Cost Analysis for Large Thermal Energy Storage Systems

Richard Lu chinger 1 Competence Centre Thermal Energy Storage, Lucerne University of Applied Sciences and Arts, Technikumstrasse 21, 6048 Horw, Lucerne, Switzerland; Institute for Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/302, A-1060 Vienna, Austria e-mail: [email protected] Reto Hendry Competence Centre Thermal Energy Storage, Lucerne University of Applied Sciences and Arts, Technikumstrasse 21, 6048 Horw, Lucerne, Switzerland e-mail: [email protected] Heimo Walter Institute for Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/302, A-1060 Vienna, Austria e-mail: [email protected] Jo rg Worlitschek Competence Centre Thermal Energy Storage, Lucerne University of Applied Sciences and Arts, Technikumstrasse 21, 6048 Horw, Lucerne, Switzerland e-mail: [email protected] Philipp Schuetz Competence Centre Thermal Energy Storage, Lucerne University of Applied Sciences and Arts, Technikumstrasse 21, 6048 Horw, Lucerne, Switzerland e-mail: [email protected] Cost Analysis for Large Thermal Energy Storage Systems Thermal energy storage (TES) technologies play a key role in decarbonizing heat supply and integrating renewable energy sources into heating systems. This study examines the investment costs of over 50 large-scale TES systems, including aquifer thermal energy storage (ATES), borehole thermal energy storage (BTES), pit thermal energy storage (PTES), and tank thermal energy storage (TTES) systems, based on desk and literature research. The analysis considers inflation and purchasing power parity to enable cost comparisons between regions and time periods and develops cost functions to enable investment appraisal. The results highlight significant differences in the cost structures and the scalability of TES technologies. PTES systems benefit from economies of scale, making them cost-effective for large storage volumes. Due to higher specific costs, TTES systems are more suitable for smaller applications. ATES and BTES systems show moderate economies of scale. However, they remain highly dependent on geological conditions. Furthermore, the study introduces a new framework for calculating the levelized cost of storage, which enables the comparison of different technologies by considering the cost of providing energy at the target temperature level. This framework provides a more accurate assessment of the relative competitiveness of TES. By providing a transparent basis for evaluating TES cost, the study’sfindings reduce uncertainty for decision-makers. Furthermore, the study recommends additional research to validate cost models and assess the practical application of TES. [DOI: 10.1115/1.4069122] Keywords: thermal energy storage, levelized cost of storage, cost modeling, borehole thermal energy storage, aquifer thermal energy storage, pit thermal energy storage, tank thermal energy storage, seasonal thermal energy storage, carbon-neutral, clean energy, energy, sustainability, thermal comfort Introduction In 2023, global primary energy consumption reached a new record, with fossil fuels accounting for over 80% [1]. Of this total, approximately 50% was allocated to heating [2], with the residential sector playing a central role. In Europe and America, buildings are responsible for about 40% of energy use and contribute to over one-third of CO 2 emissions [3]. Decarbonizing the heat supply in buildings is of crucial importance for a sustainable energy transition. This process requires rapid deployment of low-carbon technologies [4–7]. Thermal energy storage (TES) is a promising solution for reducing our dependence on fossil fuels by improving the energy efficiency of buildings and overcoming the challenges associated with the intermittent availability of renewable energy or the use of waste heat [8–11]. However, high investment costs, long payback periods, and competition from alternative storage technologies are hindering the widespread adoption of TES [12–15]. In addition to these hurdles, uncertainties about performance and long-term profitability, especially in early project phases, also lead to reluctance. Especially in these phases, reliable data and proven methodologies are often lacking, making it difficult to make informed decisions [7,16–18]. Against this background, reliable and systematically collected cost data for TES systems are essential in establishing a solid foundation for their development and dissemination. The study focuses on large TES systems designed to supply multiple buildings or thermal grids in the residential sector, providing storage capacities for both short-term and longterm heat storage. The scope of the considered technologies is limited to four sensitive technological approaches: tank, pit, 1 Corresponding author. Manuscript received April 9, 2025; final manuscript received July 2, 2025; published online August 4, 2025. Assoc. Editor: Xin Jin. ASME Journal of Engineering for Sustainable Buildings and Cities MAY 2025, Vol. 6 / 021006-1 Copyright © 2025 by ASME; reuse license CC-BY 4.0 borehole, and aquifer thermal storage. These were chosen because they are technologically mature and suitable for large-scale thermal storage. By concentrating on these systems, the analysis provides a comprehensive and focused assessment of cost and performance data relevant to real-world deployment. In the literature, fundamental studies examine the investment and lifecycle costs of various TES technologies. Notable examples include the work of Schmidt et al., which analyzed approximately 20 TES projects and demonstrated that specific investment costs tend to decrease with the increasing storage capacity [19]. Another important source is the techno-economic review by Yang et al., which systematically lists investment costs for around 30 large-scale thermal storage systems [14]. Additionally, several studies provide detailed cost analyses for specific technology groups, such as the review on aquifer TES by Fleuchaus et al. [20]. Lifecycle costs of TES are also addressed in studies by Odukomaiya et al. and Dahash et al., with application-specific considerations [21,22]. Despite the valuable contributions, the existing studies have three significant limitations: First, they do not consider the cost differences in terms of purchasing power, labor, materials, and regulations, which can have a substantial impact on investment costs. Second, most do not consider the effects of inflation, which further limits the comparability and relevance of cost data. An exception is the work presented in Ref. [19], which adjusted historical project costs to 2017 price levels. Furthermore, the literature lacks a comprehensive definition of the levelized cost of storage (LCOS) that systematically accounts for temperature-dependent differences within TES systems and, consequently, the quality of stored energy. Thus, this study aims to fill the identified gaps. First, it examines existing concepts of levelized costs for TES and develops a new TES-specificdefinition that systematically accounts for temperature-dependent differences and the quality of stored energy. Second, a systematic analysis of cost data for large TES systems will be conducted, considering country-specific purchasing power and inflation-adjusted values. This will be used to develop cost functions for estimating TES investment costs. This study provides decision-makers with information on TES costs, economies of scale, and country-specific differences on a transparent and comparable basis. It also presents a framework for calculating LCOS, allowing comparisons with other types of technology. Ultimately, this will reduce uncertainty and facilitate the planning and deployment of TES systems. Materials and Methods Thermal Energy Storage Technologies. Research on TES technologies has been ongoing since the 1970s [23]. TES stores thermal energy—heat or cold—in a medium for later use, bridging supply and consumption [24]. TES can be used in residential, commercial, and industrial buildings [15] and contribute to sustainable and efficient energy systems [25]. They can be categorized as either shortor long-term storage: −Short-term TES stores thermal energy for a few minutes up to a few days, allowing load shifting to periods when there is a surplus of renewable energy or when economic efficiency is improved. This improves flexibility, reliability, system efficiency, economics, and sector coupling, and increases energy independence [9–11,26]. They also support the integration of renewable energy by efficiently storing excess energy and making it available when needed [25,27]. −Long-term TES, also called seasonal TES (STES), stores thermal energy for several weeks or months, often from summer to winter. Their primary purpose is the storage of excess energy during periods of high supply, so that it is available when demand is high [28]. STES are mainly used in regions where there is a high demand for heating, such as central and northern Europe [29]. They are often combined with solar systems, but are also suitable for storing heat from biomass and waste heat [25]. Unlike shortterm storage systems, STES have few cycles per year and usually require larger storage volumes [28]. The TES can be implemented using three storage principles. Sensible heat storage systems use temperature changes in a medium such as water or the ground for thermal energy storage and are the most widespread TES group due to their cost efficiency and high degree of maturity [25]. Latent heat storage systems use phase change materials that store energy during changes in aggregate state without significantly changing the temperature, thus offering a higher energy density than sensible storage systems [30]. Thermochemical storage systems based on reversible chemical reactions or sorption processes enable high energy densities and are still in development [31]. For large-scale TES applications at the district or heating grid level, sensible TES is currently the best-suited option due to its high technical maturity and economic efficiency. This technology dominates the implementation of real projects and is therefore the focus of this analysis [14]. Four sensible TES technologies are considered below: aquifer, borehole, pit, and tank TES. Figure 1illustrates the four technologies schematically. Fig. 1 Schematic representation of aquifer, borehole, pit, and tank thermal energy storage (own illustration based on Ref. [32]) 021006-2 / Vol. 6, MAY 2025 Transactions of the ASME Aquifer Thermal Energy Storage. Aquifer thermal energy storage (ATES) storage systems are open-loop geothermal energy storage systems that store thermal energy in the form of heat at different temperatures in groundwater [33]. They use at least two wells to create separate warm and cold storage volumes from which energy is withdrawn as needed [20]. ATES systems typically operate either as low-temperature (LT) systems, with temperatures below 313 K (40 °C), or as high-temperature (HT) systems, with temperatures above 313 K [20]. Efficiency is dependent on factors such as the location of the well, the permeability of the aquifer, heat losses, and storage temperatures, while environmental issues such as potential changes in water quality require careful planning [34]. With more than 2800 systems installed worldwide, including approximately 2500 in the Netherlands, ATES systems are already widely used [20]. Although ATES systems are in frequent use in certain regions, little cost data are available [35]. However, the literature emphasizes that the depth of the wells particularly influences the investment costs of ATES systems. Vrijlandt et al. describe the costs for well drilling using the formula: CAPEX =100,000 +1000d+0.3d 2 , where drepresents depth in meters [36]. They also state that the specific drilling costs are around 875 €/m. Schüppler et al. estimate the particular investment costs to be between 300 €/kW and 1800 €/ kW, with smaller systems tending to have higher specific costs [37]. However, according to Vrijlandt et al., no clear correlation could be established between costs and output [36]. The assessment of operating costs varies in the literature. Todorov et al. assume annual fixed costs of 1% of the total investment costs [38]. In contrast, Schüppler et al. state the operating costs at 4% of the investment costs [37]. This discrepancy can be attributed to differences in the electricity prices used to cover operating costs, which comprise a significant portion of the variable costs of an ATES system. Payback times vary depending on the project conditions. According to Schüppler et al., Stemmle et al., and Fleuchaus et al., payback times range from 2–10 years and can be a maximum of 16 years in individual cases [20,33,37]. This illustrates the dependence of economic viability on the respective framework conditions. Borehole Thermal Energy Storage. Borehole thermal energy storage (BTES) uses the underground soil or rock to store thermal energy [39]. Heat is transferred through vertical or horizontal pipes, known as borehole heat exchangers. These are installed up to 250 m underground [40]. Like ATES, BTES systems can be classified into LT systems, with a temperature range of 278–313 K, and HT systems, ranging from 313–343 K [41–43]. Due to their lower volumetric energy density, they require a volume three to five times larger than hot water storage systems. However, this volume remains invisible [30]. BTES systems are highly dependent on underground conditions. Careful design is required to minimize the risk of localized groundwater heating and contamination [44– 47]. BTES systems have been utilized in several projects; however, no reliable data exist on their overall deployment. Although BTES systems are relatively common, cost data remain limited, as noted by Kallesøe et al. [35]. Skarphagen et al. [48] indicate that the cost of BTES systems varies by a factor of up to four, primarily due to geological conditions and drilling depth. Additionally, construction methods and site characteristics have a substantial impact on costs. While work on open land is often easier to implement, additional expenses may arise for site restoration. Integrating BTES systems into construction projects, especially those involving deep excavation pits, usually requires more complex and expensive development. Another key factor is drilling depth. As shown by Welsch et al., drilling costs increase with depth, from approximately 160 USD/m (150 €/m) at shallow depths to over 500 USD/m (480 €/m) at a depth of 1000 m [49]. A comparison of various studies highlights the wide range of specific cost estimates: Ruesch et al. estimate BTES costs at around 120 USD/m (105 CHF/m), whereas Fiorentini et al. [50] suggest 70 USD/m (66 €/m). Köppe and Lizana et al. report average drilling costs between approximately 55 and 90 USD/m (50–80 €/m) [48,51–54]. Pit Thermal Energy Storage. One of the most promising and cost-effective technologies for large-scale TES is pit thermal energy storage (PTES) [55,56]. PTES systems typically consist of pits filled with water and lined with a waterproof membrane with afloating, insulated cover [57,58]. In some cases, a mixture of water and gravel is used as the storage medium [40]. PTES features low heat loss, high energy density, and low specific cost at temperatures ranging from 283 to 368 K. PTES systems are ideal for large installations in rural or suburban areas due to their large footprint [55]. However, careful site selection is essential to minimize environmental impacts because of risks, particularly related to groundwater heating [28,59,60]. To date, 13 PTES systems constructed between 1987 and 2023 have been documented by Sifnaios et al. [61], although it remains unclear whether this list is exhaustive. The cost of PTES depends heavily on the size of the storage, the materials used, and the technical requirements. Studies show that economies of scale are critical to PTES cost-effectiveness [61–63]: Costs for small storage systems exceed 100 USD/m 3 , while costs for 60,000 m 3 systems drop to around 50 USD/m 3 . Specific costs of around 20 USD/m 3 or less can be achieved for large systems of several hundred thousand cubic meters. A closer look at the cost structure reveals that 40% of the PTES construction costs in Marstal, Denmark, were allocated to cover, and 20% to excavation, with smaller shares allocated to engineering and water treatment. Additionally, operating costs were reported at 30,000 USD per year, approximately 1% of the investment costs [63]. Wetzel et al. [49] reported a 20% increase in construction costs for the PTES in Høje Taastrup, Denmark. This was primarily due to more expensive liner and cover solutions, while design and ancillary costs remained low. Simultaneously, driven by technological advances, Sifnaios et al. [61] predict a further 20% reduction in costs by 2050. Tank Thermal Energy Storage. TTES is a mature technology for sensible thermal energy storage, from small buildings to large district heating systems [14]. TTES systems are insulated, cylindrical in shape, and are constructed of stainless steel, reinforced concrete, or fiber-reinforced polymer [64]. They can be installed as freestanding, partially buried, or fully buried units in unpressurized (atmospheric) or pressurized tanks [30,65,66]. 2 Atmospheric tanks are suitable for long-term storage at temperatures up to 371 K, while pressurized tanks allow for higher temperatures [65]. The atmospheric two-zone TTES, divided into two zones by an insulated floor, is a special subgroup. This design allows temperatures up to 403 K in the lower zone to accommodate volume changes and pressure fluctuations. Consequently, it achieves higher energy storage density than single-zone tanks [67]. TTES systems are used in multiple applications, yet there are no solid data on the number of installations. Studies show that there are significant cost differences between TTES technologies, confirming strong economies of scale, where the specific costs decrease as the size of the storage increases. Sifnaios et al. [61] estimate specific costs of 300–600 USD/m 3 for storage systems with a capacity of 1000 m 3 , while larger systems of more than 7500 m 3 fall below 200 USD/m 3 . Models developed by Narula et al. [68] and Li et al. [69] describe these trends mathematically using power law equations. They also show lower costs for larger volumes. Construction methods also have a substantial impact on costs. The author team from the SPF Institute estimates specific costs of 220–400 USD/m 3 for atmospheric tanks and 500–2000 USD/m 3 for pressurized tanks [70]. Christos [71] estimates the cost of a two-zone tank to be between 350 and 440 USD/m 3 . An even wider range of specific expenses, from 100 to 5400 USD/m 3 ,reflecting differences in design and requirements, is reported by Yang et al. [14]. Site-specific differences are highlighted in a comparison of two atmospheric storage systems in Mannheim and Flensburg by Christos [71]. In 2 https://www.ecovat.eu/. ASME Journal of Engineering for Sustainable Buildings and Cities MAY 2025, Vol. 6 / 021006-3 Mannheim, 26% of the cost was allocated to the tank, including the foundation, internal structures, and insulation. In Flensburg, this proportion was 56%, indicating different structural requirements. Summary of Thermal Energy Storage Cost Characteristics. Comparing the four technologies highlights the differing costs and economies of scale. As capacity increases, the specific investment cost of TES decreases, as shown in Refs. [19,41,54]. However, different technologies have different cost structures and scaling effects. TTES has the highest specific cost and is primarily used for comparable smaller volumes. PTES offers a very high storage capacity and significant economies of scale, making it particularly economical for large systems. BTES falls within the mid-cost range, with limited economies of scale and is typically used for medium to large projects. Even for small to medium capacities, ATES achieves low specific costs. Like BTES, ATES shows limited scaling effects. Due to limited data availability, the costeffectiveness assessment of ATES and BTES remains uncertain. However, the specific costs of TTES and PTES tend to be higher than those of BTES and ATES, as summarized by Yang et al. [14]. Investigation of the Levelized Cost of Storage for Thermal Energy Storage. A widely used and well-established metric for evaluating the costs of an energy system is the levelized cost of energy (LCOE). This method enables a direct comparison of the economic performance of provision technologies with different generation and cost structures. Kost et al. define the LCOE using Eq. (1) [72]. LCOE = I0+n t=1 At (1 +i)t n t=1 Mt,el (1 +i)t (1) where LCOE is the levelized cost of energy (USD/kWh); I₀is the investment expenditures (USD); Aₜis the annual total costs in year t(USD) (Aₜ=fixed operating costs +variable operating costs +residual value/disposal of the plant); Mₜ,ₑₗ is the produced quantity of electricity in the respective year (kWh); iis the real interest rate (%); nis the economic operational lifetime (years); and tis the year of lifetime (1, 2, …n). The levelized cost of heat (LCOH) is based on the same fundamental concept as the LCOE but is specifically tailored to assess the cost of heat supply. The literature provides definitions of LCOH with varying levels of detail. For instance, Mugnier et al. incorporate additional economic factors, such as subsidies, tax incentives, corporate tax rates, and depreciation, into their definition. However, as Huang et al. demonstrates, the detailed can also be simplified (2) [73]. 3 LCOH = I0−S0+n t=1 OPEXt (1 +i)t − RV (1 +r)n n t=1 Et (1 +i)t (2) where LCOH is the levelized cost of heat (USD/kWh); S₀is subsidies and incentives (USD); OPEXₜis the operating costs and variable operating costs in year t(USD), RV is the residual value of system (USD); and Eₜis the total energy demand in year t(kWh). The LCOH is frequently used in both literature and the industry to assess the economic performance of a system with TES. It enables the analysis of a TES’s impact on the overall system, for example, by comparing the LCOH of a system with and without TES or between different TES configurations or technologies. The LCOH provides valuable insights for system-wide decisions and is therefore often used as a key performance indicator for decision-makers when selecting a TES technology [74]. LCOH is used by Yang et al. [14] for the economic evaluation of seasonal and long-term TES. Notably, their approach subdivides the LCOH based on system boundaries. This allows for the separate analysis of the costs associated with individual system components, such as thermal storage, renewable energy, or backup heating [14]. However, the definition given by Ref. [14] has two major shortcomings: (1) The potential residual value of the TES at the end of its lifespan is not considered. However, this can play a significant role in economic evaluation, particularly for long-lasting and cost-intensive TES technologies. (2) For TES, where the storage temperature is below the required service temperature, it is necessary to raise it to the network temperature, often using a heat pump. It remains unclear how the additional costs for this process are accounted for in the LCOH. de Simón-Martín et al. [75] analyzed the LCOH for the evaluation of thermal energy systems and extend the methodology to systems with multiple forms of energy. The study underscores the significance of the temperature at which thermal energy is delivered and advocates for the utilization of the levelized cost of exergy (LCOEx) for applications with intricate temperature requirements, thereby facilitating enhanced consideration of energy quality. A more precise assessment for specific applications is provided by LCOEx, which is calculated by dividing the total discounted cost of an energy system by the discounted exergy over its lifetime. However, they identify several limitations of LCOEx, such as the complexity of cost allocation for concurrent energy production and the nonlinear effects of control strategies. A parameter used to express specific storage costs is the LCOS. LCOS is widely used to evaluate electrochemical batteries such as lithium-ion batteries [21]. Transferring and applying the LCOS formula developed for electric batteries to TES is not a trivial task. In TES systems, the exergy fraction can vary significantly throughout the storage process due to temperature changes, for example, when storage temperatures are higher or lower than the target temperature. Existing approaches to defining LCOS are often limited to specific technologies or simplified assumptions. This limits the comparability and relevance of the definitions. For example, Odukomaiya et al. [21] examine TES systems that use electrical energy as input and thermal energy as output. The definitions by de Simón-Martín et al., Jülch, Belderbos et al., and Luerssen et al. [75–78] primarily focus on electrical systems, while Luerssen et al. define LCOS for a specific application but do not establish a generalized definition. Dahash et al. [22] provide a definition that does not account for potential discharge costs or residual value. The need for a comprehensive definition of LCOS for TES can be illustrated by the following two examples: A TES system that relies on a temperature boost, such as a BTES or ATES with a maximum storage temperature significantly below the required utilization temperature, requires a temperature increase. In practice, this is often achieved using a heat pump [41,79]. In such systems, the temperature boost is essential, which is why we argue that the associated costs should be attributed to heat storage. Another, but related, aspect concerns the costs associated with discharging a storage system. Storage systems with an upper storage temperature above the utilization temperature can be further discharged below the network temperature using a heat pump, thereby increasing the total storage capacity at the same volume. An example of this boosting approach is the PTES in Toftlund, Denmark, which operates within a temperature range of 298–363 K [62]. These discharge costs should also be incorporated into the definition of LCOS to enable a realistic evaluation. According to the current state of the literature, there is no definition of LCOS for TES that considers all critical aspects, especially temperature dependence during storage and all relevant costs. Nevertheless, the LCOS of TES is vital for assessing economic feasibility and defining R&D targets that will make TES scalable and 3 http://task54.iea-shc.org/. 021006-4 / Vol. 6, MAY 2025 Transactions of the ASME competitive [21,80]. This discrepancy underscores the need for further development of thermal storage methodologies to reflect their specific characteristics and requirements accurately. Rethinking Levelized Cost of Storage for Thermal Energy Storage. In this section, a comprehensive and flexible definition of LCOS is developed, tailored to TES. As described in the previous section, existing concepts such as LCOE and LCOH, along with specificdefinitions from LCOS, can serve as a basis. However, to consider the specificities of TES and to cover all relevant aspects, these approaches need to be specifically adapted or extended. A key factor is the choice of system boundaries. This ensures that all appropriate costs are captured over the life of the system, including not only investment and operating costs, but also residual values and costs associated with charging and discharging processes. The latter is especially important when a temperature boost is required to reach the target temperature. The LCOS definition should also enable comparisons between different TES technologies, such as ATES, BTES, PTES, and TTES, while accounting for the temperature dependence of the discharged energy. For TES systems using heat pumps to adjust temperatures, these additional costs must be included to allow for a realistic assessment. TES for heating networks and buildings can be generally divided into two categories based on their storage temperature: high-temperature TES (HT-TES) and low-temperature TES (LT-TES). The key distinction lies in whether the maximum storage temperature (T max ) is above or below the required utilization temperature (T sp ). HT-TES typically includes PTES and TTES, but HT-BTES and HT-ATES can also meet these requirements. HT-TES has a sufficiently high T max , allowing part of the stored energy (Q 2 ) to be directly available and delivered to a consumer (see Fig. 2: HT-TES without discharge). If an HT-TES is further discharged, the available thermal energy (Q 2 ) must be raised to T sp (HT-TES with discharge). The resulting temperature-adjusted energy (Q 3 ) comprises Q 2 and the energy supplied for the temperature boost (E TL ), which is often provided by a heat pump. In contrast, LT-TES, which lacks Q 1 , always requires a temperature boost to reach the necessary utilization temperature (LT-TES with discharge). This classification highlights the differing requirements for temperature adjustment and the additional energy input that must be considered, particularly for LT-TES. Figure 2illustrates the distribution of the thermal capacity of a TES concerning the availability of stored energy. On the left, the total thermal capacity of the TES is divided into two portions: one above and one below the utilization temperature. On the right side, energy flows are categorized into directly and indirectly available energy. In addition, the energy flow E TL is shown, which crosses the system boundary (bottom middle), e.g., electricity for a heat pump from the grid or a local source. The diagram accounts for scenarios involving HT-TES, both with and without discharge, as well as LT-TES. It highlights the differences in energy availability and the need for temperature adjustments, particularly for LT-TES, which always require a temperature boost to reach the required utilization temperature. This concept of thermal energy fractions in HT-TES and LT-TES can also be applied to the system boundaries of the LCOS (see Fig. 3). Following the LCOH representation for TES by Yang et al., we adapt and extend it for the LCOS by incorporating a potential energy flow for discharging or temperature adjustment (E TL ). Here, E TL is assumed to represent the energy flow from a renewably operated heat pump. For simplicity, we combine the HT-TES and LT-TES groups and aggregate the flows Q 1 and Q 3 into a total discharge flow, Q dis (including E TL ). As shown, costs related to heat generation and other cost-intensive components are excluded from the LCOS and instead allocated to the LCOH. Building on this foundation, it can be concluded that the definition of LCOS must account for the total potential thermal energy provided by the TES (Q dis =E TL +Q 2 +Q 1 ). Additionally, the costs associated with discharging must be included. This is achieved by multiplying a cost factor (C TL ) with the energy required for temperature adjustment (E TL ). If C TL includes all costs associated with the temperature lift, regardless of the specific technology used for this purpose, taking these aspects into account, the equation for LCOH can be used as a starting point and adapted accordingly, resulting in the following equation for LCOS (3). LCOS = I0−S0+n t=1 At (1 +i)t − RV (1 +i)n n t=1 Qdis,t (1 +i)t (3) with Qdis =Q1,t+Q3,t=Q1,t+Q2,t+ETL,t(4) At=OPEXt+CAPEXre,t+CTL,t×ETL,t(5) where LCOS is the levelized cost of storage (USD/kWh), CAPEX re is the reinvestments in TES (USD), OPEX is the operating and maintenance costs (USD), C TL is the total costs for temperature lift (USD/ kWh), and E TL is the energy for temperature adjustment (kWh). Methodology for Cost Data Adjustment and Modeling. The methodology for adjusting and modeling cost data for various TES technologies follows four sequential steps (see Fig. 4). The approach corrects historical investment costs to 2023 levels and adjusts the purchasing power of the four countries: the United States, the United Kingdom, Switzerland, and Germany. This serves as the basis for developing cost functions for ATES, BTES, PTES, and TTES. These functions will provide a basis for economic evaluation. Fig. 2 Structure of thermal energy availability in TES considering temperature lift Fig. 3 LCOS system boundary and relevant energy flows for TES ASME Journal of Engineering for Sustainable Buildings and Cities MAY 2025, Vol. 6 / 021006-5 The first step is to gather data through literature and desk research to build a broad and practical foundation. The literature search is based on targeted keywords, including investment costs, capital costs, cost data, capital expenditure (CAPEX), initial costs, construction costs, installation costs, and infrastructure costs, in both English and German. Searches were conducted using the Web of Science and Google Scholar platforms to search major scientific databases systematically. These included Elsevier, SpringerLink, and IEEE Xplore. Gray literature sources, including project reports, technical documentation, and conference papers, supplemented this. As part of the desk research, operators and planners of TES systems were also contacted to provide practical information to complement the existing literature data. Technical project data, including storage volume, energy capacity, the number of wells, and well depths, was collected in addition to the investment costs. The approach was deliberately flexible, without following a strictly systematic literature analysis. The following two steps involve adjusting TES cost data through inflation adjustment and purchasing power parity (PPP) correction. Adjusting for inflation updates historical cost data to current price levels, thereby reducing the distortions caused by inflation. A PPP adjustment is then applied to account for differences in purchasing power between the four countries analyzed and to make the data internationally comparable. The selection of these countries was pragmatic and does not claim to provide global coverage. The inflation adjustment is made by multiplying the historical cost by the cumulative inflation factor (CIF). The CIF captures the cumulative effect of inflation over several consecutive years [81]. It is calculated as the product of annual inflation factors. Each factor is derived from the respective inflation rate using Eq. (6). The annual inflation rates are sourced from the World Bank Group and are applied in this study [82]. CIF = n i=1 (1 +Anuual inflation ratei)(6) where CIF is the cumulative inflation factor. The purchasing power adjustment is performed using the PPP as a benchmark to measure price level differences between countries. This approach converts currencies so that they represent the same purchasing power in a common base currency, such as the international dollar [100]. This method allows costs from one country to be converted into purchasing power-adjusted costs of another country by accounting for the respective PPP values. The conversion is carried out using Eq. (7). It=Is×PPPc PPPs (7) where I t is the purchasing power-adjusted investment in the target country, I s is the initial investment in the source country, PPP c is the PPP value of the target country, and PPP s is the PPP value of the source country. This study uses PPP values provided by the World Bank Group [83] listed in Appendix E. However, PPP is subject to certain uncertainties, as it is based on average price levels and does not account for industry-specific or regional differences. Additionally, incomplete or estimated data, particularly in certain countries, may affect the accuracy of the values. Cost modeling to identify cost functions for the four technologies is the next step in the approach. The cost modeling analyzes the relationship between cost data and influencing factors using scatter plots and a log transformation. A power function is derived by linear regression of the logarithmized data to describe the cost dependence, and its goodness of fit is evaluated by the coefficient of determination (R 2 ) on the logarithmized data. Finally, the results are visually presented to illustrate the derived cost functions. Capital Cost of Large Thermal Energy Storage This section presents the cost data collected through desk research for implemented ATES, BTES, PTES, and TTES projects, which are documented in Appendices A–D. The data include cost information in the respective original currency, cumulative inflation factors, inflation-adjusted investment costs in the original currency, as well as purchasing power-adjusted investment costs for the United States, the United Kingdom, Switzerland, and Germany in the respective local currencies. Additionally, the data contain technical specifications (e.g., volume, storage capacity, or borehole depth) that are used to calculate specific indicators. For simplicity and clarity, the graphs presented below are uniformly based on purchasing power-adjusted USD. The data include a total of 51 projects implemented between 1991 and 2024. Most projects are in Germany (19 projects) and Denmark (10 projects). Additional projects were analyzed in Sweden (5 projects), Switzerland (4 projects), the Netherlands (3 projects), as well as in Belgium, Canada, and the United Kingdom, with two projects each. Individual projects are also documented in Austria, Norway, and the United States. This list does not claim to be exhaustive but is based on a sample compiled through desk research and available sources, see Fig. 5. Figure 5illustrates the investment costs (in USD millions), adjusted for purchasing power, as a function of the year in which the projects were commissioned. For each technology group, the median, mean, and standard deviation (Std Dev) are provided to illustrate the distribution and variability of costs. While ATES and BTES exhibit relatively low and stable overall investment costs, PTES shows a wider range. TTES stands out due to its higher investment costs and greater variability. Based on the collected cost data, approximation functions can be derived for the different technologies to describe investment costs as a function of relevant parameters such as storage volume or drilling depth. The following section examines these cost functions and interprets the results about economies of scale and technological differences. ATES Investment Costs. As explained earlier, the investment costs of ATES systems are mainly determined by the depth of the boreholes and the system size. The analysis of the investment cost per meter drilled for ATES systems shows a moderate correlation between borehole length and specific cost (R 2 =0.53). Fig. 4 Methodological steps (rectangles) for adjusting and modeling the TES cost data, the resulting intermediate results (rounded squares), and the information flow (arrows) 021006-6 / Vol. 6, MAY 2025 Transactions of the ASME This indicates slight economies of scale, see Fig. 6. Costs vary considerably, ranging from about 650 USD/m to 38,200 USD/m, with a median of about 37,500 USD/m. The median is 5850 USD/m, while the quartiles show a considerable dispersion, ranging from 1900 USD/m to 11,000 USD/m. A few cost outliers drive the distribution up, while most projects are concentrated in the mid-range. Overall, the results indicate strong heterogeneity, which can be attributed to technical differences, site-specific factors, or geological conditions. Figure 6shows the specific investment costs (USD/m) as a function of the total well length for ATES systems. The figure illustrates three regression models: a linear regression, a log-log regression, and a regression based on a logarithmic transformation of the y-axis. Of the three, the logarithmic transformation of the y-axis provides the best fit, capturing the economies of scale and the exponential relationship between well length and specificcostmoreaccurately than the other two models. It shows the best fit, as indicated by the highest R 2 compared to the log-log model and the linear model. A notable outlier appears to stabilize the fit, as its removal decreases R 2 , suggesting that it reinforces the observed trend. The results show a moderate correlation and suggest economies of scale where specific costs decrease with the increasing borehole length. BTES Investment Costs. The assumption that the BTES costs scale with borehole length is supported by the data collected on BTES systems as well as the findings from the literature discussed earlier. Analyzing the specific investment costs for BTES systems reveals a clear dependence on the total borehole length, as shown in Fig. 7. The regression analysis (R 2 =0.73) confirms a strong correlation, showing that costs decrease as borehole length increases. Specific costs range between 9 and 256 USD/m, with a median of 143 USD/m and a spread of 247 USD/m. A moderate dispersion (standard deviation =76 USD/m) and an interquartile range of 97 USD/m indicate differences between projects, influenced by technical designs, site conditions, geological factors, and economies Fig. 7 Cost function of BTES borehole (BHE) length with and without outlier (dashed line) Fig. 5 Overview of investment costs for TES technologies over time, showing the median (dashed), mean (dash-dot), and the standard deviation (shaded area) Fig. 6 ATES cost data versus well length and modeled cost function of ATES borehole length ASME Journal of Engineering for Sustainable Buildings and Cities MAY 2025, Vol. 6 / 021006-7 of scale. Although a distinction was made between HT-BTES and LT-BTES systems, the data show a similar cost trend, allowing this separation to be disregarded in the analysis. Figure 7shows the specific investment costs (USD/m) as a function of the total borehole length for BTES systems. The regression curves are based on power functions and describe exponential relationships between length and specific costs. The solid black line represents the regression for the complete data set. It shows a strong correlation (R 2 =0.73) and suggests the presence of economies of scale. Specific costs decrease with the increasing borehole length. To examine the influence of outliers, the figure also displays the gray dashed curve, which excludes BTES with a total borehole length exceeding 100,000 m. This second regression reveals a weaker correlation (R 2 =0.43), indicating greater cost variability for smaller installations. Neither function distinguishes between HT-BTES and LT-BTES systems, as both exhibit a similar cost trend. PTES Investment Costs. The data on investment costs for PTES systems show a clear correlation with storage volume. This suggests that the costs scale with volume. Volume-specific costs range from around 35–320 USD/m 3 , with a median of around 115 USD/m 3 . The cost range of USD 285/m 3 shows that the project costs vary widely. A standard deviation of 1025 USD/m 3 further emphasizes this variability, which can be attributed to factors such as storage size, technical design, material requirements, site conditions, and economies of scale. No significant variations in costs were observed between different PTES technologies (water based/water gravel); these differences are therefore disregarded in this analysis. Despite the fluctuations, the data indicate an overall trend of decreasing costs with the increasing storage volume, suggesting a moderate scale effect and aligning with the existing literature. Figure 8shows the specific investment costs (USD/m 3 ) as a function of the storage volume for PTES systems. The regression curve follows a power function that represents the nonlinear, decreasing relationship between volume and specific costs, showing a moderate correlation (R 2 =0.61). This indicates economies of scale for PTES. A combination of different engineering designs, topographical and geological conditions, material requirements, remediation, and economies of scale explains the large cost differences in the data. TTES Investment Costs. The investment cost data for TTES systems shows a dependence on storage volume, suggesting that costs are scaled by capital cost. Investment costs range from approximately 1–48 million USD, with a median of around 18 million USD. The cost range of $47 million and a standard deviation of $14 million illustrates the wide range of project costs due to differences in size, system design, material requirements, site conditions, and economies of scale. Additionally, slight cost differences are observed among the three TTES technologies (pressurized, atmospheric, and two-zone TTES), as shown separately in Fig. 9. Despite this variability, the data suggest a general cost trend with a slight economy-of-scale effect. These findings provide the foundation for further analyses on the scalability of TTES systems based on storage volume. Figure 9illustrates the capital costs as a function of storage volume for TTES systems. The regression curves follow power functions. They illustrate different scaling behaviors for the three TTES technologies: pressurized, atmospheric, and two-zone, as well as a combined function of the three. The combined regression curve (solid line) shows a strong correlation (R 2 =0.76) and describes a nonlinear scaling relationship, where costs increase with the volume following a power law trend. For pressurized TTES systems (dashed line), the regression function (R 2 =0.81) suggests an unexpected increase in costs with volume. However, due to the limited data available (three data points), no definitive trends can be established. In comparison, atmospheric TTES systems (dotted line, ten data points) show a steeper slope (R 2 =0.66) and a more linear cost development, indicating less pronounced economies of scale. The two-zone TTES data (dashed dotted line) show a moderate correlation (R 2 =0.56) and a flatter trend. However, the limited data (four points) also limit the conclusions that can be drawn. Ignoring the differences between the technologies, the combined function shows a trend of increasing capital cost with the increasing storage volume, suggesting potential economies of scale for TTES systems. Synthesis of Regression Models. The collected cost data, together with the developed regression models, illustrate the complexity of the cost structures in large-scale TES systems and demonstrate significant variability in investment costs, which makes standardized cost estimation difficult. For ATES, the cost function (y=e 9.8 e −0.0025x ) shows a moderate correlation with R 2 =0.53, indicating that the model only explains the cost development to a limited extent and that a high proportion of the variability is due to other factors (e.g., hydrogeological factors and site-specific factors). The investment costs for BTES, on the other hand, are more dependent on the borehole length (y=34,500x −0.6 ,R 2 = 0.73), which indicates better scalability and a more stable cost Fig. 8 Cost function of PTES volume specific Fig. 9 TTES capital cost function based on volume 021006-8 / Vol. 6, MAY 2025 Transactions of the ASME structure. PTES and TTES show a more explicit volume dependency. The PTES cost function (y=8400x −0.4 ,R 2 =0.61) indicates moderate economies of scale. With TTES, the investment costs are higher; however, despite different system configurations, a common cost trend is observed (y=0.003x 0.8 ,R 2 =0.76). Overall, the cost functions indicate that, in addition to system size, technical and sitespecific factors have a significant impact on investment costs. The regression models developed, therefore, only provide an approximate cost estimate and should be supplemented with further projectspecific data. Conclusion Economic viability is a crucial factor in selecting large-scale TES systems. Uncertainty about costs hinders the dissemination of a new technology. This study addresses this problem with a comprehensive cost analysis and derives scaling relationships for the ATES, BTES, PTES, and TTES technologies. This is intended to support the decision-making process in selecting appropriate TES technologies. The study shows that economies of scale make PTES systems particularly cost-effective for large-area applications. In contrast, TTES systems appear to be more economical for smaller applications due to their higher specific costs. ATES and BTES systems show moderate economies of scale. Their costs seem to depend more on topographical and geological conditions. An overview and summary of the different cost functions and R 2 values are provided in Appendix F. Despite adjusting historical costs for inflation and purchasing power, uncertainties remain regarding operating costs and technical variations. Furthermore, the low R 2 values of the cost correlations illustrate the variability of the available data and the influence of site-specific factors. These uncertainties show that a cautious interpretation and further research appear necessary to refine the cost estimation models for large-scale TES. The cost functions presented, along with the LCOS framework, provide an improved basis for estimating the costs of TES technologies. Further research should validate these approaches in real projects and collect additional data to assess long-term economic and environmental impacts. Extending this body of research is crucial to further reduce uncertainty for decision-makers and enable more informed decisions. Acknowledgment This publication resulted from research within the research project “Sociotechnological Breakthrough of Thermal Energy Storage—a new Approach of Constructive Technology Assessment.” Funding Data •The Swiss National Science Foundation (Contract No. CRSII5_202239). Conflict of Interest There are no conflicts of interest. Data Availability Statement The authors attest that all data for this study are included in the paper. Nomenclature i=real interest rate K=Kelvin A=annual total costs D=depth E=total energy demand I=investment expenditures M=produced a quantity of electricity N=economic operational lifetime S=subsidies and incentives T=year of lifetime C TL =total costs for temperature lift E TL =input energy for temperature lift I s =initial investment source country I t =purchasing power-adjusted investment target country R 2 =coefficient of determination ATES =aquifer thermal energy storage BTES =borehole thermal energy storage CAPEX re =reinvestments cost CIF =cumulative inflation factor LCOE =levelized cost of energy LCOEx =levelized cost of exergy LCOH =levelized cost of heat LCOS =levelized cost of storage OPEX =operating and variable operating costs PPP =purchasing power parities PPP s =purchasing power parities source country PPP c =purchasing power parities target country PTES =pit thermal energy storage RV =residual value of system Std Dev =standard deviation TES =thermal energy storage TTES =tank thermal energy storage ASME Journal of Engineering for Sustainable Buildings and Cities MAY 2025, Vol. 6 / 021006-9