Optimal well distance selection for Aquifer Thermal Energy Storage (ATES) under geological uncertainty
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Aquifer Thermal Energy Storage (ATES) systems have attracted global attention for their substantial storage capacity and cost-effectiveness. Positioned as an environmentally sustainable solution, ATES harnesses excessive heat resources in urbanized areas to ensure geothermal district heat and cooling. The design and long-term forecasting of ATES is limited by the scarcity of spatiotemporal data, leading to inherent parametric uncertainty. This study addresses a critical aspect of ATES optimization by focusing on the design of optimal well distancing and injection/extraction regimes under geological uncertainty. Key performance indicators, such as the heat recovery factor and heat storage capacity, are crucial from the geoenergy perspective. Essential parameters for ATES prediction are besides the hydraulic and thermal reservoir properties the operation temperatures and the geothermal fluid injection/extraction regime. In this research, we present a stochastic optimization workflow that considers geologically reasonable parameters as prior uncertainties, with well distancing and injection/extraction regimes as optimization parameters. The objective functions involve heat recovery and thermal efficiency. The overarching goal is to create a comprehensive and reliable tool for the optimal design of ATES systems under uncertainty. The proposed methodology is applied to a numerical 3D thermal-hydraulic model of a planned ATES system in Berlin. Through this work, we aim to contribute to the advancement of optimal well distance, ensuring robustness and adaptability in the face of geological uncertainties
