Search Results (197)

The development of coal resources has created a large number of underground mined-out spaces, which can be utilized for cross-seasonal thermal storage through underground reservoirs to achieve seasonal heat storage. However, there is currently limited research on the cross-seasonal thermal storage capabilities and thermal storage performance evaluation of coal mine underground reservoirs. This study aims to evaluate the operational stability and long-term performance of a Coal Mine Underground Reservoir Energy Storage System (CMUR-ESS) under realistic geological conditions of the Shendong Coalfield. A multi-physics coupling model, integrating thermal-fluid processes, was developed based on the actual structure of the No. 5-2 coal seam goaf in the Dalinta Mine. Numerical simulations were conducted over five annual cycles, each comprising injection, storage, production, and transition stages. Results demonstrate that the system achieves progressive thermal accumulation, with the volume fraction of water above 70 °C increasing from 75.0% in the first cycle to 88.9% by the fifth cycle at the end of the storage stage. Production temperatures also improved, with peak and final temperatures rising by 6.2% and 6.8%, respectively, after five cycles. The analysis confirms enhanced heat retention and reduced thermal loss over time, indicating robust long-term stability and sustainability of the CMUR-ESS for seasonal energy storage applications. The results of this study can provide a reference for the design and evaluation of CMUR-ESS. Full article

It is crucial to understand the market structure and the formation of the electricity mix in the context of the increasingly widespread and global introduction of renewable energy sources as primary energy sources. Due to the cyclical nature of energy production from RES, a long-term plan for seasonal storage is mandatory for smooth and effective energy transition. The stability of the energy system remains a key requirement, especially due to the dynamic changes in the generation, consumption, and pricing of energy resources. This article aims to present the concept that, in the absence of a properly structured and balanced market, thermal power plants prove to be flexible and reliable power sources that can be quickly integrated into the energy system at critical moments when maintaining the grid balance is difficult (such as during peak hours of solar generation). Full article

Residential space heating in Northern Europe requires long-duration thermal storage to align summer solar gains with winter heating demand. This study investigates a compact sand-based seasonal thermal energy storage integrated with flat-plate solar collectors for an A+ class single-family house in Kaunas, Lithuania. An iterative co-design couples collector sizing with the seasonal charging target and a 3D COMSOL Multiphysics model of a 300 m³ sand-filled, phenolic foam-insulated system, with a 1D conjugate model of a copper pipe heat-exchanger network. The system was charged from March to September and discharged from October to February under measured-weather boundary conditions across three consecutive annual cycles. During the first year, the storage supplied the entire winter heating demand, though 35.2% of the input energy was lost through conduction, resulting in an end-of-cycle average sand temperature slightly below the initial state. In subsequent years, both the peak sand temperature and the residual end-of-cycle temperature increased by 3.7 °C and 3.2 °C, respectively, by the third year, indicating cumulative thermal recovery and improved retention. Meanwhile, the peak conductive losses rate decreased by 98 W, and cumulative annual losses decreased by 781.4 kWh in the third year, with an average annual reduction of 4.15%. These results highlight the progressive self-conditioning of the surrounding soil and demonstrate that a low-cost, sand-based storage system can sustain a complete seasonal heating supply with declining losses, offering a robust and scalable approach for residential building heating applications. Full article

The waste heat generated by high-performance computing (HPC) represents an opportunity for advancing the decarbonization of energy systems. Seasonal storage is necessary to regulate the balance between waste heat production and demand. High-temperature aquifer thermal energy storage (HT-ATES) is a particularly well-suited technology for this purpose due to its large storage capacity. However, integrating HT-ATES into energy systems for district heating is complex, affecting existing components. Therefore, this study applies a bi-objective mixed-integer quadratically constrained programming (MIQCP) approach to optimize the energy system at Forschungszentrum Jülich (FZJ) regarding total annualized costs (TAC) and global warming impact (GWI). The exascale computer Jupiter, which is hosted at FZJ, generates a substantial amount of renewable waste heat that is suitable for integration into district heating networks and seasonal storage. Case studies show that HT-ATES integration into the investigated system can reduce GWI by 20% and increase TAC by 1% compared to the reference case. Despite increased TAC from investments and heat pump (HP) operation, summer charging of the HT-ATES remains flexible and cost-effective. An idealized future scenario indicates that HT-ATES with a storage capacity of 16,990 MWh and HPs could cover most of the heating demand, reducing GWI by up to 91% while TAC increases by 6% relative to the reference system. Full article

Distributed energy systems (DESs) are crucial for renewable deployment, but decentralised generation substantially increases flexibility requirements. Flexibility is framed as a system property that emerges from the coordinated operation of demand, storage and dispatchable generation across multi-energy carriers. Demand response schemes and demand-side management can provide flexibility, but their effective potential is constrained by user participation. Sector-coupling strategies and energy storage systems enable temporal and cross-sector decoupling between renewable generation and demand. Electrochemical batteries are technically mature and well suited for short-term balancing, but costs and environmental impacts are significant. Power-to-Heat with heat pumps and thermal energy storage is a cost-effective solution, especially when combined with low-temperature district heating. Electric vehicles, when operated under smart-charging and vehicle-to-grid schemes, can shift large charging demands feeding energy into the grid, facing battery degradation and infrastructure costs. Power-to-Gas and Power-to-X use hydrogen and electrofuels as long-term storage but are penalised by low round-trip efficiencies and significant capital costs if power-to-power with fuel cells is applied. On the supply side, micro-CHP can provide dispatchable capacity when fuelled by renewable fuels and combined with seasonal storage. Costs and efficiencies are strongly scale-dependent, and markets, regulation, digital infrastructure and social acceptance are key enablers of flexibility. Full article

The rapid growth and seasonal availability of agricultural materials, such as straws, stalks, husks, shells, and processing wastes, present both a disposal challenge and an opportunity for renewable fuel production. Solar-assisted thermochemical conversion, such as solar-driven pyrolysis, gasification, and hydrothermal routes, provides a pathway to produce bio-oils, syngas, and upgraded chars with substantially reduced fossil energy inputs compared to conventional thermal systems. Recent experimental research and plant-level techno-economic studies suggest that integrating concentrated solar thermal (CSP) collectors, falling particle receivers, or solar microwave hybrid heating with thermochemical reactors can reduce fossil auxiliary energy demand and enhance life-cycle greenhouse gas (GHG) performance. The primary challenges are operational intermittency and the capital costs of solar collectors. Alongside, machine learning (ML) and AI tools (surrogate models, Bayesian optimization, physics-informed neural networks) are accelerating feedstock screening, process control, and multi-objective optimization, significantly reducing experimental burden and improving the predictability of yields and emissions. This review presents recent experimental, modeling, and techno-economic literature to propose a unified classification of feedstocks, solar-integration modes, and AI roles. It reveals urgent research needs for standardized AI-ready datasets, long-term field demonstrations with thermal storage (e.g., integrating PCM), hybrid physics-ML models for interpretability, and region-specific TEA/LCA frameworks, which are most strongly recommended. Data’s reporting metrics and a reproducible dataset template are provided to accelerate translation from laboratory research to farm-level deployment. Full article

District heating networks (DHNs) are a key technology in the transition toward sustainable heat supply, increasingly integrating renewable sources and thermal energy storage. High-temperature aquifer thermal energy storage (HT-ATES) can enhance DHN efficiency by shifting heat production over time, potentially reducing both costs and greenhouse gas emissions. However, most life cycle assessments (LCAs) remain static, rely on average data, and neglect temporal dispatch dynamics and marginal substitution among heat sources for environmental evaluation. This study introduces a dynamic life cycle inventory framework that explicitly links HT-ATES-operation scheduling in DHNs with marginal life cycle data. The framework expands system boundaries to capture time-varying changes in heat composition, combines a district heating merit-order representation (distinguishing must-run and flexible capacities) with linear programming to determine least-cost dispatch, and translates marginally displaced technologies into environmental and economic consequences. Foreground inputs are derived from an existing third-generation DHN (heat demand, generation assets, efficiencies) and publicly available energy carrier cost data and are linked to consequential background inventory datasets (ecoinvent). The framework is demonstrated for one year of operation for an HT-ATES concept with 50 GWh of injected heat. Hourly resolved results identify the marginally displaced technologies and indicate annual reductions of 5.86 kt CO₂e alongside cost savings of EUR 1.09 M. A comparison of alternative operation schedules shows strong sensitivity of both economic and environmental performance to operational strategy. Overall, the proposed framework provides a replicable and adaptable basis for consequential assessment of HT-ATES operation in DHNs and supports strategic decision-making on seasonal thermal storage deployment in low-carbon heat systems. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Aquifer thermal energy storage (ATES) is a concept that can help to address heating and cooling needs through the use of the subsurface as a seasonal thermal energy storage (STES) system. Over 2800 ATES systems have been deployed with storage temperatures typically below 25 °C and only a few with higher temperatures (>40 °C), which would increase the energy density and utility of the stored thermal fluids. Until now, only a few high-temperature aquifer thermal energy storage (HT-ATES) projects have been initiated and are still in operation. These HT-ATES projects have encountered a range of technical and non-technical challenges. This study reviews ten such projects: four in Germany and six in the Netherlands. The non-technical issues include public acceptance, a lack of regulatory framework for these systems, managing overlapping uses of the subsurface, managing changes with the providers and off-takers of thermal energy, and obtaining financing to implement these projects. Common technical issues include geological factors such as incomplete characterization of the subsurface and reservoir heterogeneity; geochemical issues such as mineral scaling, corrosion, and biofouling; lower than expected thermal recovery; and issues with system design and reliability. This review highlights benefits and challenges faced by HT-ATES projects with the goal to use the lessons learned to improve the siting, design, development, and operation of such systems. Recommendations include improved initial subsurface site characterization, use of coupled process models to optimize system design and predict system performance, cascaded uses of stored thermal energy to better utilize the stored heat, monitoring networks to provide feedback on system performance, and expanded system scale to allow for continued operation even when maintenance of some system components is required. Techno-economic modeling and risk analysis could be used to optimize such HT-ATES project design and identify key factors that will affect sustained economic viability. In addition, design flexibility is important for these systems to allow for changing conditions regarding the supply and demand of thermal energy. Adopting these findings should improve the performance and reduce the risks for future HT-ATES projects worldwide. Full article

While community energy initiatives and pilot projects have demonstrated technical feasibility and economic benefits, their site-specific nature limits transferability to systematic, scalable investment models. This study addresses this gap by proposing a modular framework for Renewable Energy Valleys (REVs), developed from real-world Community Energy Lab (CEL) demonstrations in Crete, Greece, which is an island with pronounced seasonal demand fluctuation, strong renewable potential, and ongoing hydrogen valley initiatives. Four modular business schemes are defined, each representing different sectoral contexts by combining a baseline of 50 residential units with one representative large consumer (hotel, rural households with thermal loads, municipal swimming pool, or hydrogen bus). For each scheme, a mixed-integer linear programming model is applied to optimally size and operate integrated solar PV, wind, battery (BAT) energy storage, and hydrogen systems across three renewable energy penetration (REP) targets: 90%, 95%, and 99.9%. The framework incorporates stochastic demand modeling, sector coupling, and hierarchical dispatch schemes. Results highlight optimal technology configurations that minimize dependency on external sources and curtailment while enhancing reliability and sustainability under Mediterranean conditions. Results demonstrate significant variation in optimal configurations across sectors and targets, with PV capacity ranging from 217 kW to 2840 kW, battery storage from 624 kWh to 2822 kWh, and hydrogen systems scaling from 65.2 kg to 192 kg storage capacity. The modular design of the framework enables replication beyond the specific context of Crete, supporting the scalable development of Renewable Energy Valleys that can adapt to diverse sectoral mixes and regional conditions. Full article

Passive design strategies incorporating phase change materials (PCM) provide effective thermal energy storage, improve indoor comfort, and reduce building energy demand. This study aimed to evaluate the effectiveness of partially filled PCM glazing systems in stabilizing indoor thermal comfort under Korean climate conditions, testing the hypothesis that partial integration can provide meaningful diurnal temperature regulation without compromising daylight access. Indoor air, interior and exterior glazing surfaces, and the PCM layer were monitored to evaluate heat transfer, while EnergyPlus simulations extended the analysis to seasonal conditions. The PCM model was developed using the Conduction Finite Difference (CondFD) algorithm and validated against experimental data, reliably reproducing dynamic phase change behavior. Field tests with a 28 °C PCM showed reductions in indoor peak temperatures of about 2.0 °C during daytime and increases of 1.5 °C at night. Under broader climatic simulations, the same PCM achieved up to 3.7 °C daytime reductions and 2.0 °C nighttime increases, depending on outdoor conditions. These findings highlight the potential of PCM-integrated glazing systems for adaptive thermal regulation in Korean climates and suggest broader applicability for passive cooling and heating strategies in buildings facing increasingly variable weather conditions. Full article

Underground gas storage (UGS) is critical to national reserves and seasonal peak-shaving, and its safe operation is integral to energy security. In UGS surface process pipelines, heterogeneous bimetal composite pipes—carbon-steel substrates lined with stainless steel—are widely used but susceptible under coupled thermal–pressure–flow loading to geometry-induced instabilities (local buckling, adhesion, and collapse), which can restrict flow, concentrate stress, and precipitate rupture and unplanned shutdowns. Conventional ultrasonic testing and magnetic flux leakage have limited sensitivity to such instabilities, while standard eddy-current testing is impeded by the ferromagnetic substrate’s high permeability and electromagnetic shielding. This study introduces magnetically saturated pulsed eddy-current testing (MS-PECT). A quasi-static bias field drives the substrate toward magnetic saturation, reducing differential permeability and increasing effective penetration; combined with pulsed excitation and differential reception, the approach improves defect responsiveness and the signal-to-noise ratio. A prototype was developed and evaluated through mechanistic modeling, numerical simulation, laboratory pipe trials, and in-service demonstrations. Field deployment on composite pipelines at the Hutubi UGS (Xinjiang, China) enabled rapid identification and spatial localization of liner collapse under non-shutdown or minimally invasive conditions. MS-PECT provides a practical tool for composite-pipeline integrity management, reducing the risk of unplanned outages, enhancing peak-shaving reliability, and supporting resilient UGS operations. Full article

Power-to-Heat and Seasonal Thermal Energy Storage are emerging technologies that facilitate the integration of variable renewable energy sources into building and district energy systems. This review synthesizes recent advancements in technologies, integration strategies, and case studies, with a particular focus on nearly zero-energy buildings and nearly zero-energy districts. A structured literature survey, prioritizing sources from 2020 to 2025, was conducted to map available options. The analysis includes Power-to-Heat systems, primarily electric boilers and heat pumps, as well as various seasonal thermal energy storage configurations, including Aquifer Thermal Energy Storage, Borehole Thermal Energy Storage, Pit Thermal Energy Storage, Tank Thermal Energy Storage, and Packed Bed Thermal Energy Storage. The findings indicate that coupling renewable energy with Power-to-Heat and seasonal thermal energy storage can significantly enhance the flexibility of buildings and district systems, reducing the curtailment of renewable sources by utilizing surplus electricity from renewable generation, particularly during periods of low demand, and lowering the environmental impact of buildings and district heating networks. Full article

The large-scale integration of renewable energy sources has made power generation highly susceptible to climate variability, increasing operational risks within power systems. The growing frequency of extreme weather events has further intensified uncertainty and stochasticity, thereby elevating risks to supply security. To enhance the operational resilience of modern power systems under extreme weather conditions, this study proposes a multi-temporal and multi-spatial power supply planning model that explicitly incorporates the impacts of such events. First, the effects of extreme weather on the source–grid–load framework are analyzed, and a radiation attenuation model for the rainy season as well as a spatiotemporal evolution model for hurricanes are developed. Subsequently, a climate-dependent power output model is established, employing the Finkelstein–Schafer statistical method to construct a Typical Meteorological Year, which serves as input for the reliable power source modeling. Furthermore, a two-stage power supply planning model based on generation adequacy was established to optimize the location and capacity of various types of backup power sources. Case studies conducted on the IEEE 24-bus system demonstrate that optimized planning of thermal power units and energy storage systems can mitigate the overall power shortfall during extreme weather events, thereby improving the system’s ability to maintain a reliable electricity supply under adverse climate conditions. Full article

Roadway anti-icing requires low-carbon alternatives to chloride salts and electric heating. This study evaluated a seasonal thermal energy storage system that couples a geothermal hydronic heated pavement (HHPS-G) with borehole thermal energy storage (BTES), operated without auxiliary heat. A coupled transient HHPS-G–BTES model was developed and validated against independent experimental data. A continuous cycle was then simulated, consisting of three months of summer pavement heat harvesting and BTES, followed by three months of winter heat discharge. A parametric analysis varied borehole depth (10, 20, and 40 m) and number of units (1, 2, and 4). Results indicated that depth is consistently more effective than unit number. Deeper fields produced larger summer pavement surface cooling with less long-term drift and yielded more persistent winter anti-icing performance. The 40 m 4-unit case lowered the end-of-summer surface temperature by 3.8 °C relative to the no-operation case and kept the surface at or above 0 °C throughout winter. In contrast, the 10 m–1-unit case was near 0 °C by late winter. A depth-first BTES design, supplemented by spacing or edge placement to limit interference, showed practical potential for anti-icing without auxiliary heat. Full article