District heating systems must decarbonize by replacing fossil fuel-based heat sources with sustainable alternatives. To fully utilize the capacity of renewable sources, seasonal thermal energy storage is necessary due to seasonal supply–demand mismatches. High-Temperature Aquifer Thermal Energy Storage (HT-ATES) offers a promising solution, but its cost-effective deployment requires coordinated sizing with the sustainable heat source, which has received limited attention in literature. This study presents a techno-economic and renewable share analysis of district heating systems incorporating deep geothermal heat, solar thermal collectors, HT-ATES, and gas boilers. We identified representative heat demand profiles for different climates by clustering to ensure broader applicability of the findings. We show that the demand profile is important for the cost-effectiveness of district heating. The results show that HT-ATES is cost-effective in most scenarios compared to natural gas boilers, particularly when paired with a geothermal source. Geothermal energy was generally more economically favorable than solar thermal collectors. Achieving 100% renewable heat supply is cost-inefficient because it requires large additional capacity for limited additional load, increasing costs by 15% compared to 99% renewable share. However, 90% renewable share can be reached with only 5% cost increase compared to the optimum, using geothermal energy. These insights provide guidance for district heating designers, operators and policymakers on optimal component sizing and promote the informed use of HT-ATES to support cost-effective decarbonization of district heating. Representative demand profiles are expected to be used often in research, as they proved influential on the levelized cost of heat.

Integrating renewable energy into district heating creates a heat supply–demand mismatch that High-Temperature Aquifer Thermal Energy Storage (HT-ATES) can help address. However, the potential greenhouse gas emission reduction and financial benefits of HT-ATES have received limited attention. Additionally, the interplay between the demand, supply components, and HT-ATES has been overlooked, while the assessment of integrating HT-ATES into a district heating system is crucial to understanding the benefits of the HT-ATES implementation. This study evaluates the integration of HT-ATES into a district heating system, focusing on both economic and environmental performance indicators. It novelly accounts for the dynamic operational interactions between HT-ATES and other system components, enabling a more realistic assessment of operational choices. The model is applied to a case study of a simplified district heating system. The results show that the relative size of the heat supplier compared to heat demand is a key determinant of the cost-effectiveness of HT-ATES. In the case study, a geothermal doublet reduced the levelized cost of heat by 25–37 €/MWh compared to a gas boiler, while also reducing reliance on fossil fuels. In contrast, HT-ATES had a limited impact on total system costs, regardless of whether it operated when stored heat was available or was used for peak shaving. Nevertheless, HT-ATES increased the renewable energy share by 9%–18% across all scenarios. Furthermore, the optimal geothermal capacity differed depending on whether HT-ATES was included. Finally, while a high renewable energy share can be cost-effective, achieving 100% renewable heat was found to be highly cost-ineffective in this case. These results support informed decision-making on HT-ATES implementation under appropriate system design conditions.

Following publication of the original article, the author reported that the citations are not in brackets throughout the article. The citation currently reads: IEA (2023), Cozzi et al. (2020)., Hermans et al. (2018) Bloemendal and Hartog (2018) The citation should read: (IEA 2023), (Cozzi et al. 2020), (Hermans et al. 2018) (Bloemendal and Hartog 2018). The original article (Geerts et al. 2025) has been corrected.

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) has the potential to significantly increase the renewable heat share in heating systems. However, HT-ATES has not been implemented in the current energy system models because the widely applied numerical models for HT-ATES are computationally expensive. This leads to a lack of HT-ATES assessment from an energy system perspective. Therefore, an accurate and computationally efficient model that is widely applicable is needed to facilitate such implementation. This research aimed to develop a novel data-driven model that generates the temperature profile of an HT-ATES accurately and computationally efficiently. A trained machine learning algorithm predicts the recovery efficiency for an HT-ATES system, which, combined with other parameters, enables a nearest neighbor search to identify a suitable temperature profile. As a result, the temperature profile generated by the data-driven model has a root mean square error of 1.22 °C compared to the numerical model output. This error was shown to be larger for lower recovery efficiency values compared to higher values. The machine learning algorithm used to predict the recovery efficiency has a root mean square error of 1.45 percentage points. The data-driven model has a computation time of less than half a second, which is more than 180,000 times faster than the numerical model that was used to generate the data. This model is, therefore, suitable for integration in larger energy system models.

Abstract High-Temperature Aquifer Thermal Energy Storage (HT-ATES) can be used to reduce greenhouse gas emissions from heating. The thermal recovery efficiency is the main parameter indicating the performance of an HT-ATES system and it is influenced by multiple aquifer properties and storage characteristics. This study presents a method for estimating recovery efficiency through numerical modeling, data analysis, and curve fitting. This method shows the relation between the recovery efficiency and various storage conditions, such as aquifer properties and storage temperature. In addition, this research explores an analytical relationship between energetic efficiency and recovery efficiency and verifies that relationship with the generated data. The proposed method can be used for the purpose of initial screening to estimate the performance of an HT-ATES system and for efficiently using HT-ATES as a component in larger energy system models. This method uses the modified Rayleigh number in combination with aquifer thickness and injected volume and has a R^2 of 85%. The analytical relation between energetic efficiency and recovery efficiency was shown to be accurate for all calculated energetic efficiency values above 60% and is less accurate with lower calculated energetic efficiency values.

Background. Several HCI researchers have started to use game design elements in their research to create playful methods for involving end-users in design. Similar to serious games, such research games serve a dual purpose: 1) to create an enjoyable experience for research participants, and 2) to collect user insights to inform the design process. Aim. We propose that the Serious Game Design Assessment (SGDA) Framework, that evaluates both a game’s enjoyable purposes and its serious purposes, may be a valuable tool in assessing and developing research games. In this article, we apply the SGDA Framework to three existing research games, one involving train passengers in assessing potential future user experiences, one involving hospital staff in ideation for wearable technology, and one involving TV viewers in ideation for future video watching scenarios. Conclusion. The assessment of the three research games suggests that the framework is indeed applicable to research games and may, as such, provide HCI researchers with clear guidance when creating new research games.

The past decade has shown that new technologies can have a profound impact on how we consume television and online video content. As technologies such as VR/AR, sensors, and smart voice assistants are maturing, it is becoming pertinent to study how they could influence the next generation of TV and video experiences. While some experiments already incorporate one or more of these technologies, a systematic study into user expectations for these new technologies has not yet been conducted. In this paper, we present the results of a co-creation session resulting in two future video watching scenarios visualized using storyboards: one presenting a hyper-personalized experience based on the automatic recognition of emotions, and another one presenting an immersive experience using Virtual and Augmented Reality. We conclude with user evaluations of both concepts, offering insights in the opportunities and challenges these concepts could bring for the future of television and video experiences.