Overview

Overview

The RL-ADN framework is designed to solve the optimal ESSs dispatch in active distribution networks using deep reinforcement learning (DRL). The architecture of the RL-ADN environment consists of three layers: Data Source, Configuration, and Interaction Loop.

Framework Architecture

Data Source Layer

The Data Source Layer provides the primary data required to simulate the environment. This includes historical load data, renewable energy generation data, and market price data. These data sources are crucial for creating realistic and varied scenarios for training DRL agents.

Configuration Layer

In the Configuration Layer, primary data from the Data Source Layer are used to build the DRL environments. This layer integrates key components such as:

• Data Manager: Handles data preprocessing, augmentation, and management.
• Distribution Network Simulator: Simulates the behavior of the distribution network, including power flow calculations.
• ESSs Models: Represents the energy storage systems within the network.

These components are configured to create the environment in which the DRL agent will operate.

Interaction Loop Layer

The Interaction Loop Layer governs the interaction between the DRL agent and the environment. This layer includes the following steps for each time step ( t ) in an episode:

1. State Acquisition: The agent obtains the current state ( s_t ).
2. Action Determination: The agent determines an action ( a_t ) based on the current state.
3. Action Execution: The environment executes the step function to perform power flow and update the status of ESSs and the distribution network.
4. Reward Calculation: The reward ( r_t ) is calculated based on the resultant observations.
5. Next State Sampling: The Data Manager samples external time-series data for the next time step ( t+1 ), including demand, renewable energy generation, and price.

Users can customize the build-state and cal-reward blocks to explore how different states and reward structures influence algorithm performance. The framework provides default implementations for state pattern and reward calculation to facilitate ease of use.

DRL Algorithms

State-of-the-art policy-based algorithms such as DDPG, SAC, TD3, and PPO are incorporated into the framework. These algorithms enable the agent to interact with the environment and learn the optimal policy for ESSs dispatch tasks.

Versatility and Customization

The proposed RL-ADN framework's versatility allows for modeling highly tailored tasks. By modifying components within the Configuration and Interaction Loop Layers, users can create unique Markov Decision Processes (MDPs) for distinct ESSs dispatch tasks. This adaptability is crucial for addressing the diverse challenges present in distribution network operations.

Summary

The RL-ADN framework offers a comprehensive and flexible solution for DRL-based battery energy arbitrage in distribution networks. By leveraging advanced data augmentation techniques and efficient power flow algorithms, the framework enhances the training process for DRL agents, resulting in more effective and robust policies for ESSs dispatch.

For more detailed information on the framework's layers and components, refer to the respective sections in this Wiki.