CodeExplain

Core Code Explanation

This page provides an overview and explanation of the core code components in the RL-ADN framework. The framework is designed to solve the optimal ESSs dispatch in active distribution networks using deep reinforcement learning (DRL).

Key Components

Environment Initialization

The environment in RL-ADN is initialized using the RL_ADN_Environment class. This class sets up the simulation environment, including loading the configuration, initializing the data manager, and setting up the distribution network simulator.

from RL_ADN import RL_ADN_Environment

# Define the configuration for the environment
config = {
    'network_data': 'path_to_network_data',
    'time_series_data': 'path_to_time_series_data',
    'ess_model': 'path_to_ess_model',
    'other_parameters': 'value'
}

# Initialize the RL-ADN environment
env = RL_ADN_Environment(config=config)