# Adding reservoir control curves and demand savings (reductions) ## Introduction Control curves can be used to implement a demand reductions when reservoir levels go below certain thresholds. This represents the implementation of temporary demand management measures. In this exercise the demand will be incrementally reduced as the reservoir goes below certain storage thresholds. This exercise will demonstrate the[ Control Curve Index Parameter](https://water-strategy.gitbook.io/water-strategy/modelling-fundamentals/parameters/control-curve-index-parameter), the [Indexd Array Parameter](https://water-strategy.gitbook.io/water-strategy/modelling-fundamentals/parameters/indexed-array-parameter) as well as the [Aggregated Parameter](https://water-strategy.gitbook.io/water-strategy/modelling-fundamentals/parameters/aggregated-parameter) as well as parameter nesting. ## Clone the scenario and define a control curve 1. Clone the **'Balanced sources'** scenario and name the new one **'Demand reductions'** 2. First we will define a **control curve** which uses storage volume thresholds to progressively reduce demand to model demand restrictions being placed on a demand. The first curve is a [Monthly Profile](https://water-strategy.gitbook.io/water-strategy/modelling-fundamentals/parameters/monthly-profile) (60% in come months and 45% in others) allowing for seasonal changes while the two subsequent curves are [Constants ](https://water-strategy.gitbook.io/water-strategy/modelling-fundamentals/parameters/constant)(40% and 10% of reservoir storage capacity).

The Control Curve will be defined in the **Parameters** tab. In the Parameters tab add a **Pywr\_Parameter**.

Name the parameter **'storage control curve'** and press *Enter***.**

Paste in the following JSON code snippet below. Please note how the `"Example reservoir"` is referenced in the `"storage_node"`attribute. ``` { "type": "controlcurveindexparameter", "storage_node": "Example reservoir", "control_curves": [ { "type": "monthlyprofileparameter", "values": [ 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.6, 0.6 ] }, { "type": "constant", "value": 0.4 }, { "type": "constant", "value": 0.1 } ], "__recorder__": { "timeseries": true } } ```

At each time step the [Control Curve Index Parameter](https://water-strategy.gitbook.io/water-strategy/modelling-fundamentals/parameters/control-curve-index-parameter) will return an Index Value as shown below:

These Indices can be associated to a Demand Factor which will be defined using an [Indexed Array Parameter](https://water-strategy.gitbook.io/water-strategy/modelling-fundamentals/parameters/indexed-array-parameter). The Demand factor will be used to reduce demand when each control curve threshold is passed. ## Associate demand factor 1. We will associate the following **Demand Factors** to the different control curve failure levels:

These will reduce demand to 90%, 80% and 50% of the Baseline demand corresponding to 10%, 20% and 50% demand reductions. 2. Create a new **Pywr\_Parameter**

3. Name the parameter **'control curve demand factor'** and press *Enter***.**

4. Paste in the following JSON code snippet below. Please note how the`"`storage `control curve"` is referenced in the `"index_parameter"`attribute. ``` { "type": "indexedarrayparameter", "index_parameter": "storage control curve", "params": [ 1, 0.9, 0.8, 0.5 ], } ```

The `Params`attribute takes in either scalars or Pywr parameters and the index of the array corresponds to index in the Parameter referenced in the **index\_parameter** which in this case is the control curve. 5. Select to make this Parameter output.

## Define the baseline demand Next we will define the baseline demand. This is the demand that the reservoir has before any reductions are implemented. In previous tutorial, the Example demand is defined as a scalar (0.1) on the Max\_flow attribute of the Example demand output node:

We will replace this with a **Parameter reference.** 1. First, we will define the baseline demand using a **Constant Parameter.** Add a new **Pywr\_parameter.**

And name it **Baseline demand** and press *Enter*.

2. The baseline demand will remain 0.1 Mm3/day. Copy and paste the JSON code snippet into the JSON tab. ``` { "type": "constant", "value": 0.1 } ```

At each time step, the modeled demand will be the Baseline Demand multiplied by the Demand Factor: `Timestep demand = Baseline demand x Demand Factor` ## Calculate **timestep demand** This can be accomplished by using an [Aggregated Parameter](https://water-strategy.gitbook.io/water-strategy/modelling-fundamentals/parameters/aggregated-parameter). 1. Add a new **Pywr\_parameter.**

Name the new parameter **'timestep demand'**

2. Copy and paste the JSON code snippet into the JSON tab. ``` { "type": "AggregatedParameter", "agg_func": "product", "parameters": [ "baseline demand", "control curve demand factor" ] } ```

Select to make this Paramter value be output in each time step.

3. The **'timestep demand'** defines the demand at each time step taking into account the state (i.e. real-time storage) in the Reservoir. This **Parameter** needs to be referenced on the **max\_flow** attribute of the Demand node. 4. Click on the Demand node and write or paste '**timestep demand'** in the max\_flow attribute replacing the scalar value (0.1).

Please note, if the Parameter name does not save, change the type of the entry to **"Descriptor"**.

Don't forget to save the changes. ## Run the model and view the results 1. **Run** the model.

2. View the **simulated** \_volume on the Reservoir

You can **zoom** into the drought, for example this is the drought that occurred in 2042-2044.

Simulated _volume on the Reservoir in 2042-2044

In the the Scenario with demand reductions, the Reservoir did not go to as low of storage (9.4 vs 8.17 Mm3). 3. Click on the simulated\_flow of the Demand node. The demand reductions can be seen.

4. You can view the control cure parameter output by clicking on the **Network Data** view.

Click to view the control cure parameter output

Clicking on **simulated\_storage control curve** shows what index the storage control curve is returning at each time step. This varies between 0-2.

## Exercise 1. Increase the Baseline Demand Parameter. How high can baseline demand be before the reservoir fully empties?