基于强化学习风电并网策略下的韧性分析

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中图分类号:TM614;TM743 文献标识码: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

近年来,随着全球对清洁能源的需求不断增加,在电力行业,风力发电已经成为了最具潜力的可再生能源之一,越来越多的国家将风力发电纳入到能源转型和电力供应的战略规划中[1]。(剩余9851字)

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