# A Combined Column-Generation and Lagrangian-Relaxation Approach for a Decentralized Scheduling Strategy of Heating Systems
- [Final presentation](Presentation.pdf)
- [Thesis](Thesis.pdf)
## Astract:
In this thesis, a decentralized day-ahead scheduling strategy for the coordination of heating supply systems has been implemented. The aim is thereby to enhance the integration of renewable energy sources and balance electricity demand and supply. This scheduling problem can be formulated as a mixed integer linear program (MILP) and solved using a column generation algorithm based on the Dantzig-Wolfe decomposition technique. Instead of solving the Dantzig-Wolfe masterproblem in each iteration, we propose a concept that combines a column generation algorithm with a Lagrangian relaxation approach and uses the subgradient method to optimize the shadow prices from the dual side. Thereby, the proposed algorithm preserves the advantages of a conventional column generation approach, i.e. its flexibility, scalability and limited data exchange. The results for a cluster of 100 buildings show that this method substantially improves the convergence of the column generation algorithm and delivers fairly optimal solutions for the scheduling problem in a reasonable amount of time. Moreover, different strategies for obtaining integer solutions at the end of the algorithm have been investigated to resolve problems arising from non- unique shadow prices. While the proposed integering step in this thesis does not deliver solutions of acceptable quality, the basic approach seems promising with room for improvement.
## Software:
- Python 2.7
- Gurobi 6.0.0
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电气代码:030 电负荷、热负荷数据.zip
共83个文件
py:38个
csv:20个
txt:17个
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电气代码:030 电负荷、热负荷数据.zip (83个子文件)
电气代码:030 电负荷、热负荷数据
说明.txt 132B
030 电负荷、热负荷数据
wind_speed.txt 206KB
具体可看Decentralized-Scheduling-Strategy-of-Heating-Systems-master
Decentralized-Scheduling-Strategy-of-Heating-Systems-master
Code
cg_lr_combination.py 13KB
plot.py 20KB
column_generation.py 8KB
model_subproblem_hp.py 5KB
model_subproblem_chp.py 7KB
basic_functions
__init__.py 0B
read_txt.py 1KB
mathfunctions.py 616B
heating_tech.py 4KB
model_masterproblem.py 13KB
object_hp.py 4KB
object_chp.py 5KB
object_master.py 8KB
data.py 4KB
microgrid_functions
__init__.py 0B
renewables.py 2KB
heat_pumps
HP_characteristics.py 8KB
__init__.py 0B
constants.py 2KB
input_data
wind_speed.txt 206KB
input_data_chp.csv 2KB
el_demand_chp.csv 11.63MB
temperature.txt 225KB
standard_load_profile.txt 445KB
sun_direct.txt 219KB
heat_demand_hp.csv 9.91MB
input_data_hp.csv 2KB
el_demand_hp.csv 11.67MB
heat_demand_chp.csv 9.23MB
LICENSE 34KB
Thesis.pdf 4.21MB
Presentation.pdf 1.55MB
README.md 2KB
el_demand_chp.csv 11.63MB
temperature.txt 225KB
standard_load_profile.txt 445KB
sun_direct.txt 219KB
heat_demand_hp.csv 9.91MB
el_demand_hp.csv 11.67MB
heat_demand_chp.csv 9.23MB
__MACOSX
030 电负荷、热负荷数据
._heat_demand_hp.csv 218B
._heat_demand_chp.csv 218B
._standard_load_profile.txt 218B
._el_demand_hp.csv 218B
具体可看Decentralized-Scheduling-Strategy-of-Heating-Systems-master
Decentralized-Scheduling-Strategy-of-Heating-Systems-master
Code
._object_chp.py 218B
._object_master.py 218B
basic_functions
._mathfunctions.py 218B
.___init__.py 218B
._heating_tech.py 218B
._read_txt.py 218B
._plot.py 218B
._constants.py 218B
._model_subproblem_hp.py 218B
microgrid_functions
._renewables.py 218B
.___init__.py 218B
._object_hp.py 218B
heat_pumps
.___init__.py 218B
._HP_characteristics.py 218B
._data.py 218B
._model_masterproblem.py 218B
input_data
._heat_demand_hp.csv 218B
._heat_demand_chp.csv 218B
._input_data_hp.csv 218B
._standard_load_profile.txt 218B
._el_demand_hp.csv 218B
._el_demand_chp.csv 218B
._wind_speed.txt 218B
._sun_direct.txt 218B
._temperature.txt 218B
._input_data_chp.csv 218B
._model_subproblem_chp.py 218B
._cg_lr_combination.py 218B
._column_generation.py 218B
._Presentation.pdf 218B
._LICENSE 218B
._Thesis.pdf 218B
._README.md 218B
._el_demand_chp.csv 218B
._wind_speed.txt 218B
._sun_direct.txt 218B
._temperature.txt 218B
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