为了研究风电场并网位置对电力网络韧性的影响,建立了一个新的分析框架来研究风电网络的韧性。在该框架中,结合了网络结构和功能模型,并应用了相应的韧性评估指标,提出了一种基于Q-Learning算法的并网策略,用以确定风电场的最优并网位置。使用含风力发电的IEEE118电网模型验证了这一策略的有效性。研究显示,基于Q-Learning算法的并网策略在减少运行成本和过载风险方面优于传统启发式方法及遗传算法,强调了合理并网策略在增强电网韧性的关键作用。
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.
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