{ "cells": [ { "cell_type": "markdown", "id": "9f4b95ba", "metadata": {}, "source": [ "(area)=\n", "# area\n", "Market data. Exactly one instance is mandatory.\n", "\n", "| | |\n", "|---|---|\n", "|Input connections||\n", "|Output connections||\n", "|License|PRODRISK_OPEN|\n", "|Release version|9.6.1|\n", "\n", "```{contents}\n", ":local:\n", ":depth: 1\n", "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "## Attributes" ] }, { "cell_type": "code", "execution_count": 1, "id": "ce9d42fa", "metadata": { "tags": [ "remove-input", "full-width" ] }, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
Object typeAttribute namePython data typeCore data typeunitI/OLicenseVersion addedDescription
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\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import itables as itables\n", "from itables import init_notebook_mode\n", "init_notebook_mode(all_interactive=True, connected=True)\n", "import pandas as pd\n", "from IPython.core.display import HTML\n", "\n", "table = pd.read_csv('../../../attributes.csv')\n", "core_type_dict = {'int' : 'integer','double':'float','string':'string','int_array':'list-of-integer-values','double_array':'list-of-double-values','xy':'table-xy-curve','xy_array':'list-of-tables','txy':'time-series','txy_stochastic':'stochastic-time-series','txy_step':'stepwise-time-series'}\n", "object_attributes = table[table[\"Object type\"] == \"area\"].reset_index().iloc[:, 1:]\n", "for index, row in object_attributes.iterrows():\n", " object_attributes.at[index, \"Attribute name\"] = f\"\"\"{row['Attribute name']}\"\"\"\n", " object_attributes.at[index, \"Core data type\"] = f\"\"\"{row['Core data type']}\"\"\"\n", "itables.show(object_attributes,\n", " dom='tlip',\n", " search={'regex': True, \"caseInsensitive\": True},\n", " column_filters='header',\n", " columns=[\n", " {\n", " 'name': '',\n", " 'className': 'dt-control',\n", " 'orderable': False,\n", " 'data': None,\n", " 'defaultContent': '',\n", " },\n", " {\n", " 'name': 'Attribute name',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'Python data type',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'Core data type',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'unit',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'I/O',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'License',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'Version added',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'Description',\n", " 'visible': False\n", " }\n", " ]\n", ")\n", "HTML('''''')" ] }, { "cell_type": "markdown", "id": "1a8ccd01", "metadata": {}, "source": [ "(area:forward_cost)=\n", "### forward_cost\n", "Forward cost per iteration (unit: MEUR)\n", "\n", "\n", "(area:priceTransition)=\n", "### priceTransition\n", "The probability of jumping from one price level to another. The nPriceLevels first scenarios represent the transition probability of jumping from price level 1 to 1, 1 to 2, 1 to 3, etc., 1 to nPriceLevels. The next nPriceLevels scenarios represent jumping from 2 to 1, 2 to 2 and so on. (unit: none)\n", "\n", "\n", "(area:reserve_up_obligation)=\n", "### reserve_up_obligation\n", "Reserve up obligation (unit: MW)\n", "\n", "\n", "(area:indvan_prd_price_level)=\n", "### indvan_prd_price_level\n", "Price level the water values at start time refers to. (unit: EUR/MWh)\n", "\n", "\n", "(area:price)=\n", "### price\n", "Market prices. (unit: EUR/MWh)\n", "\n", "\n", "(area:load)=\n", "### load\n", "Fixed load to be satisfied. NB: Loads are limited to an internal resolution of accumulated price points, changes within the week will first be accumulated then distributed for sequential price points. (unit: MW)\n", "\n", "\n", "(area:output_price)=\n", "### output_price\n", "Output price (unit: EUR/MWh)\n", "\n", "\n", "(area:reserve_down_obligation_cost)=\n", "### reserve_down_obligation_cost\n", "Violations of reserve down obligation (unit: MW)\n", "\n", "\n", "(area:transmission_spot_sale_strat)=\n", "### transmission_spot_sale_strat\n", "Separate backward transmission limitation for sales to the spot market (used in calibration), same limitations apply. (unit: MW)\n", "\n", "\n", "(area:reserve_down_price)=\n", "### reserve_down_price\n", "Reserve down price (unit: EUR/MWh)\n", "\n", "\n", "(area:output_reserve_up_price)=\n", "### output_reserve_up_price\n", "Output reserve up price (unit: EUR/MWh)\n", "\n", "\n", "(area:forward_cost_first_run)=\n", "### forward_cost_first_run\n", "Forward cost per iteration (first main iteration) (unit: MEUR)\n", "\n", "\n", "(area:water_value_result)=\n", "### water_value_result\n", "Aggregated water values (unit: EUR/MWh)\n", "\n", "\n", "(area:lognormal_probabilities)=\n", "### lognormal_probabilities\n", "Probabilities (weights) from k-means clustering used in the lognormal inflow model (unit: none)\n", "\n", "\n", "(area:total_energy_consumed)=\n", "### total_energy_consumed\n", "Total energy consumed (unit: GWh)\n", "\n", "\n", "(area:priceBand)=\n", "### priceBand\n", "The weekly prices corresponding to the price levels calculated by the price model (unit: EUR/MWh)\n", "\n", "\n", "(area:cutFrequency)=\n", "### cutFrequency\n", "The number of times a cut is binding is given as an array of XY tables. Each xy table in the array corresponds to a price level in ascending order. The x values are all 0, while the y values represent the frequency. (unit: none)\n", "\n", "\n", "(area:backward_cost_first_run)=\n", "### backward_cost_first_run\n", "Backward cost per iteration (first main iteration) (unit: MEUR)\n", "\n", "\n", "(area:transmission_spot_purchase_strat)=\n", "### transmission_spot_purchase_strat\n", "Separate backward transmission limitation for purchases from the spot market (used in calibration), same limitations apply. (unit: MW)\n", "\n", "\n", "(area:reserve_up_price)=\n", "### reserve_up_price\n", "Reserve up price (unit: EUR/MWh)\n", "\n", "\n", "(area:reserve_down_obligation)=\n", "### reserve_down_obligation\n", "Reserve down obligation (unit: MW)\n", "\n", "\n", "(area:backward_cost)=\n", "### backward_cost\n", "Backward cost per iteration (unit: MEUR)\n", "\n", "\n", "(area:total_reservoir_volume)=\n", "### total_reservoir_volume\n", "Total reservoir volume (unit: GWh)\n", "\n", "\n", "(area:transmission_spot_purchase)=\n", "### transmission_spot_purchase\n", "Transmission limitation for purchases from the spot market. NB: Transmission is limited to an internal resolution of accumulated price points, changes within the week will first be accumulated then distributed for sequential price points. (unit: MW)\n", "\n", "\n", "(area:total_reserve_down_allocation)=\n", "### total_reserve_down_allocation\n", "Output total reserve down allocation (unit: MW)\n", "\n", "\n", "(area:total_production)=\n", "### total_production\n", "Total production (unit: GWh)\n", "\n", "\n", "(area:total_reserve_down_capacity)=\n", "### total_reserve_down_capacity\n", "Maximum reserve down capacity (unit: MW)\n", "\n", "\n", "(area:total_reserve_up_allocation)=\n", "### total_reserve_up_allocation\n", "Output total reserve up allocation (unit: MW)\n", "\n", "\n", "(area:total_energy_pumped)=\n", "### total_energy_pumped\n", "Total energy pumped (unit: GWh)\n", "\n", "\n", "(area:transmission_spot_sale)=\n", "### transmission_spot_sale\n", "Transmission limitation for sales to spot market. NB: Transmission is limited to an internal resolution of accumulated price points, changes within the week will first be accumulated then distributed for sequential price points. (unit: MW)\n", "\n", "\n", "(area:reserve_up_obligation_cost)=\n", "### reserve_up_obligation_cost\n", "Violations of reserve up obligation (unit: MW)\n", "\n", "\n", "(area:fcost_per_scen)=\n", "### fcost_per_scen\n", "Forward cost per scenario from final simulation. (unit: MEUR)\n", "\n", "\n", "(area:output_reserve_down_price)=\n", "### output_reserve_down_price\n", "Output reserve down price (unit: EUR/MWh)\n", "\n", "\n", "(area:waterValue)=\n", "### waterValue\n", "Aggregated water values at the end of the simulation interval. Each xy table in the array corresponds to a price level in ascending order. The tables consist of 51 xy points, where the x values represent the relative water level in the reservoir in steps of 2% from 100% down to 0% (descending order). The y values are the water values for the given price level and degree of reservoir filling. (unit: % and EUR/MWh)\n", "\n", "\n", "(area:expected_objective_value)=\n", "### expected_objective_value\n", "Expected objective value (sum over all scenarios, [production-pump] * price * probability for each scenario, corrected for value of remaining water.) (unit: kEUR)\n", "\n", "\n", "(area:total_reservoir_overflow)=\n", "### total_reservoir_overflow\n", "Total reservoir overflow (unit: GWh)\n", "\n", "\n", "(area:total_storable_inflow)=\n", "### total_storable_inflow\n", "Total inflow (regulated + unregulated) (unit: GWh)\n", "\n", "\n", "(area:total_nonstorable_inflow)=\n", "### total_nonstorable_inflow\n", "Reserved for future use: total unregulated inflow (unit: GWh)\n", "\n", "\n", "(area:cutRHS)=\n", "### cutRHS\n", "The right-hand side of the cuts is given as an array of XY tables. Each xy table in the array corresponds to a price level in ascending order. The x values are all 0, while the y values are the right-hand side of the cuts. (unit: kEUR)\n", "\n", "\n", "(area:total_reserve_up_capacity)=\n", "### total_reserve_up_capacity\n", "Maximum reserve up capacity (unit: MW)\n", "\n", "\n", "(area:total_discharge)=\n", "### total_discharge\n", "Total discharge (unit: GWh)" ] } ], "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": "3.11.12" }, "source_map": [ 11, 36, 114 ] }, "nbformat": 4, "nbformat_minor": 5 }