Optimization of Borehole Thermal Energy Storage Systems Using a Genetic Algorithm

Borehole thermal energy storage (BTES) represents cutting-edge technology harnessing the Earth’s subsurface to store and extract thermal energy for heating and cooling purposes. Achieving optimal performance in BTES systems relies heavily on selecting the right operational parameters. Among these parameters, charging and discharging flow rates play a significant role in determining the amount of heat that can be effectively recovered from the system. In this study, we introduce a genetic algorithm as an optimization tool aimed at fine-tuning these operational parameters within a baseline BTES model. The BTES model was developed using FEFLOW software and simulated over a 3-year period. After each 3-year simulation, the genetic algorithm iteratively adjusted the operational parameters to attain the optimal configuration for maximizing heat recovery from the BTES system. Additional analysis was conducted to explore the impact of BTES system size and borehole spacing on heat recovery. Results indicate that the genetic algorithm effectively optimized parameters, leading to enhanced heat recovery efficiency. Moreover, the scenario studies highlighted that closer borehole spacing correlates with higher recovery efficiency.