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Related Experiment Video

Updated: Jan 14, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Reinforcement Learning for Online Reconfiguration of Active Distribution Network.

Guokai Hao, Yuanzheng Li, Yang Li

IEEE Transactions on Neural Networks and Learning Systems

|October 23, 2025

Summary

This summary is machine-generated.

This study introduces an online-offline deep reinforcement learning (DRL) framework for active distribution network reconfiguration (ADNR) to manage renewable energy fluctuations. The novel approach enhances grid stability and renewable energy integration by improving DRL performance in real-time operations.

Related Experiment Videos

Last Updated: Jan 14, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Electrical Engineering
Power Systems
Artificial Intelligence

Background:

High penetration of renewable energy (RE) in active distribution networks (ADNs) introduces uncertainty and variability, impacting grid stability and efficiency.
Traditional deep reinforcement learning (DRL) methods for ADN reconfiguration (ADNR) often rely on historical data, leading to potential mismatches and challenges with unseen scenarios.

Purpose of the Study:

To develop an advanced online-offline DRL framework for effective online ADNR.
To mitigate the challenges posed by RE uncertainty and variability in ADNs.
To improve the generalization and real-time performance of DRL algorithms for ADNR.

Main Methods:

Formulated ADNR as a state-driven Markov decision process (MDP) incorporating ADN operational characteristics.
Proposed a state-driven proximal policy optimization (SD-PPO) algorithm to enhance DRL generalization.
Introduced an optimized action proximal policy optimization (OA-PPO) algorithm for personalized online training.

Main Results:

The proposed online-offline DRL framework effectively reduces power loss in IEEE ADN systems.
Enhanced accommodation of renewable energy is achieved through improved ADNR strategies.
Superior computational performance compared to traditional DRL and ADNR algorithms was demonstrated.

Conclusions:

The novel online-offline DRL framework offers a robust solution for managing RE fluctuations in ADNs.
The SD-PPO and OA-PPO algorithms significantly improve DRL performance for real-time ADNR.
The approach provides a computationally efficient and effective method for enhancing ADN operation and control.