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An Intelligent Scheduling Approach for Electric Power Generation

机译：发电的智能调度方法

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摘要

Although China is vigorously developing clean energy and nuclear power, the thermal power generation (mainly coal power) is still the most important power generation method at present. Economic load dispatch (ELD) is a typical optimization problem in power systems which lots of researchers are trying to explore. The purpose of ELD is to increase the efficiency of thermal power generation under the conditions of load and operational constraints. When it comes to power generation scheduling, manual operation is still the main form, which is inefficient. In order to use a large amount of historical power generation data to improve the efficiency of power generation scheduling and achieve the effect of energy conservation, we propose an intelligent power generation scheduling system based on Deep neural networks (DNN) and Ant colony optimization (ACO). Experiments show that our DNN algorithm can predict the unit coal consumption precisely. Compared with the dynamic programming algorithm and equal differential increment rate algorithm, ACO can complete power generation scheduling tasks more quickly and efficiently.
机译：尽管中国正在大力发展清洁能源和核能，但火力发电（主要是煤炭发电）仍是目前最重要的发电方式。经济负载分配（ELD）是电力系统中的典型优化问题，许多研究人员正在尝试探索这一问题。 ELD的目的是在负载和运行限制条件下提高火力发电的效率。对于发电调度，手动操作仍然是主要形式，效率低下。为了利用大量的历史发电数据来提高发电调度效率并达到节能效果，我们提出了一种基于深度神经网络（DNN）和蚁群优化（ACO）的智能发电调度系统。 ）。实验表明，我们的DNN算法可以准确预测单位耗煤量。与动态规划算法和等差增量率算法相比，ACO可以更快，更高效地完成发电调度任务。

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  • 来源
    《Chinese Journal of Electronics》 |2018年第6期|1170-1175|共6页
  • 作者

    Yu Li; Jin Xin; Li Zeng; Xiong Yan; Huang Wenchao;

  • 作者单位

    Anhui Univ Sci & Technol, Coll Comp Sci & Engn, Huainan 232001, Peoples R China;

    Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China;

    Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China;

    Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China;

    Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Deep learning; Ant colony optimization (ACO); Smart grid;

    机译：深度学习;蚁群优化（ACO）;智能电网;

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