Metadata-Version: 2.1
Name: acopoweropt
Version: 0.3.1
Summary: Ant Colony Power Systems Optimizer
Home-page: https://github.com/ettoreaquino/acopoweropt
Keywords: power systems,optimization,python,ant-colony
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: PyQt5 (>=5.15.4,<6.0.0)
Requires-Dist: cvxopt (>=1.2.6,<2.0.0)
Requires-Dist: imageio (>=2.9.0,<3.0.0)
Requires-Dist: matplotlib (>=3.4.2,<4.0.0)
Requires-Dist: pandas (>=1.2.4,<2.0.0)
Requires-Dist: seaborn (>=0.11.1,<0.12.0)
Project-URL: Repository, https://github.com/ettoreaquino/acopoweropt
Description-Content-Type: text/markdown

[![PyPI version](https://badge.fury.io/py/acopoweropt.svg)](https://badge.fury.io/py/acopoweropt)

# 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. This approach, although more elegant, is far from trivial to be implemented, mainly because the __enviroment__ where the ants would look for food gets deeply entangled with the domain. For example, the modeling of pheromone matrix for the traveler salesman might not be adequate for a Power Systems Unit Commitment problem.

For that reason, the initial approach was to create two main _Entities_: A `Power System` and a `PowerColony`, where the first must be a Power System which can be solved by a mathematical method and the second should be an Ant Colony initialized to seek optimal results of a Power System problem.

Since the dispatch of "multi operative zone" Thermal Generation Units (TGUs) bring non-linearities to the formulation, obtaining a global optimal financial dispach of the system is not a trivial task. The Ant Colony algorithm came in hand as a tool to iteractively seek a global optimal result without having to rely on brute computational force.

### 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

The code below samples a possible configuration which can be used to operate the system and solves this configuration.

```python
from acopoweropt import system

# Instance 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'))
```

Another option is to bring your own sequence of operative zones (1 for each TGU) and build the operation data from it:

```python
from acopoweropt import system

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

# Define a sequence of operative zones for each of the 10 TGUs
opzs = [2, 3, 1, 2, 1, 1, 3, 1, 1, 3]

# Build a configuration that represents such sequence of operative zones
operation = PSystem.get_operation(operative_zones=opzs)

# Solve the Economic Dispatch of the specific configuration:
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'))
```

### Defining Power Colonies
An Ant Colony should seek for a global optimal solution or "the optimal source of food". The algorithm was proposed by Marco Dorigo, check [Wiki](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms) for more details.

#### Example

The code below initializes a PowerColony with a desired PowerSystem as the "environment" for the ants to seek their food. Once instantiated, the PowerColony immediately unleashes their ants for a first seek for solutions, therefore `PowerColony.paths` and `PowerColony.pheromone` can be observed.

```python
from acopoweropt import colony, system

PSystem = system.PowerSystem(name='s15')
PColony = colony.PowerColony(n_ants=100,
                            pheromone_evp_rate={'worst': 0.75, 'mean': 0.25, 'best': 0.05},
                            power_system=PSystem)
```

Now a PowerColony can seek for optimal sources of food:
```python
PColony.seek(max_iter=20, power_system=PSystem, show_progress=True)

ax = PColony.paths.groupby('iteration').distance.min().plot(y='distance')

PColony.create_pheromone_movie(duration=0.25)
```

## License

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