Frontiers in Energy Research (Oct 2024)
Load frequency optimal control of the hydropower-photovoltaic hybrid microgrid system based on the off-policy integral reinforcement learning algorithm
Abstract
With the promotion and development of clean energy, it is challenging to ensure the optimization of control performance in frequency control of the hydropower-photovoltaic hybrid microgrid system caused by the output power fluctuation of photovoltaic power generation. In this study, an optimal load frequency controller (LFC) for a hydropower-photovoltaic hybrid microgrid system was designed to improve the dynamic response when the load and photovoltaic output power are perturbed based on the off-policy integral reinforcement learning algorithm. First, a mechanism model of the hydropower-photovoltaic hybrid microgrid system was established. Next, the LFC problem was transformed into a zero-sum game control problem based on the characteristics of the power system. Subsequently, three neural networks were employed to approximate the Nash equilibrium solution of the zero-sum game with historical input and output data when the system dynamics are completely unknown. Finally, simulation experiments were conducted to verify the effectiveness and optimality of the proposed method. The introduction of this method provides a new perspective for frequency control for the hydropower-photovoltaic hybrid microgrid system.
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