Geothermal energy use from shallow groundwater systems is attractive for the supply of heat and hot water to buildings. It offers economic and environmental advantages over traditional fossil-fuel based technologies, in particular when large scale systems are well adapted to the always unique hydrogeological conditions. Computer based numerical simulations are used to examine the performance of multiple borehole heat exchangers installed in the ground. This paper demonstrates how evolutionary algorithms can be utilized to configure the elements of a geothermal system in an ideal way, and thus substantially enhance the energy extraction rate in comparison to standardized approaches. Differential evolution (DE), evolution strategies (ES) and particle swarm optimizers (PSO) are combined with a local search approach and compared with respect to their efficiency in the optimization of synthetic, real case oriented and static systems. First results are promising, especially for the PSO and the DE with the local search approach.
[doi]
@inproceedings{Beck10,
author = {Markus Beck and Jozsef Hecht-Mendez and Michael de Paly and Peter
Bayer and Philipp Blum and Andreas Zell},
title = {Optimization of the Energy Extraction of a Shallow Geothermal System},
booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
year = {2010},
pages = {3622-3628},
address = {Barcelona, Spain},
month = jul,
abstract = {Geothermal energy use from shallow groundwater systems is attractive
for the supply of heat and hot water to buildings. It offers economic
and environmental advantages over traditional fossil-fuel based technologies,
in particular when large scale systems are well adapted to the always
unique hydrogeological conditions. Computer based numerical simulations
are used to examine the performance of multiple borehole heat exchangers
installed in the ground. This paper demonstrates how evolutionary
algorithms can be utilized to configure the elements of a geothermal
system in an ideal way, and thus substantially enhance the energy
extraction rate in comparison to standardized approaches. Differential
evolution (DE), evolution strategies (ES) and particle swarm optimizers
(PSO) are combined with a local search approach and compared with
respect to their efficiency in the optimization of synthetic, real
case oriented and static systems. First results are promising, especially
for the PSO and the DE with the local search approach.},
doi = {10.1109/CEC.2010.5585921},
}