GC_Morton2000 module

class GC_Morton2000.Morton2000(grid, init_config, fitness_ratio=1, loss_factor=0.08, verbose_logging=30)

Bases: object

Implementation of the Morton 2000 algorithm for optimizing network configurations.

net

The network object containing distribution network data.

candidates

A list to store candidate network configurations.

initial_loops

Initial loop configurations for the network.

init_config

Initial configuration of the network.

best_power_loss

Variable to track the best (minimum) power loss during optimization.

ChooseBestTree()

Chooses the best tree configuration based on the lowest power loss.

This method tests each candidate configuration and tracks the configuration with the lowest power loss.

Returns:

The best tree configuration and the associated power flow results.

RemoveDuplicates(tot_candidates)

Removes duplicate candidates from the list of total candidates.

This method populates the candidates list with unique configurations.

SearchTrees()

Starts the search for trees in the network based on initial loops.

This method reconfigures the network, initializes candidates, and starts the recursive search for trees.

Solve(max_candidates=inf)

Main method to solve the network optimization problem.

Parameters:

max_candidates – The maximum number of candidate configurations to consider.

Returns:

The best tree configuration and associated results.

__init__(grid, init_config, fitness_ratio=1, loss_factor=0.08, verbose_logging=30) → None

Initializes the Morton2000 class with required parameters.

Parameters:

• net – The network object for optimization.

• init_config – The initial configuration of the network.

• verbose_logging – Logging level for debug messages.