Recovery efficiency in high-temperature aquifer thermal energy storage systems

Highlights

• The recovery efficiency, R, of aquifer thermal energy storage systems is computed

• A wide range of operating parameters are covered by the simulations

• ATES may be viable up to 300 degC and daily cycles are very efficient

• R is written in terms of the Rayleigh number; also a CNN is strongly predictive

• R may be estimated accurately only when an accurate fluid equation of state is used

Abstract

Aquifer Thermal Energy Storage (ATES) uses excess thermal energy to heat water which is stored in an aquifer until it is needed, at which time the hot water is recovered and the heat used for some purpose e.g. electricity generation. The recovery efficiency (i.e. the ratio of heat energy recovered to heat energy injected, $R$) is one of the most important factors dictating the viability of ATES systems.

The variation of $R$ with various aquifer properties and operating parameters is explored for high temperature (HT) ATES systems with injection temperatures $\ge 90{}^{\circ }\mathrm{C}$, extending the results of previous studies to higher temperatures and a broader range of aquifer properties and operating conditions. $R$ values are calculated using numerical models of a single-well ATES system, which is validated by comparison with previous field and modelling studies.

The results show that HT-ATES may be viable with injection temperatures as high as 300 ${}^{\circ }\mathrm{C}$, depending on the aquifer properties and operating parameters. Daily cycles are very efficient over a broad range of conditions, whereas the efficiency of annual cycles is much more variable. The most important parameters governing $R$ are aquifer thickness, injection temperature, horizontal and vertical permeability, and dispersion length.

The $R$ values are used to derive an improved version of the Rayleigh number relationship proposed by Schout et al. (2014), extending the applicability of this relationship to daily cycles and improving its accuracy for annual cycles. An alternative method for estimating $R$ using a convolutional neural network is proposed.

The calculated $R$ values may be considered best-case because aspects such as background groundwater flow and geochemical effects are ignored. Practical factors such as energy supply/demand requirements, reservoir and above-ground engineering, financial or regulatory aspects, and public acceptance are not considered. Nevertheless, the results of this study can be used for rapid screening of large areas for potential HT-ATES sites, defining requirements for potential sites, and estimating $R$ values for specific sites, before performing detailed feasibility studies.