{ "cells": [ { "cell_type": "markdown", "id": "f37c0834", "metadata": {}, "source": [ "(scenario)=\n", "# scenario\n", "The scenario object is used to define a scenario tree when running the stochastic version of SHOP (SHARM). A single scenario called 'S1' is always created in SHOP, and only N-1 new scenarios should be created when running SHARM.\n", "\n", "| | |\n", "|---|---|\n", "|Input connections||\n", "|Output connections||\n", "|License|SHOP_SCENARIO_FUNCTIONALITY|\n", "|Release version|13.0.0.a|\n", "\n", "```{contents}\n", ":local:\n", ":depth: 1\n", "```\n", "\n", "\n", "\n", "## Examples\n", " - [](multiple-price-bid-matrix)\n", " \n", "\n", "## References\n", " - Applying successive linear programming for stochastic short-term hydropower optimization {cite}`Belsnes2016`\n", " - Progressive hedging for stochastic programs with cross-scenario inequality constraints {cite}`Aasgard2020`\n", " - Evaluating a stochastic-programming-based bidding model for a multireservoir system {cite}`Aasgard2014`\n", " - Comparing Bidding Methods for Hydropower {cite}`Aasgard2016b`\n", " - Hydropower Bidding Using Linearized Start-Ups {cite}`Krohn2017`\n", " - Value of multi-market trading for a hydropower producer {cite}`Fodstad2017`\n", " - Optimizing day-ahead bid curves in hydropower production {cite}`Aasgard2018`\n", " - Coordinated hydropower bidding in the day-ahead and balancing market {cite}`Aasgard2019a`\n", " - Hydropower bidding in a multi-market setting {cite}`Aasgard2019b`\n", " - The value of coordinated hydropower bidding in the Nordic day-ahead and balancing market {cite}`Aasgard2022`\n", " \n", "\n", "## Attributes" ] }, { "cell_type": "code", "execution_count": 1, "id": "aac2c2ed", "metadata": { "tags": [ "remove-input", "full-width" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "HTML(\"\"\"
\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "id": "7d9c036d", "metadata": {}, "source": [ "(scenario:scenario_id)=\n", "### scenario_id\n", "Scenario ID. Should be numbered consecutively starting from 1 (xUnit: NO_UNIT, yUnit: NO_UNIT)\n", "\n", "\n", "(scenario:probability)=\n", "### probability\n", "Probability between 0 and 1 for realization of this scenario. The sum of probabilities for all scenarios must be equal to 1. Only the probability for the last time step is used and it will be applied to all time steps to ensure consistency (xUnit: NO_UNIT, yUnit: NO_UNIT)\n", "\n", "\n", "(scenario:common_scenario)=\n", "### common_scenario\n", "Definition of non-anticipativity constraints for the full model. All decision variables must be the same for all scenarios and time steps where common_scenario is equal. All input time-series with stochastic data must accordingly have the same value for all scenarios and time steps where common_scenario is equal. (xUnit: NO_UNIT, yUnit: NO_UNIT)\n", "\n", "\n", "(scenario:common_history)=\n", "### common_history\n", "Definition of which scenario that should be used as the source for a branch in the current scenario. Branching from a different scenario than itself allows modelling of continuous short-term uncertainty by re-using existing scenarios. (xUnit: NO_UNIT, yUnit: NO_UNIT)" ] } ], "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, 49, 61 ] }, "nbformat": 4, "nbformat_minor": 5 }