In this paper, we present the application of Evolutionary Algorithms (EAs) and linear programming for minimizing thermal impacts in the ground by operating a low-enthalpy geothermal plant with a field of multiple borehole heat exchangers (BHEs). The new methodology is demonstrated on two synthetic case studies with 36 BHEs that are grounded in reality and operated to produce given seasonal heating energy demand. We compare the performance of six different Evolutionary Algorithms (EAs) (two Differential Evolution variants, Particle Swarm Optimization, two Evolution Strategy based Algorithms, real valued Genetic Algorithm) and Monte-Carlo random search to find the optimal BHE positions. Additionally, linear programming is applied to adjust the energy extraction (loads) for the individual BHEs in the field. Both optimization steps are applied separately and in combination, and the achieved system improvements are compared to the conditions for the non-optimized case. The EAs were able to find constellations that cause less pronounced temperature changes in the subsurface (18% - 25%) than those associated with non-optimized BHE fields. Further, we could show that exclusive optimization of BHE energy extraction rates delivers slightly better results than the optimization of BHE positions. Combining both optimization approaches is the best choice and, ideally, adjusts the geothermal plant.
@inproceedings{Beck12,
author = {Markus Beck and Michael de Paly and Jozsef Hecht-Mendez and Peter
Bayer and Andreas Zell},
title = {{Evaluation of the Performance of Evolutionary Algorithms for Optimization
of Low-Enthalpy Geothermal Heating Plants}},
booktitle = {Genetic and Evolutionary Computation Conference, GECCO-2012},
year = {2012},
pages = {1047-1054},
address = {Philadelphia, USA},
month = jul,
abstract = {In this paper, we present the application of Evolutionary Algorithms
(EAs) and linear programming for minimizing thermal impacts in the
ground by operating a low-enthalpy geothermal plant with a field
of multiple borehole heat exchangers (BHEs). The new methodology
is demonstrated on two synthetic case studies with 36 BHEs that are
grounded in reality and operated to produce given seasonal heating
energy demand. We compare the performance of six different Evolutionary
Algorithms (EAs) (two Differential Evolution variants, Particle Swarm
Optimization, two Evolution Strategy based Algorithms, real valued
Genetic Algorithm) and Monte-Carlo random search to find the optimal
BHE positions. Additionally, linear programming is applied to adjust
the energy extraction (loads) for the individual BHEs in the field.
Both optimization steps are applied separately and in combination,
and the achieved system improvements are compared to the conditions
for the non-optimized case. The EAs were able to find constellations
that cause less pronounced temperature changes in the subsurface
(18% - 25%) than those associated with non-optimized BHE fields.
Further, we could show that exclusive optimization of BHE energy
extraction rates delivers slightly better results than the optimization
of BHE positions. Combining both optimization approaches is the best
choice and, ideally, adjusts the geothermal plant.},
}