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项目作者： rl-institut

项目描述 ：
Comparison of urbs & oemof
高级语言： Python
项目地址: git://github.com/rl-institut/urbs-oemof.git
创建时间： 2018-11-07T13:39:07Z
项目社区：https://github.com/rl-institut/urbs-oemof
开源协议：GNU General Public License v3.0
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Comparison of urbs & oemof

This script allows the user to get some understanding of the differences between the two energy modelling frameworks; urbs & oemof.

Frameworks

urbs: https://github.com/tum-ens/urbs

oemof: https://github.com/oemof

Input Data

mimo.xlsx (https://github.com/rl-institut/urbs-oemof/blob/master/mimo.xlsx) is a simpler version of the example input data (https://github.com/tum-ens/urbs/blob/master/mimo-example.xlsx) of urbs.

Required Packages

oemof, os, sys, logging, pandas, numpy, networkx, matplotlib, datetime, pprint, getpass, oedialect, sqlalchemy, geoalchemy2

How to Use

  • After installing the above mentioned required packages, run mimo.py via python3 mimo.py.
  • The script should output the differences as text on cmd.
  • Under result folder, generated plots can be found.

complexity

scala

  • 10% most basic system with a node having a power plant and a demand
  • 15% 3 node system, each having the same power plant and the same demand
  • 25% 3 node system, each having the same 4 power plants and the same demand
  • 30% 3 node system, same 3 renewable power plants are implemented to 25%
  • 40% 3 node system, same PP & rPP, but different demand
  • 50% 3 node system, same amount of PP & rPP as 40%, but different specification (capacity, costs, etc…)
  • 51% 7 more nodes are implemented to 50%

code

10% - Nd_0[PP_0] & Nd_0[Dmd_0]\
15% - Nd_i[PP_0] & Nd_i[Dmd_0] // i=0,1,2\
25% - Nd_i[PP_j] & Nd_i[Dmd_0] // i=0,1,2 // j=0,1,2,3\
30% - Nd_i[PP_j] & Nd_i[rPP_i] & Nd_i[Dmd_0] // i=0,1,2 // j=0,1,2,3\
40% - Nd_i[PP_j] & Nd_i[rPP_i] & Nd_i[Dmd_0[i]] // i=0,1,2 // j=0,1,2,3\
50% - Nd_i[PP_j[i]] & Nd_i[rPP_i[i]] & Nd_i[Dmd_0[i]] // i=0,1,2 // j=0,1,2,3\
51% - Nd_x[PP_j[x]] & Nd_x[rPP_i[x]] & Nd_x[Dmd_0[x]] x=0-10 // i=0,1,2 // j=0,1,2,3

legend

  • PP: Power Plant
  • rPP: Renewable Power Plant
  • Dmd: Demand
  • Nd: Node-Area-Region

example with 50% complexity

Nd_0[PP_0[0],PP_1[0],PP_2[0],PP_3[0]] & Nd_0[rPP_0[0],rPP_1[0],rPP_2[0]] & Nd_0[Dmd_0[0]]\
Nd_1[PP_0[1],PP_1[1],PP_2[1],PP_3[1]] & Nd_1[rPP_0[1],rPP_1[1],rPP_2[1]] & Nd_1[Dmd_0[1]]\
Nd_2[PP_0[2],PP_1[2],PP_2[2],PP_3[2]] & Nd_2[rPP_0[2],rPP_1[2],rPP_2[2]] & Nd_2[Dmd_0[2]]