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Related Experiment Video

Updated: Jan 20, 2026

Pricing: Competitive Bidding
01:22

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258

Event-Triggered Multiagent Optimization for Two-Layered Model of Hybrid Energy System With Price Bidding-Based Demand

Huifeng Zhang, Dong Yue, Chunxia Dou

    IEEE Transactions on Cybernetics
    |September 4, 2019
    PubMed
    Summary

    This study introduces an event-triggered multiagent strategy for optimizing hybrid energy systems. It effectively manages intermittent energy and demand response, ensuring system stability and efficient operation.

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    Area of Science:

    • Energy Systems Engineering
    • Artificial Intelligence in Energy
    • Optimization and Control

    Background:

    • Hybrid energy systems face operational challenges due to intermittent energy sources and dynamic load demand response (DR).
    • Uncertainty and dynamic characteristics necessitate advanced control strategies for optimal system performance and stability.

    Purpose of the Study:

    • To propose an event-triggered multiagent coordinated optimization strategy for hybrid energy systems.
    • To address the complexities of intermittent energy and demand response for improved system operation.
    • To develop a robust framework for managing diverse energy resources and consumer load strategies.

    Main Methods:

    • A two-layered architecture featuring a price-bidding-based demand response (DR) model utilizing Nash equilibrium theory.
    • Four specialized agents designed for intermittent power uncertainty, dynamic economic dispatch (DED) of thermal units, energy storage scheduling, and load-shifting.
    • An event-triggered mechanism and consensus algorithm for upper-layer coordination and lower-layer subsystem optimization, respectively.

    Main Results:

    • The proposed event-triggered multiagent optimization strategy demonstrates efficiency in stabilizing hybrid energy systems.
    • Simulation results validate the effectiveness of the coordinated control approach in managing energy resources and load.
    • The intelligent control method, involving generator switching and load curtailment, proves effective for system stability.

    Conclusions:

    • The event-triggered multiagent coordinated optimization strategy is a promising solution for the operational challenges in hybrid energy systems.
    • This approach effectively handles uncertainty and dynamic characteristics, leading to improved system stability and efficiency.
    • The developed framework provides a robust method for integrating and optimizing diverse energy components and demand-side management.