The sustained lunar exploration heavily relies on a comprehensive understanding and innovative utilization of in-situ resources. Current research has largely focused on material resources such as lunar regolith, while the value of the unique lunar cryogenic environment is often neglected. In this paper, the lunar cold resources are conceptualized as an in-situ environmental resource co-equal with material assets, comprising three primary types: deep-space cold resource, terrain-shielded cold resource, and cryogenic material-based cold resource. As an ultimate heat sink, the deep-space cold resource provides the fundamental cooling source for the thermal control design of lunar bases and equipment. Permanent shadow regions (PSRs), as representative examples of terrain-shielded cold resources, offer a near-perfect cryogenic environment for advanced physics research and the storage of strategic materials. The other terrain-shielded resources, such as lava tubes, demonstrate their potential for lunar base siting by offering constant temperatures and natural shielding from harsh space radiation and micrometeorites. Utilizing cryogenic material-based cold resources, such as water ice, enables the flexible mitigation of peak thermal loads at a lunar base. This paper expands the traditional framework of lunar in-situ resource utilization, forging a new path for the synergistic utilization of diverse cold resources in lunar exploration.

Nearly-zero energy buildings (NZEBs) constitute a critical component of global decarbonization strategies in the building sector, demanding enhanced accuracy in building energy models (BEMs) to serve as reliable tools for design, operation, and performance evaluation. This study proposes a general multi-stage calibration framework to improve sub-hourly prediction accuracy of indoor air states, thermal loads, and power consumption in BEMs, and its application is demonstrated through a real-world NZEB office floor case study. The research first establishes a high-fidelity NZEB simulation platform through coupled simulation softwares (TRNSYS-CONTAM-DAYSIM) and MATLAB, then decomposes the calibration process into three sequential stages following the “indoor temperature/humidity-thermal load-power consumption” causal chain, integrating measured data with optimization algorithm to calibrate influential parameters screened via global sensitivity analysis. The results show significantly improved prediction accuracy for indoor air temperature, cooling load, and chiller power, while reducing calibration time by over 50% compared to conventional single-stage approaches. The adaptive optimization configuration, parameter classification management logic, and multi-typed high-resolution measured data utilization within this hierarchical calibration procedure provide a replicable workflow for similar applications, advancing NZEB modeling capabilities for both research and practice.

Against the backdrop of increasing renewable energy penetration and flexible resources in low-carbon parks, energy systems face heightened uncertainties in multi-energy coupling and dynamic responses. This study develops a carbon-oriented two-stage collaborative optimization framework for integrated energy systems, consisting of day-ahead cost-minimization scheduling and intra-day rolling optimization for dynamic carbon mitigation. The day-ahead stage optimizes cooling/heating sources and thermal storage under forecasted conditions, while the intra-day stage performs carbon-oriented and demand-response adjustments by flexible resources including battery storage and controllable loads. A comprehensive simulation model of a typical lowcarbon park, combined with ultra-short-term forecasting and rolling-horizon optimization is established to verify the performance under various supply-demand conditions. Robustness against operational uncertainties is ensured through representative intra-day disturbance scenarios. Results show that the strategy reduces carbon emissions by 2.1-9.34%, lowers average carbon intensity by 6.8%, decreases operational costs by 7.5%, and increases renewable energy utilization by 15.3%. These findings demonstrate that incorporating dynamic carbon signals into decision-making provides an effective and practical approach for low-carbon operation of park-level integrated energy systems.

The coaxial deep borehole heat exchanger (C-DBHE) is a critical component for deep geothermal development. Multiple coaxial deep borehole heat exchangers (C-DBHEs) are typically deployed to meet high heat extraction demands in practical engineering applications. However, the prohibitively high computational cost of numerical simulations remains a major challenge for modeling C-DBHEs. To address this issue, this study proposes an efficient numerical algorithm based on spatiotemporal decoupling and dimensionality reduction. The key innovation is the synergistic integration of the alternating direction implicit method with explicit heat source treatment, which transforms the three-dimensional problem into a series of one-dimensional calculations, enabling rapid simulations previously unattainable with conventional methods. The reliability of the model algorithm is validated through mesh independence tests, comparisons with literature results, and field measurements. Results demonstrate that the algorithm significantly enhances computational efficiency while maintaining acceptable accuracy. Specifically, a 20-year simulation for a three-borehole configuration can be completed in just 2030 s on a personal computer. Simulations of various C-DBHEs configurations reveal that thermal interference intensifies with increasing borehole cluster size and operation duration. Heat extraction performance of the central borehole in C-DBHEs showing the most significant decline. This study provides an efficient and reliable simulation tool for the design, optimization, and long-term performance assessment of large-scale deep geothermal systems.

The ongoing decarbonization of district heating is increasing interest in medium-deep geothermal systems. However, previous studies have focused on the heat extraction characteristics of the medium-deep borehole heat exchanger (MDBHE) and have neglected investigations of MDBHE-based hybrid heating systems, leaving the effects of subsystem capacity configurations on system performance unclear. This study aims to clarify how subsystem capacity configurations affect system operational performance and the subsurface temperature field. A novel MDBHE hybrid heating system assisted by a solar collector system and a gas boiler system was developed on a dynamic simulation platform. The results show that increasing the solar collector system capacity from 10% to 30% of peak load raises the MDBHE annual average inlet temperature from 16.11 °C to 20.77 °C, reduces energy consumption by 4.20 tce, and slightly improves system efficiency by 1.46%. CO2 and N2O emissions are reduced by 14.76 t and 0.11 kg, respectively. Increasing the ground source heat pump system capacity from 60% to 90% of peak load reduces MDBHE outlet temperature by 4.93%, nearly eliminates the energy consumption of the gas boiler system, and increases system energy efficiency by 76.75%. These findings provide valuable guidance for the design and coordinated operation of MDBHE hybrid heating systems.

Driven by carbon neutrality, end-use electrification, and growing demand-side flexibility, load forecasting in building and district energy systems is moving beyond historical time-series fitting toward support for real-time control, day-ahead scheduling, and market participation. This paper reviews studies published from 2015 to 2025 from four perspectives: bibliometric analysis, task characterization, methodological development, and decision-support applications. Focusing on cooling, heating, electricity, and net loads, it organizes the literature within a three-dimensional framework defined by forecasting target, spatial scale, and temporal scale, showing that forecasting tasks differ in physical drivers, error tolerance, and operational purpose. It then summarizes major methods, including statistical and machine learning methods, deep temporal and spatiotemporal models, physics-informed models, transfer learning, and probabilistic forecasting. The field is shifting from an emphasis on predictive accuracy toward decision value, but progress remains limited by the lack of shared multi-scenario benchmarks, reliance on hard-to-obtain inputs such as occupancy data, unclear decision robustness under coupled model errors, and the gap between research prototypes and real deployment.

The growing adoption of heat pumps presents new opportunities for flexible power system regulation. Mediumdeep ground source heat pump systems (MDGSHPS) exhibit substantial short-term power adjustment capabilities, establishing them as ideal flexible resources on the user side. However, research on MDGSHPS and their interactions with smart grids remains limited, with scarce focus on dynamic oversupply management and targeted optimization control. This study addresses these gaps by quantifying the short-term oversupply flexibility of MDGSHPS through dynamic simulation and regression analysis, which provides a quantitative basis for assessing the system's regulation potential. It further develops a tailored model predictive control (MPC) framework that synergistically optimizes renewable energy utilization and demand response, integrating load prediction models and genetic algorithm-based optimization to adapt to the system's nonlinear and time-varying characteristics. This dual approach fills the void in MDGSHPS's dynamic performance evaluation and enhances its adaptability in complex grid interaction scenarios. Results indicate that adjusting source-side water temperature and leveraging geothermal oversupply can enhance grid stability: during a 1 h pre-spike price period (15 degrees C inlet), cumulative heat intake increases by 64.4%; with thermal energy storage, heat extraction increased by 67.8% while electricity costs were reduced by 62.3%. Compared to the no-energy-storage mode, the fixed time interval control strategy cuts operating costs by 10.5%, while the MPC mode achieves an 11.5% reduction, effectively managing oversupply and supporting demand response under variable loads. This research highlights the potential of MDGSHPS to enhance grid stability and optimize renewable energy integration, thereby contributing to improved power system efficiency.

The Moon exhibits extreme environmental characteristics such as prolonged day-night cycle, large temperature fluctuations, high vacuum, and intense radiation, significantly limiting the applicability of traditional building thermal engineering and energy design methods in this environment. These environmental characteristics pose severe challenges to the thermal performance of lunar base buildings and the stability and safety of their energy systems. Therefore, this paper explores the key technical challenges and primary solutions in the construction of energy systems for long-term lunar habitats. It proposes that by advancing research on precise prediction technologies for building and equipment loads in lunar bases, key technologies for efficient energy storage and photovoltaic power generation, as well as the construction and optimization of multi-energy synergistic energy systems for lunar bases, the autonomy and reliability of lunar energy systems can be enhanced, thereby ensuring the sustained operation of future lunar bases.

The use of deep borehole heat exchanger (DBHE) arrays to exploit deep geothermal energy for clean heating in large-scale buildings has attracted increasing attention. However, existing studies have mainly focused on the heat transfer performance of a single DBHE, while the thermal interaction mechanisms within DBHE arrays and the effects of key operating and design parameters on their thermal, economic, and environmental performance remain insufficiently understood. Moreover, system-level multi-objective optimization and the identification of optimal operating conditions for DBHE arrays are still lacking. This study develops a numerical model for DBHE arrays to analyze inter-borehole thermal interaction mechanisms and quantify the effects of inlet temperature (Tin), flow rate (G), and borehole depth (D) on the coefficient of system performance (CSP), return on investment ratio (E0), and carbon emission reduction (Delta CO2). A multi-objective optimization framework is further established by coupling the NSGA-II algorithm with the TOPSIS-entropy decision-making method to determine the optimal parameter combination. The results show that inter-borehole thermal interference plays a critical role in the long-term performance of DBHE arrays, and a borehole spacing greater than 15 m is required to alleviate performance deterioration. In addition, Tin, G, and D exert significant but distinct effects on the three objectives: increasing Tin and G leads to diminishing performance gains, whereas increasing D improves CSP and Delta CO2 but reduces E0 because of the higher drilling cost. The optimal operating conditions are identified as Tin = 5.3 degrees C, G = 6.5 kg/s, and D = 2760 m, corresponding to CSP = 4.75, Delta CO2 = 2019.91 t, and E0 = 0.42. This study provides new insights into the thermal interaction mechanisms of DBHE arrays and offers a practical basis for their optimal design and operation in sustainable building heating applications.

Deep borehole heat exchanger (DBHE) heating systems have gained increasing attention due to their high efficiency, long-term sustainability, and operational stability. However, traditional DBHE systems are primarily limited to serving as direct building heating systems through one-way heat extraction from the subsurface, lacking flexibility for integration. This study explores a novel hybrid configuration that incorporates thermal energy storage (TES) and solar collectors (SC) into a DBHE-based heating system. A detailed TRNSYS simulation model was developed for an office building in Xi'an, China, incorporating the full configurations of the DBHE, TES, and SC subsystems. Key design parameters, including the solar collector area and tilt angle, TES tank volume, and the heat pump's storage temperature setpoint, were systematically optimized to minimize the system's levelized cost of heating (LCOH). The results indicate that the DBHE-TES-SC system achieved improved heating performance with a more dynamic temperature profile during the heating season. The system also benefits from short-term thermal storage and peak-shaving strategies, offering increased operational flexibility. Over a 10-year operation period, although the coefficient of performance (COP) of the heat pump decreases from 5.63 to 5.14, the outlet temperature decay rate is only 6.5%, demonstrating superior longterm operational sustainability. From an economic perspective, the proposed system reduced annual operating costs by over 36 %, shortened the payback period by 0.45 years, and achieved a lower LCOH than that of a conventional DBHE system. In addition, a preliminary case study is presented to explore the feasibility of deep borehole thermal energy storage (DBTES) for storing intermittent solar energy during the non-heating season. The results show that this strategy can further reduce the annual electricity consumption by an average of 2.47 MWh, underscoring the need for a comprehensive analysis of DBTES in future research. These findings highlight the strong techno-economic potential of integrating TES and SC into DBHE systems. The proposed optimization method can serve as a reference for decision-makers in the geothermal community for building heating applications.

As a significant innovation in ground source heat pump technology, the medium-deep geothermal heat pump system (MD-GHPs) is designed to overcome the limitations like low-temperature inefficiencies of air source heat pumps, low heat exchange efficiency and large land use of shallow ground source heat pumps, and recharge issues of groundwater source heat pumps. Despite extensive global research on MD-GHPs' mathematical models, design optimization, and operational control, the lack of comprehensive review has obscured the developmental context of this technology. This paper constructs a systematic research framework covering five core modules. Firstly, it clarifies the applicable scenarios and technical characteristics of different borehole forms through technical classification. Secondly, the commonly used numerical and analytical models for the heat transfer process of underground buried pipes are described in detail, and key design methods for MD-GHPs are proposed. Then, from the perspective of energy efficiency improvement, the key parameters affecting the system operation are analyzed, and a system configuration scheme for multi-objective optimization and an intelligent control strategy are proposed. Subsequently, it analyzes constraining factors such as policies and systems, economic investment, technology, and social environmental challenges. Finally, three innovative directions are proposed, namely the collaboration of borehole cluster, the complementarity of composite energy systems, and the interaction with the power grid. Through systematically sorting out the technological evolution path and key breakthroughs, this study provides support for the theoretical improvement and engineering practice of MDGHPs, thereby facilitating the large-scale application of this technology in building heating.

Deep borehole heat exchangers (DBHEs) have emerged as a promising solution for clean building heating. However, their long-term performance in severely cold regions remains insufficiently investigated. This study presents continuous field measurements from 2017 to 2025 for a DBHE array coupled with a solar thermal storage system in Daqing, a representative severely cold region in China where winter temperatures can drop to nearly -30 degrees C and the heating season lasts 193 days, thereby filling the gap in long-term data for such regions. The results showed that without thermal storage, the DBHE array's heating capacity and efficiency declined from 2017 to 2022, indicating difficulty in meeting long-term heating demand. After installing the solar thermal storage system in 2022, the evaporator inlet temperature during 2022-2025 stabilized between 6.5 and 7.5 degrees C, while the condenser inlet temperature rose to 36-43 degrees C. During the 2024-2025 heating season, the system's heating power and coefficient of performance (COP) recovered to 195.2 kW and 3.50, increasing by 31.4 % and 25.2 % compared with their lowest values. Furthermore, a numerical model of the DBHE array was developed to investigate the influence of seasonal thermal storage on the rock-soil temperature field. Implementing thermal storage effectively mitigated rock-soil temperature decay and cold accumulation, especially improving temperatures in the shallow and medium layers. The findings demonstrate the effectiveness of the DBHE-solar coupled system in enhancing long-term stability and energy utilization in severely cold regions, providing a solid foundation for its large-scale application.

Flexible energy use in residential communities is crucial for improving renewable energy utilization and balancing supply-demand in smart grids. However, few studies have investigated the flexibility potential of residential air conditioners (ACs), particularly given uncertainties in operating time, set temperature, energy efficiency, and occupant willingness, which significantly affect prediction accuracy. This study proposed an uncertainty-based prediction method to quantify the flexibility scheduling potential of residential ACs. Data on AC energy use behaviors were collected through field measurements and questionnaire surveys. Stochastic prediction models were developed using K-means clustering and Monte Carlo method to represent operational and behavioral uncertainties. Flexibility evaluation indicators were established from perspectives of peak load reduction, energy savings, and economic performance, forming a comprehensive assessment framework. A residential community in China's hot summer and cold winter zone validated the proposed method. Results indicate that load reduction strategies significantly enhance cooling flexibility potential, whereas load suspension strategies effectively improve heating flexibility potential. Occupant willingness strongly influences outcomes. In the "without considering willingness" scenario, the maximum peak-shaving rate reaches 28% for cooling and 100% for heating, the maximum energy-saving rate reaches 29% for cooling and 81% for heating, and the maximum cost-saving rate reaches 14% for cooling and 43% for heating. Compared with this scenario, the "considering willingness" scenario yields differences of 9% and 27% in the peak-shaving rate, 8% and 22% in the energy-saving rate, and 4% and 12% in cost-saving rate for cooling and heating seasons, respectively.

Integrating medium-deep geothermal energy and solar thermal energy in buildings can enhance renewable energy utilization and coordinated multi-energy management while reducing carbon emissions. The long-term operation of medium-deep U-type borehole heat exchanger (MDUBHE) systems may lead to performance degradation and rock-soil temperature attenuation, especially in severe cold regions. Solar-assisted heating is an effective measure to mitigate these problems. However, design methods for such coupled systems remain underdeveloped, and the mechanisms by which solar assistance affects long-term system performance are still not fully understood. In this paper, a coupled simulation platform for a solar-assisted MDUBHE heating system is established, an operation control strategy for the coupled system is developed, the accuracy of the platform is validated, and the effects of solar-assisted measures are investigated. The results show that solar-assisted heating effectively alleviates the attenuation of MDUBHE water temperature, with the inlet and outlet water temperatures in the 15th year increasing by 18.7% and 11.1%, respectively. The operational efficiency of the coupled system is also improved after the integration of the solar collector system. Furthermore, solar-assisted heating reduces the thermal impact of the coupled system on the rock-soil and alleviates its temperature attenuation, with the thermal attenuation rates at depths of z = 1600 m to z = 2000 m reduced to 8.85%, 9.20%, 9.51%, 9.79%, and 10.03%, respectively. This study provides useful guidance for the design and implementation of solar-assisted MDUBHE systems.

As renewable energy penetration increases, mismatches between generation and demand lead to underutilization of clean energy, particularly in industrial parks with overlapping of building and process loads. To enhance system flexibility and renewable utilization, hybrid energy storage systems integrating electrical, thermal, and cooling storage technologies offer a promising solution. However, a scalable and generalizable design framework for such systems remains lacking. Here, we propose a general and scenario-adaptive design framework for hybrid energy storage systems. The framework encompasses five core stages: demand analysis, energy storage selection, energy system modeling, optimization design, and performance evaluation. We develop a hierarchical optimization method to jointly optimize equipment configuration and operation scheduling through iterative feedback between the two layers, achieving better scalability and robustness than existing collaborative approaches. The proposed framework is systematically evaluated across industrial parks spanning different climate zones and energy demand levels. Results show that the proposed framework significantly enhances energy cost savings (43.7%) and reduces carbon emissions (69.9%). This work provides a practical and transferable pathway for deploying hybrid energy storage systems in carbon-intensive sectors, thereby facilitating the low-carbon transition of industrial sectors.

Carbon emission accounting is the core means to grasp the carbon emission level of the park and identify the main carbon emission sources. The boundaries of carbon emission accounting in existing parks are vague, and the accounting methods have not yet formed a clear framework. At the same time, research on carbon emission models mostly stays at theoretical analysis, making it difficult to achieve quantitative goals, and there is no industry recognized unified carbon emission calculation model. This article focuses on tracking the carbon footprint of low-carbon parks, clarifying the geographical and operational emission sources, as well as the scope of carbon emission gas accounting. Multiple accounting methods are compared, and a planning stage based on emission factor method and dynamic simulation of operational stage are proposed to establish an overall dynamic calculation model with carbon reduction accounting, providing quantitative support for energy conservation and carbon reduction in parks.

Integrating intelligent charging stations with building energy systems not only meets the charging requirements of electric vehicle (EV) users but also alleviates the burden of electrified transportation imposed on the utility grid and enhances building energy flexibility. While, stochastic and urgent vehicle-charging demands bring large challenges to the above-stated integrated energy system due to the difficulty of simultaneously achieving high user satisfaction with vehicle charging, grid stability, and building energy flexibility. This study proposed an optimization framework encompassing office buildings, distributed renewable energy systems, charging stations, and EVs to cover dynamic building energy loads and meet diverse vehicle charging demands, and meanwhile maximize distributed photovoltaic (PV) utilization and reduce utility grid burden. An intelligent order-charging strategy, which manages vehicle charging behaviors according to the charging urgent level, was incorporated in the above-stated framework. The research results indicate that Case 3, which operated under the aforementioned framework, exhibited superior energy performance compared to Case 1, which involved a single charging station with traditional charging control, and the EV charging satisfaction increased from 94 % to 100 %. Moreover, the onsite-renewable penetration of the charging station's total energy consumption reached 26 %. Notably, during the periods with limited sunlight (from 7:00 a.m. to 8:00 a.m. and from 4:00 p.m. to 6:00 p.m.), the energy system would reduce about 60 % energy supply of the charging stations to let the utility grid to prioritize meeting the building load demand, thereby significantly enhancing the energy flexibility. This study's work contributes to achieving low carbon and high flexibility of building-charging-station energy system.

As a new form of heat pump system, medium-deep ground source heat pump (MGSHP) systems designed to overcome the limitations like low-temperature inefficiencies of air source heat pumps, low heat exchange and large land use of shallow-buried pipe ground source heat pumps, and recharge issues of groundwater source heat pumps. Current research mainly concentrates on the heating performance of an independent system of MGSHP. But in practice, independent MGSHP heating systems often operate under partial load during the heating season, causing capacity underutilization and poor economics. To address this issue, this study proposes a composite system combining MGSHP with an auxiliary heat source. Given its recent development, optimized configuration designs remain relatively unexplored Therefore, this study establishes an optimization model for the composite system by simulating typical engineering projects using dynamic methods based on building heat load data. The model aims to minimize life cycle costs and simulates the optimal system design capacity. Results indicate that, under the conditions set in this study, the composite system of MGSHP combined with a gas boiler achieves the lowest average annual cost of 301,300 Yuan/Year and the design capacity of the MGSHP under the optimal capacity configuration takes up 54.69 % of the total design capacity. However, in actual operation, the heat pump system prioritizes bearing the base load, and the proportion of the total cumulative heat load that the MGSHP system can bear during the heating season is 73.35 %. These findings can be a valuable reference for practical engineering system capacity configuration.

The heating, ventilation, and air conditioning (HVAC) system is a promising flexibility resource for building energy management and grid stability. Quantitatively assessing HVAC demand response (DR) potential is crucial for integrating buildings into DR programs. However, utilities and load aggregators typically only have total building energy data, making HVAC energy disaggregation challenging. This study proposes an unsupervised and hybrid time-frequency domain decomposition method (MSTL-VMD) to disaggregate HVAC energy from total consumption and an improved equivalent thermal parameter model to quantify DR potential while considering outdoor weather, indoor environment, and occupant comfort. The effectiveness of the proposed method is verified based on historical data from 10 office buildings over 3 years. The results indicate that the HVAC energy can be accurately disaggregated using the MSTL-VMD method with NRMSE of 2.4