", " hrl: 679\n", " vol_head:\n", " ref: 0\n", " x:\n", " - 0\n", " - 97.5\n", " - 104.15\n", " y:\n", " - 650\n", " - 679\n", " - 680\n", " start_head: 670\n", " inflow:\n", " 2018-01-23 00:00:00: 60\n", " energy_value_input: 10\n", " plant:\n", " Plant1:\n", " outlet_line: 672\n", " main_loss:\n", " - 0\n", " penstock_loss:\n", " - 0.001\n", " mip_flag:\n", " 2018-01-23 00:00:00: 1\n", " Plant2:\n", " outlet_line: 586\n", " main_loss:\n", " - 0\n", " penstock_loss:\n", " - 0.0001\n", " - 0.0002\n", " mip_flag:\n", " 2018-01-23 00:00:00: 1\n", " generator:\n", " Plant1_G1:\n", " penstock: 1\n", " p_min: 60\n", " p_max: 120\n", " p_nom: 120\n", " gen_eff_curve:\n", " ref: 0\n", " x:\n", " - 60\n", " - 120\n", " y:\n", " - 100\n", " - 100\n", " turb_eff_curves:\n", " - ref: 170\n", " x:\n", " - 28.12\n", " - 30.45\n", " - 32.78\n", " - 35.11\n", " - 37.45\n", " - 39.78\n", " - 42.11\n", " - 44.44\n", " - 46.77\n", " - 49.1\n", " - 51.43\n", " - 53.76\n", " - 56.1\n", " - 58.83\n", " y:\n", " - 85.8733\n", " - 87.0319\n", " - 88.0879\n", " - 89.0544\n", " - 89.9446\n", " - 90.7717\n", " - 91.5488\n", " - 92.2643\n", " - 92.8213\n", " - 93.109\n", " - 93.217\n", " - 93.039\n", " - 92.657\n", " - 92.1746\n", " - ref: 200\n", " x:\n", " - 28.12\n", " - 30.45\n", " - 32.78\n", " - 35.11\n", " - 37.45\n", " - 39.78\n", " - 42.11\n", " - 44.44\n", " - 46.77\n", " - 49.1\n", " - 51.43\n", " - 53.76\n", " - 56.1\n", " - 58.83\n", " y:\n", " - 86.7321\n", " - 87.9022\n", " - 88.9688\n", " - 89.945\n", " - 90.8441\n", " - 91.6794\n", " - 92.4643\n", " - 93.187\n", " - 93.7495\n", " - 94.0401\n", " - 94.1492\n", " - 93.9694\n", " - 93.5836\n", " - 93.0964\n", " - ref: 230\n", " x:\n", " - 28.12\n", " - 30.45\n", " - 32.78\n", " - 35.11\n", " - 37.45\n", " - 39.78\n", " - 42.11\n", " - 44.44\n", " - 46.77\n", " - 49.1\n", " - 51.43\n", " - 53.76\n", " - 56.1\n", " - 58.83\n", " y:\n", " - 87.5908\n", " - 88.7725\n", " - 89.8497\n", " - 90.8355\n", " - 91.7435\n", " - 92.5871\n", " - 93.3798\n", " - 94.1096\n", " - 94.6777\n", " - 94.9712\n", " - 95.0813\n", " - 94.8998\n", " - 94.5101\n", " - 94.0181\n", " startcost:\n", " 2018-01-23 00:00:00: 300\n", " Plant1_G2:\n", " penstock: 1\n", " p_min: 60\n", " p_max: 120\n", " p_nom: 120\n", " gen_eff_curve:\n", " ref: 0\n", " x:\n", " - 60\n", " - 120\n", " y:\n", " - 100\n", " - 100\n", " turb_eff_curves:\n", " - ref: 170\n", " x:\n", " - 28.12\n", " - 30.45\n", " - 32.78\n", " - 35.11\n", " - 37.45\n", " - 39.78\n", " - 42.11\n", " - 44.44\n", " - 46.77\n", " - 49.1\n", " - 51.43\n", " - 53.76\n", " - 56.1\n", " - 58.83\n", " y:\n", " - 85.8733\n", " - 87.0319\n", " - 88.0879\n", " - 89.0544\n", " - 89.9446\n", " - 90.7717\n", " - 91.5488\n", " - 92.2643\n", " - 92.8213\n", " - 93.109\n", " - 93.217\n", " - 93.039\n", " - 92.657\n", " - 92.1746\n", " - ref: 200\n", " x:\n", " - 28.12\n", " - 30.45\n", " - 32.78\n", " - 35.11\n", " - 37.45\n", " - 39.78\n", " - 42.11\n", " - 44.44\n", " - 46.77\n", " - 49.1\n", " - 51.43\n", " - 53.76\n", " - 56.1\n", " - 58.83\n", " y:\n", " - 86.7321\n", " - 87.9022\n", " - 88.9688\n", " - 89.945\n", " - 90.8441\n", " - 91.6794\n", " - 92.4643\n", " - 93.187\n", " - 93.7495\n", " - 94.0401\n", " - 94.1492\n", " - 93.9694\n", " - 93.5836\n", " - 93.0964\n", " - ref: 230\n", " x:\n", " - 28.12\n", " - 30.45\n", " - 32.78\n", " - 35.11\n", " - 37.45\n", " - 39.78\n", " - 42.11\n", " - 44.44\n", " - 46.77\n", " - 49.1\n", " - 51.43\n", " - 53.76\n", " - 56.1\n", " - 58.83\n", " y:\n", " - 87.5908\n", " - 88.7725\n", " - 89.8497\n", " - 90.8355\n", " - 91.7435\n", " - 92.5871\n", " - 93.3798\n", " - 94.1096\n", " - 94.6777\n", " - 94.9712\n", " - 95.0813\n", " - 94.8998\n", " - 94.5101\n", " - 94.0181\n", " startcost:\n", " 2018-01-23 00:00:00: 300\n", " Plant2_G1:\n", " penstock: 1\n", " p_min: 100\n", " p_max: 180\n", " p_nom: 180\n", " gen_eff_curve:\n", " ref: 0\n", " x:\n", " - 100\n", " - 180\n", " y:\n", " - 100\n", " - 100\n", " turb_eff_curves:\n", " - ref: 60\n", " x:\n", " - 126.54\n", " - 137.03\n", " - 147.51\n", " - 158\n", " - 168.53\n", " - 179.01\n", " - 189.5\n", " - 199.98\n", " - 210.47\n", " - 220.95\n", " - 231.44\n", " - 241.92\n", " - 252.45\n", " - 264.74\n", " y:\n", " - 92.7201\n", " - 93.2583\n", " - 93.7305\n", " - 94.1368\n", " - 94.4785\n", " - 94.7525\n", " - 94.9606\n", " - 95.1028\n", " - 95.179\n", " - 95.1892\n", " - 95.1335\n", " - 95.0118\n", " - 94.8232\n", " - 94.5191\n", " startcost:\n", " 2018-01-23 00:00:00: 300\n", " Plant2_G2:\n", " penstock: 2\n", " p_min: 30\n", " p_max: 55\n", " p_nom: 55\n", " gen_eff_curve:\n", " ref: 0\n", " x:\n", " - 30\n", " - 55\n", " y:\n", " - 100\n", " - 100\n", " turb_eff_curves:\n", " - ref: 60\n", " x:\n", " - 40.82\n", " - 44.2\n", " - 47.58\n", " - 50.97\n", " - 54.36\n", " - 57.75\n", " - 61.13\n", " - 64.51\n", " - 67.89\n", " - 71.27\n", " - 74.66\n", " - 78.04\n", " - 81.44\n", " - 85.4\n", " y:\n", " - 83.87\n", " - 85.1937\n", " - 86.3825\n", " - 87.4362\n", " - 88.3587\n", " - 89.1419\n", " - 89.7901\n", " - 90.3033\n", " - 90.6815\n", " - 90.9248\n", " - 91.0331\n", " - 91.0063\n", " - 90.8436\n", " - 90.4817\n", " startcost:\n", " 2018-01-23 00:00:00: 300\n", " Plant2_G3:\n", " penstock: 2\n", " p_min: 30\n", " p_max: 55\n", " p_nom: 55\n", " gen_eff_curve:\n", " ref: 0\n", " x:\n", " - 30\n", " - 55\n", " y:\n", " - 100\n", " - 100\n", " turb_eff_curves:\n", " - ref: 60\n", " x:\n", " - 40.82\n", " - 44.2\n", " - 47.58\n", " - 50.97\n", " - 54.36\n", " - 57.75\n", " - 61.13\n", " - 64.51\n", " - 67.89\n", " - 71.27\n", " - 74.66\n", " - 78.04\n", " - 81.44\n", " - 85.4\n", " y:\n", " - 83.87\n", " - 85.1937\n", " - 86.3825\n", " - 87.4362\n", " - 88.3587\n", " - 89.1419\n", " - 89.7901\n", " - 90.3033\n", " - 90.6815\n", " - 90.9248\n", " - 91.0331\n", " - 91.0063\n", " - 90.8436\n", " - 90.4817\n", " startcost:\n", " 2018-01-23 00:00:00: 300\n", " Plant2_G4:\n", " penstock: 2\n", " p_min: 30\n", " p_max: 55\n", " p_nom: 55\n", " gen_eff_curve:\n", " ref: 0\n", " x:\n", " - 30\n", " - 55\n", " y:\n", " - 100\n", " - 100\n", " turb_eff_curves:\n", " - ref: 60\n", " x:\n", " - 40.82\n", " - 44.2\n", " - 47.58\n", " - 50.97\n", " - 54.36\n", " - 57.75\n", " - 61.13\n", " - 64.51\n", " - 67.89\n", " - 71.27\n", " - 74.66\n", " - 78.04\n", " - 81.44\n", " - 85.4\n", " y:\n", " - 83.87\n", " - 85.1937\n", " - 86.3825\n", " - 87.4362\n", " - 88.3587\n", " - 89.1419\n", " - 89.7901\n", " - 90.3033\n", " - 90.6815\n", " - 90.9248\n", " - 91.0331\n", " - 91.0063\n", " - 90.8436\n", " - 90.4817\n", " startcost:\n", " 2018-01-23 00:00:00: 300\n", " market:\n", " Day_ahead:\n", " market_type: ENERGY\n", " max_buy:\n", " 2018-01-23 00:00:00: 9999\n", " max_sale:\n", " 2018-01-23 00:00:00: 9999\n", " buy_price:\n", " 2018-01-23 00:00:00: 32.994\n", " 2018-01-23 01:00:00: 31.124\n", " 2018-01-23 02:00:00: 29.314\n", " 2018-01-23 03:00:00: 28.074\n", " 2018-01-23 04:00:00: 30.014\n", " 2018-01-23 05:00:00: 33.364\n", " 2018-01-23 06:00:00: 42.684\n", " 2018-01-23 07:00:00: 74.824\n", " 2018-01-23 08:00:00: 77.734\n", " 2018-01-23 09:00:00: 62.334\n", " 2018-01-23 10:00:00: 55.894\n", " 2018-01-23 11:00:00: 46.964\n", " 2018-01-23 12:00:00: 42.584\n", " 2018-01-23 13:00:00: 40.944\n", " 2018-01-23 14:00:00: 39.214\n", " 2018-01-23 15:00:00: 39.144\n", " 2018-01-23 16:00:00: 41.674\n", " 2018-01-23 17:00:00: 46.924\n", " 2018-01-23 18:00:00: 37.104\n", " 2018-01-23 19:00:00: 32.994\n", " 2018-01-23 20:00:00: 31.274\n", " 2018-01-23 21:00:00: 29.754\n", " 2018-01-23 22:00:00: 28.784\n", " 2018-01-23 23:00:00: 28.084\n", " 2018-01-24 00:00:00: 27.244\n", " 2018-01-24 01:00:00: 26.624\n", " 2018-01-24 02:00:00: 25.734\n", " 2018-01-24 03:00:00: 25.394\n", " 2018-01-24 04:00:00: 25.994\n", " 2018-01-24 05:00:00: 27.404\n", " 2018-01-24 06:00:00: 28.944\n", " 2018-01-24 07:00:00: 32.184\n", " 2018-01-24 08:00:00: 33.084\n", " 2018-01-24 09:00:00: 32.344\n", " 2018-01-24 10:00:00: 30.914\n", " 2018-01-24 11:00:00: 30.164\n", " 2018-01-24 12:00:00: 30.064\n", " 2018-01-24 13:00:00: 29.564\n", " 2018-01-24 14:00:00: 29.464\n", " 2018-01-24 15:00:00: 29.514\n", " 2018-01-24 16:00:00: 29.674\n", " 2018-01-24 17:00:00: 30.074\n", " 2018-01-24 18:00:00: 29.554\n", " 2018-01-24 19:00:00: 28.864\n", " 2018-01-24 20:00:00: 28.414\n", " 2018-01-24 21:00:00: 28.074\n", " 2018-01-24 22:00:00: 27.164\n", " 2018-01-24 23:00:00: 25.504\n", " 2018-01-25 00:00:00: 26.194\n", " 2018-01-25 01:00:00: 25.224\n", " 2018-01-25 02:00:00: 24.054\n", " 2018-01-25 03:00:00: 23.894\n", " 2018-01-25 04:00:00: 23.684\n", " 2018-01-25 05:00:00: 26.094\n", " 2018-01-25 06:00:00: 28.204\n", " 2018-01-25 07:00:00: 30.904\n", " 2018-01-25 08:00:00: 31.574\n", " 2018-01-25 09:00:00: 31.464\n", " 2018-01-25 10:00:00: 31.174\n", " 2018-01-25 11:00:00: 30.914\n", " 2018-01-25 12:00:00: 30.574\n", " 2018-01-25 13:00:00: 30.604\n", " 2018-01-25 14:00:00: 30.634\n", " 2018-01-25 15:00:00: 31.064\n", " 2018-01-25 16:00:00: 32.084\n", " 2018-01-25 17:00:00: 36.264\n", " 2018-01-25 18:00:00: 34.474\n", " 2018-01-25 19:00:00: 32.184\n", " 2018-01-25 20:00:00: 31.494\n", " 2018-01-25 21:00:00: 30.734\n", " 2018-01-25 22:00:00: 29.714\n", " 2018-01-25 23:00:00: 28.984\n", " sale_price:\n", " 2018-01-23 00:00:00: 32.992\n", " 2018-01-23 01:00:00: 31.122\n", " 2018-01-23 02:00:00: 29.312\n", " 2018-01-23 03:00:00: 28.072\n", " 2018-01-23 04:00:00: 30.012\n", " 2018-01-23 05:00:00: 33.362\n", " 2018-01-23 06:00:00: 42.682\n", " 2018-01-23 07:00:00: 74.822\n", " 2018-01-23 08:00:00: 77.732\n", " 2018-01-23 09:00:00: 62.332\n", " 2018-01-23 10:00:00: 55.892\n", " 2018-01-23 11:00:00: 46.962\n", " 2018-01-23 12:00:00: 42.582\n", " 2018-01-23 13:00:00: 40.942\n", " 2018-01-23 14:00:00: 39.212\n", " 2018-01-23 15:00:00: 39.142\n", " 2018-01-23 16:00:00: 41.672\n", " 2018-01-23 17:00:00: 46.922\n", " 2018-01-23 18:00:00: 37.102\n", " 2018-01-23 19:00:00: 32.992\n", " 2018-01-23 20:00:00: 31.272\n", " 2018-01-23 21:00:00: 29.752\n", " 2018-01-23 22:00:00: 28.782\n", " 2018-01-23 23:00:00: 28.082\n", " 2018-01-24 00:00:00: 27.242\n", " 2018-01-24 01:00:00: 26.622\n", " 2018-01-24 02:00:00: 25.732\n", " 2018-01-24 03:00:00: 25.392\n", " 2018-01-24 04:00:00: 25.992\n", " 2018-01-24 05:00:00: 27.402\n", " 2018-01-24 06:00:00: 28.942\n", " 2018-01-24 07:00:00: 32.182\n", " 2018-01-24 08:00:00: 33.082\n", " 2018-01-24 09:00:00: 32.342\n", " 2018-01-24 10:00:00: 30.912\n", " 2018-01-24 11:00:00: 30.162\n", " 2018-01-24 12:00:00: 30.062\n", " 2018-01-24 13:00:00: 29.562\n", " 2018-01-24 14:00:00: 29.462\n", " 2018-01-24 15:00:00: 29.512\n", " 2018-01-24 16:00:00: 29.672\n", " 2018-01-24 17:00:00: 30.072\n", " 2018-01-24 18:00:00: 29.552\n", " 2018-01-24 19:00:00: 28.862\n", " 2018-01-24 20:00:00: 28.412\n", " 2018-01-24 21:00:00: 28.072\n", " 2018-01-24 22:00:00: 27.162\n", " 2018-01-24 23:00:00: 25.502\n", " 2018-01-25 00:00:00: 26.192\n", " 2018-01-25 01:00:00: 25.222\n", " 2018-01-25 02:00:00: 24.052\n", " 2018-01-25 03:00:00: 23.892\n", " 2018-01-25 04:00:00: 23.682\n", " 2018-01-25 05:00:00: 26.092\n", " 2018-01-25 06:00:00: 28.202\n", " 2018-01-25 07:00:00: 30.902\n", " 2018-01-25 08:00:00: 31.572\n", " 2018-01-25 09:00:00: 31.462\n", " 2018-01-25 10:00:00: 31.172\n", " 2018-01-25 11:00:00: 30.912\n", " 2018-01-25 12:00:00: 30.572\n", " 2018-01-25 13:00:00: 30.602\n", " 2018-01-25 14:00:00: 30.632\n", " 2018-01-25 15:00:00: 31.062\n", " 2018-01-25 16:00:00: 32.082\n", " 2018-01-25 17:00:00: 36.262\n", " 2018-01-25 18:00:00: 34.472\n", " 2018-01-25 19:00:00: 32.182\n", " 2018-01-25 20:00:00: 31.492\n", " 2018-01-25 21:00:00: 30.732\n", " 2018-01-25 22:00:00: 29.712\n", " 2018-01-25 23:00:00: 28.982\n", "connections:\n", " - from: Plant1\n", " to: Reservoir2\n", " - from: Reservoir1\n", " to: Plant1\n", " - from: Plant1_G1\n", " to: Plant1\n", " - from: Plant1_G2\n", " to: Plant1\n", " - from: Reservoir2\n", " to: Plant2\n", " - from: Plant2_G1\n", " to: Plant2\n", " - from: Plant2_G2\n", " to: Plant2\n", " - from: Plant2_G3\n", " to: Plant2\n", " - from: Plant2_G4\n", " to: Plant2\n", "\n" ] } ], "source": [ "with open('model.yaml', 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "id": "4e2d305b", "metadata": {}, "source": [ "(discrete-droop-reserve-obligation-yaml)=\n", "## reserve_obligation.yaml" ] }, { "cell_type": "code", "execution_count": 8, "id": "cb160677", "metadata": { "Collapsed": "false", "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model:\n", " reserve_group:\n", " fcr_group:\n", " fcr_n_up_obligation:\n", " 2018-01-23 00:00:00: 20\n", " fcr_n_down_obligation:\n", " 2018-01-23 00:00:00: 20\n", "connections:\n", " - from: fcr_group\n", " to: Plant1_G1\n", " - from: fcr_group\n", " to: Plant1_G2\n", " - from: fcr_group\n", " to: Plant2_G1\n", " - from: fcr_group\n", " to: Plant2_G2\n", " - from: fcr_group\n", " to: Plant2_G3\n", " - from: fcr_group\n", " to: Plant2_G4\n", "\n" ] } ], "source": [ "with open('reserve_obligation.yaml', 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "id": "2127df45", "metadata": {}, "source": [ "(discrete-droop-input-yaml)=\n", "## discrete_droop_input.yaml" ] }, { "cell_type": "code", "execution_count": 9, "id": "bf9e231f", "metadata": { "Collapsed": "false", "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model:\n", " generator:\n", " Plant2_G1:\n", " discrete_droop_values:\n", " - 1\n", " - 1.5\n", " - 2\n", " - 2.5\n", " - 3\n", " - 3.5\n", " - 4\n", " - 4.5\n", " - 5\n", " - 5.5\n", " - 6\n", " - 6.5\n", " - 7\n", " - 7.5\n", " - 8\n", " - 8.5\n", " - 9\n", " - 9.5\n", " - 10\n", " - 10.5\n", " - 11\n", " - 11.5\n", " - 12\n", " Plant2_G2:\n", " discrete_droop_values:\n", " - 1\n", " - 1.5\n", " - 2\n", " - 2.5\n", " - 3\n", " - 3.5\n", " - 4\n", " - 4.5\n", " - 5\n", " - 5.5\n", " - 6\n", " - 6.5\n", " - 7\n", " - 7.5\n", " - 8\n", " - 8.5\n", " - 9\n", " - 9.5\n", " - 10\n", " - 10.5\n", " - 11\n", " - 11.5\n", " - 12\n", " Plant2_G3:\n", " discrete_droop_values:\n", " - 1\n", " - 1.5\n", " - 2\n", " - 2.5\n", " - 3\n", " - 3.5\n", " - 4\n", " - 4.5\n", " - 5\n", " - 5.5\n", " - 6\n", " - 6.5\n", " - 7\n", " - 7.5\n", " - 8\n", " - 8.5\n", " - 9\n", " - 9.5\n", " - 10\n", " - 10.5\n", " - 11\n", " - 11.5\n", " - 12\n", " Plant2_G4: \n", " discrete_droop_values:\n", " - 1\n", " - 1.5\n", " - 2\n", " - 2.5\n", " - 3\n", " - 3.5\n", " - 4\n", " - 4.5\n", " - 5\n", " - 5.5\n", " - 6\n", " - 6.5\n", " - 7\n", " - 7.5\n", " - 8\n", " - 8.5\n", " - 9\n", " - 9.5\n", " - 10\n", " - 10.5\n", " - 11\n", " - 11.5\n", " - 12\n" ] } ], "source": [ "with open('discrete_droop_input.yaml', 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "id": "af1ac081", "metadata": {}, "source": [ "(discrete-droop-model-ascii)=\n", "## model.ascii" ] }, { "cell_type": "code", "execution_count": 10, "id": "ccc2ee24", "metadata": { "Collapsed": "false", "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#;\n", "# Name of the datafile is: model.ascii;\n", "#;\n", "\n", "SIZE\n", "#Num_reservoirs;Num_plants;Num_gates;Num_junctions\n", "2 2 0 0\n", "\n", "OPTIMIZATION time\n", "#Start_time; End_time;\n", "20180123000000 20180126000000\n", "\n", "#;N_full_iterations;Accuracy;\n", "OPTIMIZATION 1000 1.00\n", "#Time resolution in the optimization;\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 NO_UNIT 73\n", "# Time; f(t);\n", "20180123000000000 1.00000000000000000\n", "\n", "GLOBAL_SETTINGS mipgap_rel\n", "# relative mipgap for the optimization;\n", "0.000000\n", "\n", "GLOBAL_SETTINGS mipgap_abs\n", "# absolute mipgap_unit for the optimization;\n", "0.000000\n", "\n", "RESERVOIR attributes Reservoir1\n", "#ID;Water_course;Type;Maxvol;Lrl;Hrl;\n", "0 0 0 39.000 860.000 905.000\n", "\n", "RESERVOIR vol_head Reservoir1\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 3 MM3 METER\n", "# x_value; y_value;\n", "0.0000000000 860.0000000000\n", "39.0000000000 906.0000000000\n", "41.6600000000 907.0000000000\n", "\n", "RESERVOIR energy_value_input Reservoir1\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 1 MM3 NOK/MWH\n", "# x_value; y_value;\n", "0.0000000000 30.0000000000\n", "\n", "RESERVOIR attributes Reservoir2\n", "#ID;Water_course;Type;Maxvol;Lrl;Hrl;\n", "0 0 0 97.500 650.000 679.000\n", "\n", "RESERVOIR vol_head Reservoir2\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 3 MM3 METER\n", "# x_value; y_value;\n", "0.0000000000 650.0000000000\n", "97.5000000000 679.0000000000\n", "104.1500000000 680.0000000000\n", "\n", "RESERVOIR inflow Reservoir2\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 M3/S 73\n", "# Time; f(t);\n", "20180123000000000 60.00000000000000000\n", "\n", "RESERVOIR energy_value_input Reservoir2\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 1 MM3 NOK/MWH\n", "# x_value; y_value;\n", "0.0000000000 10.0000000000\n", "\n", "PLANT attributes Plant1\n", "#Id;Water_course;Type;Future_use;Prod_area;Num_gen;Num_pump;\n", "0 0 6 0 1 2 0\n", "#Num_main_seg;Num_penstock;Time_delay;Read_prod_factor;Outlet_line;\n", "1 1 0 0.000000 672.000000\n", "0.000000\n", "0.001000\n", "\n", "PLANT mip_flag Plant1\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 NO_UNIT 73\n", "# Time; f(t);\n", "20180123000000000 1.00000000000000000\n", "\n", "GENERATOR attributes Plant1 1\n", "#Id Type Penstock Nom_prod Min_prod Max_prod Start_cost\n", "0 7 1 120.00 60.00 120.00 300.00\n", "\n", "GENERATOR gen_eff_curve Plant1 1\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 2 MW %\n", "# x_value; y_value;\n", "60.0000000000 100.0000000000\n", "120.0000000000 100.0000000000\n", "\n", "GENERATOR turb_eff_curves Plant1 1\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 170.00 14 M3/S %\n", "# x_value; y_value;\n", "28.1200000000 85.8733000000\n", "30.4500000000 87.0319000000\n", "32.7800000000 88.0879000000\n", "35.1100000000 89.0544000000\n", "37.4500000000 89.9446000000\n", "39.7800000000 90.7717000000\n", "42.1100000000 91.5488000000\n", "44.4400000000 92.2643000000\n", "46.7700000000 92.8213000000\n", "49.1000000000 93.1090000000\n", "51.4300000000 93.2170000000\n", "53.7600000000 93.0390000000\n", "56.1000000000 92.6570000000\n", "58.8300000000 92.1746000000\n", "\n", "GENERATOR turb_eff_curves Plant1 1\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 200.00 14 M3/S %\n", "# x_value; y_value;\n", "28.1200000000 86.7321000000\n", "30.4500000000 87.9022000000\n", "32.7800000000 88.9688000000\n", "35.1100000000 89.9450000000\n", "37.4500000000 90.8441000000\n", "39.7800000000 91.6794000000\n", "42.1100000000 92.4643000000\n", "44.4400000000 93.1870000000\n", "46.7700000000 93.7495000000\n", "49.1000000000 94.0401000000\n", "51.4300000000 94.1492000000\n", "53.7600000000 93.9694000000\n", "56.1000000000 93.5836000000\n", "58.8300000000 93.0964000000\n", "\n", "GENERATOR turb_eff_curves Plant1 1\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 230.00 14 M3/S %\n", "# x_value; y_value;\n", "28.1200000000 87.5908000000\n", "30.4500000000 88.7725000000\n", "32.7800000000 89.8497000000\n", "35.1100000000 90.8355000000\n", "37.4500000000 91.7435000000\n", "39.7800000000 92.5871000000\n", "42.1100000000 93.3798000000\n", "44.4400000000 94.1096000000\n", "46.7700000000 94.6777000000\n", "49.1000000000 94.9712000000\n", "51.4300000000 95.0813000000\n", "53.7600000000 94.8998000000\n", "56.1000000000 94.5101000000\n", "58.8300000000 94.0181000000\n", "\n", "GENERATOR attributes Plant1 2\n", "#Id Type Penstock Nom_prod Min_prod Max_prod Start_cost\n", "0 7 1 120.00 60.00 120.00 300.00\n", "\n", "GENERATOR gen_eff_curve Plant1 2\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 2 MW %\n", "# x_value; y_value;\n", "60.0000000000 100.0000000000\n", "120.0000000000 100.0000000000\n", "\n", "GENERATOR turb_eff_curves Plant1 2\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 170.00 14 M3/S %\n", "# x_value; y_value;\n", "28.1200000000 85.8733000000\n", "30.4500000000 87.0319000000\n", "32.7800000000 88.0879000000\n", "35.1100000000 89.0544000000\n", "37.4500000000 89.9446000000\n", "39.7800000000 90.7717000000\n", "42.1100000000 91.5488000000\n", "44.4400000000 92.2643000000\n", "46.7700000000 92.8213000000\n", "49.1000000000 93.1090000000\n", "51.4300000000 93.2170000000\n", "53.7600000000 93.0390000000\n", "56.1000000000 92.6570000000\n", "58.8300000000 92.1746000000\n", "\n", "GENERATOR turb_eff_curves Plant1 2\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 200.00 14 M3/S %\n", "# x_value; y_value;\n", "28.1200000000 86.7321000000\n", "30.4500000000 87.9022000000\n", "32.7800000000 88.9688000000\n", "35.1100000000 89.9450000000\n", "37.4500000000 90.8441000000\n", "39.7800000000 91.6794000000\n", "42.1100000000 92.4643000000\n", "44.4400000000 93.1870000000\n", "46.7700000000 93.7495000000\n", "49.1000000000 94.0401000000\n", "51.4300000000 94.1492000000\n", "53.7600000000 93.9694000000\n", "56.1000000000 93.5836000000\n", "58.8300000000 93.0964000000\n", "\n", "GENERATOR turb_eff_curves Plant1 2\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 230.00 14 M3/S %\n", "# x_value; y_value;\n", "28.1200000000 87.5908000000\n", "30.4500000000 88.7725000000\n", "32.7800000000 89.8497000000\n", "35.1100000000 90.8355000000\n", "37.4500000000 91.7435000000\n", "39.7800000000 92.5871000000\n", "42.1100000000 93.3798000000\n", "44.4400000000 94.1096000000\n", "46.7700000000 94.6777000000\n", "49.1000000000 94.9712000000\n", "51.4300000000 95.0813000000\n", "53.7600000000 94.8998000000\n", "56.1000000000 94.5101000000\n", "58.8300000000 94.0181000000\n", "\n", "GENERATOR startcost Plant1 2\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 NOK 73\n", "# Time; f(t);\n", "20180123000000000 300.00000000000000000\n", "\n", "PLANT attributes Plant2\n", "#Id;Water_course;Type;Future_use;Prod_area;Num_gen;Num_pump;\n", "0 0 6 0 1 4 0\n", "#Num_main_seg;Num_penstock;Time_delay;Read_prod_factor;Outlet_line;\n", "1 2 0 0.000000 586.000000\n", "0.000000\n", "0.000100 0.000200\n", "\n", "PLANT mip_flag Plant2\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 NO_UNIT 73\n", "# Time; f(t);\n", "20180123000000000 1.00000000000000000\n", "\n", "GENERATOR attributes Plant2 1\n", "#Id Type Penstock Nom_prod Min_prod Max_prod Start_cost\n", "0 7 1 180.00 100.00 180.00 300.00\n", "\n", "GENERATOR gen_eff_curve Plant2 1\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 2 MW %\n", "# x_value; y_value;\n", "100.0000000000 100.0000000000\n", "180.0000000000 100.0000000000\n", "\n", "GENERATOR turb_eff_curves Plant2 1\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 60.00 14 M3/S %\n", "# x_value; y_value;\n", "126.5400000000 92.7201000000\n", "137.0300000000 93.2583000000\n", "147.5100000000 93.7305000000\n", "158.0000000000 94.1368000000\n", "168.5300000000 94.4785000000\n", "179.0100000000 94.7525000000\n", "189.5000000000 94.9606000000\n", "199.9800000000 95.1028000000\n", "210.4700000000 95.1790000000\n", "220.9500000000 95.1892000000\n", "231.4400000000 95.1335000000\n", "241.9200000000 95.0118000000\n", "252.4500000000 94.8232000000\n", "264.7400000000 94.5191000000\n", "\n", "GENERATOR attributes Plant2 2\n", "#Id Type Penstock Nom_prod Min_prod Max_prod Start_cost\n", "0 7 2 55.00 30.00 55.00 300.00\n", "\n", "GENERATOR gen_eff_curve Plant2 2\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 2 MW %\n", "# x_value; y_value;\n", "30.0000000000 100.0000000000\n", "55.0000000000 100.0000000000\n", "\n", "GENERATOR turb_eff_curves Plant2 2\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 60.00 14 M3/S %\n", "# x_value; y_value;\n", "40.8200000000 83.8700000000\n", "44.2000000000 85.1937000000\n", "47.5800000000 86.3825000000\n", "50.9700000000 87.4362000000\n", "54.3600000000 88.3587000000\n", "57.7500000000 89.1419000000\n", "61.1300000000 89.7901000000\n", "64.5100000000 90.3033000000\n", "67.8900000000 90.6815000000\n", "71.2700000000 90.9248000000\n", "74.6600000000 91.0331000000\n", "78.0400000000 91.0063000000\n", "81.4400000000 90.8436000000\n", "85.4000000000 90.4817000000\n", "\n", "\n", "GENERATOR attributes Plant2 3\n", "#Id Type Penstock Nom_prod Min_prod Max_prod Start_cost\n", "0 7 2 55.00 30.00 55.00 300.00\n", "\n", "GENERATOR gen_eff_curve Plant2 3\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 2 MW %\n", "# x_value; y_value;\n", "30.0000000000 100.0000000000\n", "55.0000000000 100.0000000000\n", "\n", "GENERATOR turb_eff_curves Plant2 3\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 60.00 14 M3/S %\n", "# x_value; y_value;\n", "40.8200000000 83.8700000000\n", "44.2000000000 85.1937000000\n", "47.5800000000 86.3825000000\n", "50.9700000000 87.4362000000\n", "54.3600000000 88.3587000000\n", "57.7500000000 89.1419000000\n", "61.1300000000 89.7901000000\n", "64.5100000000 90.3033000000\n", "67.8900000000 90.6815000000\n", "71.2700000000 90.9248000000\n", "74.6600000000 91.0331000000\n", "78.0400000000 91.0063000000\n", "81.4400000000 90.8436000000\n", "85.4000000000 90.4817000000\n", "\n", "GENERATOR attributes Plant2 4\n", "#Id Type Penstock Nom_prod Min_prod Max_prod Start_cost\n", "0 7 2 55.00 30.00 55.00 300.00\n", "\n", "GENERATOR gen_eff_curve Plant2 4\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 0.00 2 MW %\n", "# x_value; y_value;\n", "30.0000000000 100.0000000000\n", "55.0000000000 100.0000000000\n", "\n", "GENERATOR turb_eff_curves Plant2 4\n", "#Id;Number;Reference;Pts;X_unit;Y_unit\n", "0 0 60.00 14 M3/S %\n", "# x_value; y_value;\n", "40.8200000000 83.8700000000\n", "44.2000000000 85.1937000000\n", "47.5800000000 86.3825000000\n", "50.9700000000 87.4362000000\n", "54.3600000000 88.3587000000\n", "57.7500000000 89.1419000000\n", "61.1300000000 89.7901000000\n", "64.5100000000 90.3033000000\n", "67.8900000000 90.6815000000\n", "71.2700000000 90.9248000000\n", "74.6600000000 91.0331000000\n", "78.0400000000 91.0063000000\n", "81.4400000000 90.8436000000\n", "85.4000000000 90.4817000000\n", "\n", "\n", "#Write connection data for the hydro power system\n", "#; From_type/To_type; From_name; To_name;\n", "CONNECT RESERVOIR/PLANT Reservoir1 Plant1\n", "CONNECT RESERVOIR/PLANT Reservoir2 Plant2\n", "CONNECT PLANT/RESERVOIR Plant1 Reservoir2\n", "\n", "#Initial reservoir volumes;\n", "STARTRES 2 METER\n", "#Name; Head (masl);\n", "Reservoir1 900.0\n", "Reservoir2 670.0\n", "\n", "\n", "MULTI_MARKET price_buy 1 0\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 NOK/MWH 73\n", "# Time; f(t);\n", "20180123000000000 32.99399999999999977\n", "20180123010000000 31.12399999999999878\n", "20180123020000000 29.31400000000000006\n", "20180123030000000 28.07399999999999807\n", "20180123040000000 30.01399999999999935\n", "20180123050000000 33.36400000000000432\n", "20180123060000000 42.68400000000000460\n", "20180123070000000 74.82399999999999807\n", "20180123080000000 77.73399999999999466\n", "20180123090000000 62.33400000000000318\n", "20180123100000000 55.89400000000000546\n", "20180123110000000 46.96400000000000574\n", "20180123120000000 42.58400000000000318\n", "20180123130000000 40.94400000000000261\n", "20180123140000000 39.21400000000000574\n", "20180123150000000 39.14400000000000546\n", "20180123160000000 41.67399999999999949\n", "20180123170000000 46.92399999999999949\n", "20180123180000000 37.10399999999999920\n", "20180123190000000 32.99399999999999977\n", "20180123200000000 31.27399999999999736\n", "20180123210000000 29.75399999999999778\n", "20180123220000000 28.78399999999999892\n", "20180123230000000 28.08399999999999963\n", "20180124000000000 27.24399999999999977\n", "20180124010000000 26.62399999999999878\n", "20180124020000000 25.73399999999999821\n", "20180124030000000 25.39399999999999835\n", "20180124040000000 25.99399999999999977\n", "20180124050000000 27.40399999999999991\n", "20180124060000000 28.94399999999999906\n", "20180124070000000 32.18400000000000460\n", "20180124080000000 33.08400000000000318\n", "20180124090000000 32.34400000000000119\n", "20180124100000000 30.91399999999999793\n", "20180124110000000 30.16399999999999793\n", "20180124120000000 30.06400000000000006\n", "20180124130000000 29.56400000000000006\n", "20180124140000000 29.46399999999999864\n", "20180124150000000 29.51399999999999935\n", "20180124160000000 29.67399999999999949\n", "20180124170000000 30.07399999999999807\n", "20180124180000000 29.55399999999999849\n", "20180124190000000 28.86399999999999721\n", "20180124200000000 28.41399999999999793\n", "20180124210000000 28.07399999999999807\n", "20180124220000000 27.16399999999999793\n", "20180124230000000 25.50399999999999778\n", "20180125000000000 26.19399999999999906\n", "20180125010000000 25.22400000000000020\n", "20180125020000000 24.05399999999999849\n", "20180125030000000 23.89399999999999835\n", "20180125040000000 23.68399999999999750\n", "20180125050000000 26.09399999999999764\n", "20180125060000000 28.20400000000000063\n", "20180125070000000 30.90399999999999991\n", "20180125080000000 31.57399999999999807\n", "20180125090000000 31.46399999999999864\n", "20180125100000000 31.17399999999999949\n", "20180125110000000 30.91399999999999793\n", "20180125120000000 30.57399999999999807\n", "20180125130000000 30.60399999999999920\n", "20180125140000000 30.63400000000000034\n", "20180125150000000 31.06400000000000006\n", "20180125160000000 32.08400000000000318\n", "20180125170000000 36.26400000000000290\n", "20180125180000000 34.47400000000000375\n", "20180125190000000 32.18400000000000460\n", "20180125200000000 31.49399999999999977\n", "20180125210000000 30.73399999999999821\n", "20180125220000000 29.71399999999999864\n", "20180125230000000 28.98399999999999821\n", "20180126000000000 28.98399999999999821\n", "\n", "MULTI_MARKET price_sale 1 0\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 NOK/MWH 73\n", "# Time; f(t);\n", "20180123000000000 32.99199999999999733\n", "20180123010000000 31.12199999999999989\n", "20180123020000000 29.31200000000000117\n", "20180123030000000 28.07199999999999918\n", "20180123040000000 30.01200000000000045\n", "20180123050000000 33.36200000000000188\n", "20180123060000000 42.68200000000000216\n", "20180123070000000 74.82200000000000273\n", "20180123080000000 77.73199999999999932\n", "20180123090000000 62.33200000000000074\n", "20180123100000000 55.89200000000000301\n", "20180123110000000 46.96200000000000330\n", "20180123120000000 42.58200000000000074\n", "20180123130000000 40.94200000000000017\n", "20180123140000000 39.21200000000000330\n", "20180123150000000 39.14200000000000301\n", "20180123160000000 41.67199999999999704\n", "20180123170000000 46.92199999999999704\n", "20180123180000000 37.10199999999999676\n", "20180123190000000 32.99199999999999733\n", "20180123200000000 31.27199999999999847\n", "20180123210000000 29.75199999999999889\n", "20180123220000000 28.78200000000000003\n", "20180123230000000 28.08200000000000074\n", "20180124000000000 27.24200000000000088\n", "20180124010000000 26.62199999999999989\n", "20180124020000000 25.73199999999999932\n", "20180124030000000 25.39199999999999946\n", "20180124040000000 25.99200000000000088\n", "20180124050000000 27.40200000000000102\n", "20180124060000000 28.94200000000000017\n", "20180124070000000 32.18200000000000216\n", "20180124080000000 33.08200000000000074\n", "20180124090000000 32.34199999999999875\n", "20180124100000000 30.91199999999999903\n", "20180124110000000 30.16199999999999903\n", "20180124120000000 30.06200000000000117\n", "20180124130000000 29.56200000000000117\n", "20180124140000000 29.46199999999999974\n", "20180124150000000 29.51200000000000045\n", "20180124160000000 29.67200000000000060\n", "20180124170000000 30.07199999999999918\n", "20180124180000000 29.55199999999999960\n", "20180124190000000 28.86199999999999832\n", "20180124200000000 28.41199999999999903\n", "20180124210000000 28.07199999999999918\n", "20180124220000000 27.16199999999999903\n", "20180124230000000 25.50199999999999889\n", "20180125000000000 26.19200000000000017\n", "20180125010000000 25.22200000000000131\n", "20180125020000000 24.05199999999999960\n", "20180125030000000 23.89199999999999946\n", "20180125040000000 23.68199999999999861\n", "20180125050000000 26.09199999999999875\n", "20180125060000000 28.20200000000000173\n", "20180125070000000 30.90200000000000102\n", "20180125080000000 31.57199999999999918\n", "20180125090000000 31.46199999999999974\n", "20180125100000000 31.17200000000000060\n", "20180125110000000 30.91199999999999903\n", "20180125120000000 30.57199999999999918\n", "20180125130000000 30.60200000000000031\n", "20180125140000000 30.63200000000000145\n", "20180125150000000 31.06200000000000117\n", "20180125160000000 32.08200000000000074\n", "20180125170000000 36.26200000000000045\n", "20180125180000000 34.47200000000000131\n", "20180125190000000 32.18200000000000216\n", "20180125200000000 31.49200000000000088\n", "20180125210000000 30.73199999999999932\n", "20180125220000000 29.71199999999999974\n", "20180125230000000 28.98199999999999932\n", "20180126000000000 28.98199999999999932\n", "\n", "MULTI_MARKET max_buy 1 0\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 MW 73\n", "# Time; f(t);\n", "20180123000000000 9999.00000000000000000\n", "\n", "MULTI_MARKET max_sale 1 0\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 MW 73\n", "# Time; f(t);\n", "20180123000000000 9999.00000000000000000\n", "\n", "\n" ] } ], "source": [ "with open('model.ascii', 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "id": "79216d0c", "metadata": {}, "source": [ "(discrete-droop-reserve-obligation-ascii)=\n", "## reserve_obligation.ascii" ] }, { "cell_type": "code", "execution_count": 11, "id": "eb38d689", "metadata": { "Collapsed": "false", "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RESERVE_GROUP\tfcr_n_up\t1\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 MW 1\n", "# Time; f(t);\n", "20180123000000000 20.0\n", "\n", "RESERVE_GROUP\tfcr_n_down\t1\n", "#Id;Number;Start_Time;Time_unit;Period;Data_type;Y_unit;Pts;\n", "0 0 20180123000000000 HOUR 0 -1 MW 1\n", "# Time; f(t);\n", "20180123000000000 20.0\n", "\n", "\n", "#FCR-N UP\n", "GENERATOR\tgen_fcr_n_up_group\tPlant1 1\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_up_group\tPlant1 2\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_up_group\tPlant2 1\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_up_group\tPlant2 2\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_up_group\tPlant2 3\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_up_group\tPlant2 4\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "\n", "#FCR-N DOWN\n", "GENERATOR\tgen_fcr_n_down_group\tPlant1 1\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_down_group\tPlant1 2\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_down_group\tPlant2 1\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_down_group\tPlant2 2\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_down_group\tPlant2 3\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n", "\n", "GENERATOR\tgen_fcr_n_down_group\tPlant2 4\n", "#\tId\tnumber\tstarttime\t\ttime_unit\tperiod\tdata_type\ty_unit\tnpts\n", "0\t0\t20190913060000000\tHOUR\t\t8760\t\t-1\tNO_UNIT\t1\n", "#\ttime\t\ty\n", "20190913060000000\t1.000000000000\n" ] } ], "source": [ "with open('reserve_obligation.ascii', 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "id": "670880d7", "metadata": {}, "source": [ "(discrete-droop-input-ascii)=\n", "## discrete_droop_input.ascii" ] }, { "cell_type": "code", "execution_count": 12, "id": "fb968438", "metadata": { "Collapsed": "false", "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GENERATOR\tdiscrete_droop_values\tPlant2 1\n", "1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0\n", "\n", "GENERATOR\tdiscrete_droop_values\tPlant2 2\n", "1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0\n", "\n", "GENERATOR\tdiscrete_droop_values\tPlant2 3\n", "1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0\n", "\n", "GENERATOR\tdiscrete_droop_values\tPlant2 4\n", "1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0\n" ] } ], "source": [ "with open('discrete_droop_input.ascii', 'r') as f:\n", " print(f.read())" ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.13.8" } }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" }, "source_map": [ 11, 29, 36, 46, 64, 68, 86, 94, 130, 134, 150, 154, 158, 164, 168, 174, 178, 184, 188, 194, 198, 204, 208, 214, 218 ] }, "nbformat": 4, "nbformat_minor": 5 }