View in EDS

RETRACTED: Generating adversarial deep reinforcement learning -based frequency control of Island City microgrid considering generalization of scenarios.

Saved in:
Bibliographic Details
Title: RETRACTED: Generating adversarial deep reinforcement learning -based frequency control of Island City microgrid considering generalization of scenarios.
Authors: Wang, Houtianfu, Zhang, Zhecong, Wang, Qixin
Source: Frontiers in Energy Research; 2025, p1-11, 11p
Subject Terms: REINFORCEMENT learning, MICROGRIDS, ELECTRIC power distribution grids, AUTOMATIC frequency control, ALGORITHMS
Abstract: The increasing incorporation of new energy sources into power grids introduces significant variability, complicating traditional load frequency control (LFC) methods. This variability can cause frequent load disturbances and severe frequency fluctuations in island city microgrids, leading to increased generation costs. To tackle these challenges, this paper introduces a novel Data knowledge-driven load frequency control (DKD-LFC) method, aimed at optimizing the balance between generation cost and frequency stability in isolated microgrids with high renewable energy integration. The DKD-LFC replaces conventional controllers with agent-based systems, utilizing reinforcement learning for adaptive frequency control in complex environments. A new policy generation algorithm, based on generative adversarial-proximal policy optimization (DAC-PPO), is proposed. This algorithm extends the traditional Actor-Critic framework of the Proximal Policy Optimization (PPO) by incorporating a Discriminator network. This network evaluates whether the input state-action pairs align with current or expert policies, guiding policy updates toward expert policies during training. Such an approach enhances the algorithm's generalization capability, crucial for effective LFC application in diverse operational contexts. The efficacy of the DKD-LFC method is validated using the isolated island city microgrid LFC model of the China Southern Grid (CSG), demonstrating its potential in managing the complexities of modern power grids. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Energy Research is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:The increasing incorporation of new energy sources into power grids introduces significant variability, complicating traditional load frequency control (LFC) methods. This variability can cause frequent load disturbances and severe frequency fluctuations in island city microgrids, leading to increased generation costs. To tackle these challenges, this paper introduces a novel Data knowledge-driven load frequency control (DKD-LFC) method, aimed at optimizing the balance between generation cost and frequency stability in isolated microgrids with high renewable energy integration. The DKD-LFC replaces conventional controllers with agent-based systems, utilizing reinforcement learning for adaptive frequency control in complex environments. A new policy generation algorithm, based on generative adversarial-proximal policy optimization (DAC-PPO), is proposed. This algorithm extends the traditional Actor-Critic framework of the Proximal Policy Optimization (PPO) by incorporating a Discriminator network. This network evaluates whether the input state-action pairs align with current or expert policies, guiding policy updates toward expert policies during training. Such an approach enhances the algorithm's generalization capability, crucial for effective LFC application in diverse operational contexts. The efficacy of the DKD-LFC method is validated using the isolated island city microgrid LFC model of the China Southern Grid (CSG), demonstrating its potential in managing the complexities of modern power grids. [ABSTRACT FROM AUTHOR]
ISSN:2296598X
DOI:10.3389/fenrg.2024.1377465