Optimization of Real-time Capacity Allocation

This Python package performs dispatch optimization for real-time economic optimization.

Clone the repository, navigate to the directory containing setup.py and execute:

pip install -e .

ORCA can be used via individual components or with the CollectedNextDispatch object.

The Optimization and RewardForecast objects are intended to have a simple, cohesive interface that allows plug and play use. Each Optimization object is required to have a return_next_dispatch method that performs the optimization and returns the next time step's optimal dispatch. Each RewardForecast object is required to have a gen_reward method that returns n samples of the reward/price data required in the time horizon for optimization. New optimization or reward forecast algorithms may be implemented and when placed in the appropriate directory, the CollectedNextDispatch object can find and use them or they may be used independently for optimization workflows.

The CollectedNextDispatch object instantiates the required Optimization and RewardForecast objects specified in a YAML file. Note that only one Optimization object is required, but many RewardForecast objects may be specified to represent reward/price information of multiple components. An example of a YAML specification file is given in notebooks/CollectedNextDispatchExample.yaml.

Examples of how to use the various objects within ORCA are given in the notebooks directory.

Each Optimization and RewardForecast object should have associated unit tests. These can be found in the tests directory. They are written using the unittest framework and can be run using a command like python -m unittest discover -v from the directory adjacent to tests.

ORCA uses black for code formatting.