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English(EN) Genetic Algorithm Based Coordination and Optimization Model for Generation Grid Load Storage in Active Distribution Networks

模糊逻辑和遗传算法优化电网储能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新颖的优化框架，该框架集成了模糊逻辑和遗传算法来管理有源配电网中的储能和发电。该方法使用模糊集表示可再生能源、负荷模式和市场价格来模拟系统不确定性，例如天气变化和用户需求波动。遗传算法然后结合这些模糊元素，即使在不确定的条件下也能提高系统稳定性和优化运营成本。在IEEE-69电力系统上的模拟表明，与传统的确定性方法相比，这种模糊遗传算法策略能够有效减少技术约束并避免不可行的网络适应。 AI

影响 这项研究为管理电网中的能源分配和存储提供了一种更稳健的方法，尤其是在整合可再生能源和需求波动的情况下。

排序理由 详细介绍电力系统新计算框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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模糊逻辑和遗传算法优化电网储能

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详细介绍电力系统新计算框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Sheng Wang ·

    基于遗传算法的有源配电网发电网格负荷存储协调优化模型

    Create an optimization framework that combines fuzzy logic and genetic algorithms for risk assessment and coordination of generation, grid connection, load, and energy storage facilities in active distribution networks. In order to capture the system uncertainties caused by weath…

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