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acopoweropt-0.3.1
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Ant Colony Power Systems Optimizer
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | acopoweropt-0.3.1 |
| نام | acopoweropt |
| نسخه کتابخانه | 0.3.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ettore Aquino |
| ایمیل نویسنده | ettore@ettoreaquino.com |
| آدرس صفحه اصلی | https://github.com/ettoreaquino/acopoweropt |
| آدرس اینترنتی | https://pypi.org/project/acopoweropt/ |
| مجوز | - |
[](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).
نیازمندی
| مقدار | نام |
|---|---|
| >=1.2.4,<2.0.0 | pandas |
| >=1.2.6,<2.0.0 | cvxopt |
| >=3.4.2,<4.0.0 | matplotlib |
| >=0.11.1,<0.12.0 | seaborn |
| >=5.15.4,<6.0.0 | PyQt5 |
| >=2.9.0,<3.0.0 | imageio |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<4.0 | Python |
نحوه نصب
نصب پکیج whl acopoweropt-0.3.1:
pip install acopoweropt-0.3.1.whl
نصب پکیج tar.gz acopoweropt-0.3.1:
pip install acopoweropt-0.3.1.tar.gz