Probabilistic Framework Evaluates Storing Surplus Renewable Electricity as Aquifer Heat

A new demand-oriented operational framework reveals how high-temperature aquifer thermal energy storage systems can convert surplus renewable electricity into useful seasonal heat. Increasing shares of variable renewable electricity across power systems frequently generate periods of excess power, creating an urgent operational requirement for flexible technologies that can absorb and utilize generation spikes. Because consumer heat demand remains heavily concentrated in colder winter months, long-duration seasonal thermal storage is essential to bridge this temporal gap, and high-temperature aquifer thermal energy storage represents a prominent subsurface technology capable of fulfilling this seasonal balancing role.

Instead of viewing subsurface storage as a fixed geological property, the research team developed an operational probability of success framework that evaluates whether high-temperature aquifer systems can realistically convert historically observed surplus electricity into delivered seasonal warmth. Within this framework, operational success is defined dynamically as the probability that delivered heat meets or exceeds an explicit, specified thermal requirement set by downstream demand targets.

To establish a physically grounded reference case, the researchers modeled the existing Neubrandenburg aquifer thermal energy storage system in Germany. The study utilized historical German electricity data recorded between 2015 and 2022 to construct empirical four-week surplus distributions. These empirical power distributions captured real-world energy-system variability and were directly coupled to a chronological Monte Carlo inventory model designed to simulate ongoing subsurface charging and discharging cycles under realistic operational conditions.

The chronological Monte Carlo inventory model tracked operational constraints across each injection decision, accounting for charging availability, well thermal throughput, adopted storage limits, and subsurface thermal recovery. In the baseline modeled configuration, the system operated with a thermal power rating of approximately 3.72 megawatts, an adopted maximum storage limit of 12 gigawatt-hours, and a thermal recovery factor of 0.72. The simulation evaluated 20,000 retained Monte Carlo cycles, first generating an empirical distribution of achievable seasonal heat delivery that was subsequently tested against alternative heat-demand benchmarks.

Across the 20,000 retained baseline Monte Carlo cycles, the modeled system produced a mean seasonal heat delivery of 8.551 gigawatt-hours. The simulations revealed high operational reliability for baseline heat generation, reaching the modeled upper heat delivery ceiling of 8.640 gigawatt-hours in 94.63 percent of the evaluated cycles.

Detailed constraint diagnostics within the framework identified which physical and operational boundaries actually restricted injection decisions. In the baseline case, well thermal throughput represented the unique limiting factor in 57.06 percent of injection decisions, while the remaining available storage space formed the bottleneck in 28.82 percent of decisions.

Sensitivity analyses demonstrated that increasing the thermal power of the system alters the range of heat-demand targets that can be satisfied with high probability. However, the operational benefits of expanding thermal power gradually diminish as the system approaches its upper modeled delivery ceiling. The researchers also found that equivalent non-interacting doublet well configurations display the identical trade-off between well throughput and subsurface storage capacity limits.

The authors conclude that the operational success of high-temperature aquifer thermal energy storage depends jointly on charging opportunities, thermal power, storage capacity, thermal recovery, system configuration, and the specific thermal target chosen to define success. The resulting probabilistic modeling framework offers a transferable screening methodology that directly links historical energy-system fluctuations with subsurface physical constraints and explicit consumer heat-delivery requirements.

Subject of Research: Operational probability of success framework for high-temperature aquifer thermal energy storage

Article Title: When the grid overflows: a novel probabilistic framework to assess the success of storing excess electricity as heat in high-temperature ATES (HT-ATES) systems

Article References: Sangin, S., Rüter, H., & Kirsten, U. (2026). When the grid overflows: a novel probabilistic framework to assess the success of storing excess electricity as heat in high-temperature ATES (HT-ATES) systems. Geothermal Energy. https://doi.org/10.1186/s40517-026-00421-5

Image Credits: AI Generated

DOI: 10.1186/s40517-026-00421-5

Keywords: high-temperature aquifer thermal energy storage, variable renewable electricity, seasonal thermal storage, surplus electricity storage, probability of success framework, Monte Carlo inventory model, Neubrandenburg reference system, well thermal throughput, thermal recovery factor, subsurface energy storage, German electricity grid data, heat demand targets, doublet well configurations, charging availability, energy system variability, power-to-heat conversion