基于強化學習風電并網(wǎng)策略下的韌性分析

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中圖分類號：TM614;TM743 文獻標識碼：A

Abstract：To explore the impact of wind power station grid-connection sites on the resilience of power networks，this paper introduces a new analytical framework for assessing the resilience of wind power network. By integrating the network's structural and functional models and applying relevant resilience assessment metrics，we propose a Q-Learning-based grid-connection strategy to identify the optimal grid-connection locations for wind power station. We validate this strategy using the IEEE 118 power grid model，which incorporates wind power grid-connection. Our research shows that the Q-Learning-based grid-connection strategy surpasses traditional heuristic methods and genetic algorithms in reducing operational costs and the risk of overload， highlighting the crucial role of strategic grid-connection in strengthening the network's resilience.

Keywords： complex network；resilience; Q -Learning algorithm;wind power grid connection

0 引言

近年來，隨著全球?qū)η鍧嵞茉吹男枨蟛粩嘣黾?，在電力行業(yè)，風力發(fā)電已經(jīng)成為了最具潛力的可再生能源之一，越來越多的國家將風力發(fā)電納入到能源轉(zhuǎn)型和電力供應的戰(zhàn)略規(guī)劃中[1]。（剩余9851字）