Usage Guide

HAMLET allows you to set up, execute, and analyze energy system simulations with ease. This guide provides an overview of common workflows and commands to help you get started.

Running a Basic Scenario

Follow these steps to execute a pre-configured scenario:

Navigate to the Examples Directory:
```
cd examples
```
Pick the `create_simple_scenario` folder
```
cd create_simple_scenario
```
Run the jupyter notebook:
```
run.ipynb
```

Common Commands and Functions

Creator Module

Purpose: Define agents, markets, and grids.
Example:

from hamlet import Creator

creator = Creator(path=\"./configs\", name=\"example_scenario\")
creator.new_scenario_from_configs()

Executor Module

Purpose: Execute the simulation scenarios.
Example:

from hamlet import Executor

executor = Executor(path_scenario=\"./scenarios/example_scenario\")
executor.run()

Analyzer Module

Purpose: Analyze and visualize the results.
Example:

from hamlet import Analyzer

analyzer = Analyzer(path_results=\"./results/example_scenario\")
analyzer.plot_virtual_feeder_flow()

Advanced Options

Custom Configurations: Copy and configure the YAML files in the configs directory to customize your scenario.
Parallel and Sequential Execution: Parallel execution is turned on by default to decrease simulation time. However, in some circumstances (e.g. debugging), it might be better to run the simulation sequentially, which can be done by setting num_workers.

executor = Executor(path_scenario=\"./scenarios/example_scenario\", num_workers=1)

Interactive Debugging: Use IDEs like PyCharm or VS Code to debug specific steps in the simulation pipeline.

Next Steps

Once you’re comfortable with the basics, explore more advanced use cases:

Custom Agents: Define unique agent behaviors.
Complex Markets: Simulate diverse market configurations.
Grid Variations: Test different grid topologies and constraints.

Refer to the Examples for HAMLET section for detailed workflows.