Metadata-Version: 2.1
Name: acopoweropt
Version: 0.2.0
Summary: Ant Colony Power Systems Optimizer
Home-page: https://github.com/ettoreaquino/acopoweropt
Keywords: power systems,optimization,python
Author: Ettore Aquino
Author-email: ettore@ettoreaquino.com
Requires-Python: >=3.8,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: cvxopt (>=1.2.6,<2.0.0)
Requires-Dist: pandas (>=1.2.4,<2.0.0)
Project-URL: Repository, https://github.com/ettoreaquino/acopoweropt
Description-Content-Type: text/markdown

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# Ant Colony Power Systems Optimizer

This library aims to provide a tool to obtain an optimal dispach of a Power System comprised of Thermal Generation Units. The approach combines the Ant Colony Optimizer with a non-linear solver provided by CVXOPT.

> This is an under development library

## Installation instructions

### PyPi
A pre-built binary wheel package can be installed using pip:
```sh
pip install acopoweropt
```

### Poetry
Poetry is a tool for dependency management and packaging in Python. `acopoweropt` can be installed in a poetry managed project:
```sh
poetry add acopoweropt
```

## Usage
From a domain perspective, there should be a complete decoupling between an Ant Colony and a Power System, after all ants do not have knowledge of power systems. Therefore an initial approach was to develop to main _Entities_: A `Colony` and a `Power System`. A Power System should be solved by a mathematical method which might can be or not the optimal result, which is where the Ant Colony Algorithm can be used.

### Defining Systems
The systems configuration should be defined in the [`systems.json`](systems.json) file. In the example provided, 3 systems where defined: 's10', 's15' and 's40', the names were chosen for convention and will be used by the `PowerSystem` class to initialize the desired configuration.


### Example

```python
from acopoweropt import system

# Intance a PowerSystem class from a configuration file where 's10` defines a
# system configuration
PSystem = system.PowerSystem(name='s10')

# Randomly selects a possible system operation (there are cases where more than
# a single unit can be operated in diferent configurations)
operation = PSystem.sample_operation()

# Solve the Economic Dispatch of the units of a specific configuration of the
# system, in this case, let's use the previously sampled one:
solution = PSystem.solve(operation=operation)

# Prints total financial cost of the operation
print("Total Financial Cost: {}".format(solution.get('Ft')))

# Prints the operation with its power dispach values
print(solution.get('operation'))
```

## License

See the [LICENSE](LICENSE.md) file for license rights and limitations (MIT).
