Tutorial 8: Operation Scenarios¶

It is often helpful to test the results of a capacity-planning optimization in various operational scenarios. Such tests allow users to: (1) evaluate the robustness of their model results to various contingencies, (2) ensure that results obtained through time-series aggregation are feasibly on the full hourly time-series, (3) and refine estimates of marginal carrier prices that are obtained through the dual variables.

The zen-operation wrapper allows users to seamlessly run operational scenarios on the results of a previously model.

Tip

Fully customized operation scenarios can be created using the allow_investment configuration in system.json. When false, this configuration prohibits new capacity additions. All capacities, including future capacities, must then be pre-specified in the model via capacity_existing.

Setup¶

The zen-operations requires users to specify a set of operational scenarios in a scenarios_op.json file. This new file uses the same syntax as the standard ZEN-garden scenario tool (refer to the :ref: tutorial on scenario analysis<t_scenario.t_scenario>). It should be placed into the <dataset> folder of the model being run:

<data_folder>/
|--<dataset>/
|   |--energy_system/...
|   |--set_carriers/...
|   |--set_technologies/...
|   |--scenarios.json
|   |--scenarios_op.json
|   `--system.json
|
`--config.json

For example, the scenarios_op.json file shown below tells ZEN-garden to run an operational scenario with all 8760 hours per year. Such a scenario is useful for testing whether the results from time-series aggregation are feasible with a full hourly resolution:

{
  "full_year": {
    "system": {
      "conduct_time_series_aggregation": false,
      "aggregated_time_steps_per_year": 8760
    }
  }
}

Running Operational Scenarios¶

The zen-operation wrapper requires that you first run the original model and have the model results available. This can be done using the standard ZEN-garden commands, as described in the section on running a model::

cd "<data>"
zen-garden --dataset="<dataset>"

Next, run then zen-operation wrapper by entering the command:

zen-operation --dataset="<dataset>" --scenarios_op="scenarios_op.json"

The wrapper first creates a copy of the original model to use for the operational optimizations. The capacity_addition results of the original model are copied to capacity_existing in the new operational model. This ensures that the technology capacities in the operational model match the optimal capacities of the original model. Finally, the operational model is run without capacity additions. Any scenarios specified in the scenarios_op file are included in the operational simulations. For a detailed description of all options in the wrapper, see zen_garden.cli.zen_operation.cli_zen_operation().

If the original capacity-planning model has multiple scenarios, then the zen-operations wrapper applies the operational scenarios to all of the capacity planning scenarios. For example, if the capacity planning model has 5 scenarios, and there are two operational scenarios, then the zen-operations wrapper will run \(5 \times 2 = 10\) scenarios in total.

The zen-operation wrapper can also be run directly from a python script. The code below replicates the command line commands presented previously:

import zen_garden
import os

os.chdir("<data>")
zen_garden.run(dataset="<dataset>")
zen_garden.operation_scenarios(dataset="<dataset>", scenarios_op="scenarios_op.json")

Results¶

The results will be available in the outputs folder of the current working directory. The results will be stored under the following dataset name: "<dataset>_<scenario>__operation".

Example¶

Download the run the example dataset 5_multiple_time_steps_per_year as described in the tutorial setup. Use the zen-operation wrapper to answer the following question:

Run an operation-only version of the example-dataset. Do the operational results match that of the original capacity-planning solution?
1. To answer this question, run the following code in a terminal window. The ZEN-garden environment must be activated for these commands to work:
```
zen-garden --dataset="5_multiple_time_steps_per_year"
zen-operation --dataset="5_multiple_time_steps_per_year"
zen-visualization
```
  These commands do not specify a value of the --scenarios_op flag of zen-operation. In this case, ZEN-garden will run only one operational scenario with all parameter values equal to that of the original dataset. The results of the operational simulations will be located under the name 5_multiple_time_steps_per_year_none__operation.
  
  The last command opens the ZEN-garden visualization platform. Explore the results of the visualization platform to see the differences between the two solutions.
Solution: The operational variables (e.g. production, emissions) have the same values in the capacity-planning problem and the operational problem. This makes sense since the planning problem includes an operational optimization. The total installed capacity in each year is also the same in both problems. That said, the operation problem has no capacity addition in any model year since all technology capacities were specified exogenously.
Define a new operational scenario in which the system is operated to minimize emissions instead of cost. Do the results change? Is the heat demand still met?
1. In the config.json file, set the solver configuration save_duals to true. This ensures that the dual variables are saved in the results.
```
{
  "analysis": {
    // Additional analysis fields go here
  },
  "solver": {
    // Additional solver fields go here
    "save_duals": true,
  }
}
```
2. Create a scenario_op file in the 5_multiple_time_steps_per_year folder. In the file, add a scenario that minimizes emissions instead of costs.
```
{
  "min_emissions": {
    "analysis": {
      "objective": "total_carbon_emissions"
    }
  }
}
```
3. Run the operational scenario using the following code:
```
zen-garden --dataset="5_multiple_time_steps_per_year"
zen-operation --dataset="5_multiple_time_steps_per_year" --scenarios_op="scenarios_op.json"
zen-visualization
```
Solution: In the operational model that minimizes emissions, heat demand gets shed. The original cost-minimization capacity-planning model installs a gas boiler to meet heat demand. The operational optimization can not change the installed technology capacities. Since the gas boiler has non-zero emissions, the minimum emissions optimization chooses to shed heat demand rather than use the gas boiler.