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    Area of Science:

    • Electrical Engineering
    • Computer Science
    • Operations Research

    Background:

    • Traditional power systems face challenges in transitioning to smart grids.
    • Economic Dispatch (ED) and Unit Commitment (UC) are critical for power system operation.
    • Revisiting ED and UC is essential for smart grid integration.

    Purpose of the Study:

    • To unify Economic Dispatch (ED) and Unit Commitment (UC) problems for smart grid applications.
    • To develop novel Q-learning-based optimization algorithms for power systems.
    • To address the infinite horizon Unit Commitment (UC) problem.

    Main Methods:

    • Formulation of a unified ED and UC problem.
    • Development of a centralized Q-learning-based optimization algorithm.
    • Design of a distributed counterpart algorithm by relaxing global information requirements.

    Main Results:

    • The proposed algorithms operate in an online manner, requiring no prior cost function information.
    • The algorithms effectively handle complex cost functions difficult to obtain mathematically.
    • Case studies demonstrate the practical effectiveness of both centralized and distributed approaches.

    Conclusions:

    • The unified formulation and Q-learning algorithms provide a robust solution for smart grid ED and UC.
    • The developed algorithms offer adaptability and efficiency in dynamic power system environments.
    • The research facilitates the transition to smarter, more optimized power grids.