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scenario

iLab Assistant

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.

Input connections
Output connections
LicenseSHOP_SCENARIO_FUNCTIONALITY
Release version13.0.0.a

Examples

References

Attributes

AttributeData typeI/ORequiredUnitDefaultLicenseVersion addedNaN allowed
scenario_idintInputNoNO_UNIT0SHOP_SCENARIO_FUNCTIONALITY13.0.0.aNo
probabilitytimeseriesInputNoNO_UNITNone/nullSHOP_SCENARIO_FUNCTIONALITY13.0.0.aNo
common_scenariotimeseriesInputNoNO_UNITNone/nullSHOP_SCENARIO_FUNCTIONALITY13.0.0.aNo
common_historytimeseriesInputNoNO_UNITNone/nullSHOP_SCENARIO_FUNCTIONALITY13.0.0.aNo

scenario_id

Data type: int. Unit: NO_UNIT.

Scenario ID. Should be numbered consecutively starting from 1.

probability

Data type: timeseries. Unit: NO_UNIT.

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.

common_scenario

Data type: timeseries. Unit: NO_UNIT.

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.

common_history

Data type: timeseries. Unit: NO_UNIT.

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.

References
  1. Aasgård, E. K., & Skjelbred, H. I. (2020). Progressive hedging for stochastic programs with cross-scenario inequality constraints. Computational Management Science, 17(1), 141–160. 10.1007/S10287-019-00359-Z/FIGURES/9
  2. Belsnes, M. M., Wolfgang, O., Follestad, T., & Aasgård, E. K. (2016). Applying successive linear programming for stochastic short-term hydropower optimization. Electric Power Systems Research, 130, 167–180. 10.1016/J.EPSR.2015.08.020
  3. Aasgård, E. K., Andersen, G. S., Fleten, S. E., & Haugstvedt, D. (2014). Evaluating a stochastic-programming-based bidding model for a multireservoir system. IEEE Transactions on Power Systems, 29(4), 1748–1757. 10.1109/TPWRS.2014.2298311
  4. Aasgård, E. K., Skjelbred, H. I., & Solbakk, F. (2016). Comparing Bidding Methods for Hydropower. Energy Procedia, 87, 181–188. 10.1016/J.EGYPRO.2015.12.349
  5. Aasgård, E. K. (2017). Hydropower Bidding Using Linearized Start-Ups. Energies 2017, Vol. 10, Page 1975, 10(12), 1975. 10.3390/EN10121975
  6. Fodstad, M., Mathisen, M., & Midthun, K. T. (2017). Value of multi-market trading for a hydropower producer. International Conference on the European Energy Market, EEM. 10.1109/EEM.2017.7981989
  7. Aasgård, E. K., Naversen, C. Ø., Fodstad, M., & Skjelbred, H. I. (2018). Optimizing day-ahead bid curves in hydropower production. Energy Systems, 9(2), 257–275. 10.1007/S12667-017-0234-Z/FIGURES/15
  8. Aasgård, E. K. (2019). Coordinated hydropower bidding in the day-ahead and balancing market. Proceedings of the 6th International Workshop on Hydro Scheduling in Competitive Electricity Markets, 16–25. 10.1007/978-3-030-03311-8_3
  9. Aasgård, E. K., Fleten, S. E., Kaut, M., Midthun, K. T., & Perez-Valdes, G. A. (2019). Hydropower bidding in a multi-market setting. Energy Systems, 10(3), 543–565. 10.1007/S12667-018-0291-Y/FIGURES/1
  10. Aasgård, E. K. (2022). The value of coordinated hydropower bidding in the Nordic day-ahead and balancing market. Energy Systems, 13(1), 53–77. 10.1007/S12667-020-00388-7/TABLES/5