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Related Experiment Video

Updated: Aug 5, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems.

Suwei Zhai1, Wenyun Li2, Zhenyu Qiu3

  • 1Electric Power Research Institute of China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, China.

Entropy (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

This study introduces a novel wind-storage cooperation strategy using Dueling Double Deep Q-Network (D3QN). The approach optimizes energy management by integrating demand response loads for enhanced grid stability and efficiency.

Keywords:
deep neural networksenergy storage systemreinforcement learningwind farm

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Area of Science:

  • Artificial Intelligence
  • Information Theory
  • Energy Systems Engineering

Background:

  • Reinforcement learning is a key information theory with growing applications.
  • Effective wind-storage cooperation is crucial for grid stability and renewable energy integration.
  • Demand response loads, including residential price-responsive and thermostatically controlled loads (TCLs), play a vital role in grid flexibility.

Purpose of the Study:

  • To develop an optimized cooperative decision-making strategy for wind and energy storage systems.
  • To incorporate diverse demand response loads into the wind-storage cooperation model.
  • To leverage advanced deep reinforcement learning for improved energy management.

Main Methods:

  • A novel wind-storage cooperative model was proposed, integrating wind farms, energy storage, power grids, and demand response loads (residential price response and TCLs).
  • A new decision-making mechanism was designed, combining direct control of TCLs and indirect control of price response loads.
  • The Dueling Double Deep Q-Network (D3QN) algorithm was employed to solve the complex cooperative decision-making problem.

Main Results:

  • The proposed D3QN-based strategy effectively optimizes the decision-making process in a wind-storage cooperation system.
  • Numerical results demonstrated the superior performance of the D3QN algorithm in managing energy resources.
  • The integration of demand response loads enhanced the system's overall efficiency and responsiveness.

Conclusions:

  • The D3QN algorithm provides an effective solution for optimizing wind-storage cooperative decision-making.
  • The proposed model and mechanism successfully integrate various demand response loads, improving system flexibility.
  • This research contributes to the advancement of intelligent energy management systems for renewable energy integration.