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pypownet

PowerBuilder

1.0.0

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Pownet重构

Pypownet：Pownet模型优化的Python库

Pownet是模拟大型电源系统的单位承诺和经济派遣的最小成本优化模型。它已用于模型柬埔寨，老挝和泰国电力系统。 Pypowneter改善了Pownet的原始实现，并简化了模型规范过程。它旨在帮助研究人员在Pownet模型上导入自己的电力系统数据，并作为优化求解器的基准。最终，我们希望我们的努力鼓励更多地区在电力系统中采用可再生能源。

要求

Pypowner用Python 3.6编写。它需要以下python包：（i）pyomo，（ii）numpy和（iii）pandas。它还需要优化求解器（例如CPLEX）。 Pypownetr已在Windows 10的Anaconda中进行了测试。

安装

您可以使用以下方式执行最小安装pypownetr ：

    git clone https://github.com/pacowong/pypownet.git
    cd pypownet
    pip install -e .

如何运行

 python pypownetr / solver . py datasets / kamal0013 / camb_2016 2016 1 2 1 glpk

如果您已经安装了[GLPK]，则将使用柬埔寨电源系统上的数据执行模型。该脚本还生成了包含每个决策变量值的.CSV文件。

引用

如果您使用Pypowner进行研究，请引用以下论文（主要来自原始作者）：

 @article { chowdhury2020pownet ,
  title = { {PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis} } ,
  author = { Chowdhury, AFM Kamal and Kern, Jordan and Dang, Thanh Duc and Galelli, Stefano } ,
  journal = { Journal of Open Research Software } ,
  volume = { 8 } ,
  number = { 1 } ,
  year = { 2020 } ,
  publisher = { Ubiquity Press }
}

 @article { chowdhury2020expected ,
  title = { {Expected Benefits of Laos' Hydropower Development Curbed by Hydroclimatic Variability and Limited Transmission Capacity: Opportunities to Reform} } ,
  author = { Chowdhury, AFM Kamal and Dang, Thanh Duc and Bagchi, Arijit and Galelli, Stefano } ,
  journal = { Journal of Water Resources Planning and Management } ,
  volume = { 146 } ,
  number = { 10 } ,
  pages = { 05020019 } ,
  year = { 2020 } ,
  publisher = { American Society of Civil Engineers }
}

 @article { chowdhury2020greater ,
  title = { {The Greater Mekong's climate-water-energy nexus: how ENSO-triggered regional droughts affect power supply and CO2 emissions} } ,
  author = { Chowdhury, Kamal AFM and Dang, Thanh Duc and Nguyen, Hung TT and Koh, Rachel and Galelli, Stefano } ,
  journal = { Earth and Space Science Open Archive ESSOAr } ,
  year = { 2020 } ,
  publisher = { American Geophysical Union }
}

 @misc { pypownetr ,
    author = { Pak-Kan Wong } ,
    title = { {PyPowNetR: A Python Library for Refactored PowNet Model Optimization} } ,
    year = { 2020 } ,
    publisher = { GitHub } ,
    journal = { GitHub repository } ,
    howpublished = { url{https://github.com/pacowong/pypownet} } ,
}