apply_minq Scheme Example

This example shows how to use the ReservoirModel.apply_minq() scheme when modelling a single reservoir model.

Note

For details about the full model file structure please see Basic Single Reservoir.

We consider a reservoir with a single inflow, Q_in, and an outflow Q_out. The outflow is determined in such a way that the peak outflow is minimum while making sure that the elevation remains within given bounds.

The ReservoirModel.apply_minq() scheme can be applied to calculate an optimal outflow and apply it to the simuatlion.

Main Model (python) File

An example of the main model file minq_example.py is given below.

"""Example illustrating the minq scheme."""
from pathlib import Path

from rtctools.util import run_simulation_problem

from rtctools_simulation.reservoir.model import ModelConfig, ReservoirModel

CONFIG = ModelConfig(base_dir=Path(__file__).parent)


class Reservoir(ReservoirModel):
    """Class for demonstrating the minq scheme."""

    def apply_schemes(self):
        """Apply minq at each simulated timestep."""
        self.apply_minq(
            h_min=0,
            h_max=40.0,
            h_target="rule_curve",
        )


# Create and run the model.
if __name__ == "__main__":
    model = run_simulation_problem(Reservoir, config=CONFIG, previous_run_plot_config=None)
    results = model.extract_results()

The template file mentioned in the Basic Single Reservoir will look very similar to this file, except that the apply_schemes() method still needs to be filled out.

The line

CONFIG = ModelConfig(base_dir=Path(__file__).parent)

sets the model configuration. This model configuration is defined by the base directory base_dir. In most cases, the base directory is Path(__file__).parent, which is the directory of the current file.

The line

class Reservoir(ReservoirModel):

defines a class SingleReservoir that inherits all properties and functionalities of the predefined class ReservoirModel. An overview of this class can be found in Reservoir API and details of the underlying model it uses can be found in Single Reservoir Model.

The method ReservoirModel.apply_schemes() is called every timestep and contains the logic for which schemes are applied. The first argument self is the SingleReservoir object itself. Since SingleReservoir inherits from ReservoirModel, self can call any of the ReservoirModel methods, such as ReservoirModel.apply_minq(). An overview of all available ReservoirModel methods can be found in Reservoir API.

In this example, the ReservoirModel.apply_schemes() method calls the apply_minq method. If no optimal outflow has been calculated yet, this method will calculate the optimal outflow (i.e. outflow with a minimal peak) given upper bounds on the elevation (h_min, h_max) and a target elevation (h_target). It will then use this optimized outflow to set the outflow of the simulation. The upper and lower bounds for the elevation are set in this example to 0 and 40, respectively, and the target elevation is set to the timeseries rule_curve. This timeseries is given via the timeseries_import.xml file.

Lookup tables

This model uses the standard lookup tables h_from_v.

Note

For further details about the lookup tables please see Basic Single Reservoir.

Input Data Files

The rule_curve timeseries is supplied to the model via the timeseries_import.xml input file.

Note

For further details about input file structure please see Basic Single Reservoir.

Output Data

The results of the simulation will appear in the output folder in a file called timeseries_export.xml. The data is linked to model variables via the rtcDataConfig.xml in the same way as with timeseries_import.xml.

Automatic Plotting

You can optionally include a plot_table.csv in the input folder. This is used by the rtc-tools-interfaces module (automatically installed with this package) to plot the model output. For more details on how to use this file and visualize results, see RTC-Tools-Interface.

The results of the simulation run can be seen in the plot below.

Note that in this case, the peak outflow is still high as the solution balances minimizing peak outflow with meeting the rulecurve. If we adjust the weights in the apply_minq call, we can achieve a lower peak outflow. Let minimize_peak_q_weight=0.9999 and h_target_weight=0.0001, then we obtain the following results.

This shows that by adjusting the weights, we can prioritize minimizing peak outflow over following the rule curve.

Note

The choice of weights depends on the specific application and desired outcomes. It is recommended to use the default weights unless there is a clear rationale for adjusting them.