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    Area of Science:

    • Smart Grid Technology
    • Artificial Intelligence
    • Optimization Theory

    Background:

    • The dynamic economic dispatch problem (DEDP) is crucial for smart grid efficiency.
    • Existing DEDP solutions often rely on assumptions of known or convex cost functions, limiting their applicability.
    • Coupling constraints in smart grids add complexity to traditional dispatch algorithms.

    Purpose of the Study:

    • To propose a new distributed multiagent reinforcement learning (MARL) algorithm for the DEDP in smart grids.
    • To address DEDP scenarios where cost functions are unknown or non-convex.
    • To develop a robust algorithm capable of handling coupling constraints.

    Main Methods:

    • A distributed projection optimization algorithm was designed for generation units to determine feasible power outputs under coupling constraints.
    • Quadratic functions approximated state-action value functions, enabling convex optimization for approximate DEDP solutions.
    • Neural networks (NNs) were employed in action networks to learn power output distributions based on total demand, ensuring generalization.
    • An improved experience replay mechanism enhanced training stability.

    Main Results:

    • The proposed MARL algorithm effectively solves the DEDP without prior knowledge of cost function properties.
    • The algorithm demonstrated generalization ability to predict optimal power output distribution for unseen total power demands.
    • Simulations verified the effectiveness and robustness of the developed MARL approach.

    Conclusions:

    • The novel MARL algorithm provides a flexible and effective solution for the DEDP in smart grids, even with complex constraints and unknown cost functions.
    • The integration of neural networks and an improved experience replay mechanism enhances predictive accuracy and training stability.
    • This research contributes to more efficient and reliable smart grid operations through advanced AI-driven optimization.