Abstract

As the climate changes, the energy flexibility of urban buildings is becoming increasingly critical for the energy transition. The integration of emerging technologies, photovoltaics, energy storage, direct current, and flexibility (PEDF), can empower buildings to accelerate their decarbonization and enhance power system resilience against extreme weather. This study first develops models for the critical components within PEDF buildings, to capture the voltage dynamics of the distribution system and the thermal dynamics of indoor environments. Then, a rolling optimization framework is implemented to schedule energy dispatch from the main grid and energy storage systems, aiming to minimize operation costs. After that, droop control is integrated to provide active frequency support, incorporating a strategy for determining the optimal droop coefficient. Finally, numerical studies are conducted to test the effectiveness of the proposed approach.

Abstract

To facilitate the integration of heterogeneous distributed renewable energy sources (DREs), hybrid AC/DC microgrids (MGs) are constructed by synthesizing the inherent advantages of AC and DC MGs, emerging as promising grid architectures. Among them, the interlinking converter (ILC) serves as the critical interface connecting AC and DC subgrids while enabling bidirectional power exchange. Considering the system deviations caused by droop control and the additional complexity introduced by ILC integration, a distributed cooperative control strategy in hybrid AC/DC MGs is proposed in this paper for coordinating heterogeneous DREs and ILCs within the energy internet paradigm. A comprehensive hierarchical cooperative control framework is established, within which the proposed strategy achieves simultaneous frequency and voltage restoration, proportional power/current sharing and regulated inter-subgrid power exchange. Theoretical stability analysis and detailed simulation studies demonstrate the efficacy of the proposed control strategy.

Abstract

Accurate prediction of photovoltaic power generation is crucial for the effective integration of renewable energy into the power grid, which is beneficial for real-time balance of the power grid and optimization of energy storage systems. However, due to the intermittency and volatility of photovoltaic power generation, achieving accurate photovoltaic prediction remains a challenge, especially for long-term photovoltaic prediction. This paper proposes a new method for long-term photovoltaic power generation prediction. This method innovatively combines graph neural network and LightTS model. Experiments were conducted using the proposed model on datasets from three photovoltaic power stations characterized by distinct terrain and climate conditions. The results show that our model surpasses all benchmark models in long-term forecasting and also achieves notable superiority in medium- and short-term predictions.

Abstract

The retention behavior of water-based fracturing fluids and their impacts on methane adsorption characteristics in deep shale organic nanopores were systematically investigated through molecular simulation. A representative Type II-D kerogen model was employed to examine three prevalent fracturing fluid additives: polyacrylamide (PAM), hydroxypropyl guar gum (GUAR), and cetyltrimethylammonium bromide (CTAB). Complex interactions between fracturing fluids and methane under varying pressure conditions were elucidated through integrated grand canonical Monte Carlo simulations and molecular dynamics simulations, complemented by comprehensive analysis of adsorption isotherms, relative concentration distributions, and Langmuir parameters. A dual adsorption mechanism was demonstrated: while all additives were found to reduce overall methane capacity through pore space occupation (with PAM showing the strongest effect, decreasing Langmuir volume from 24.41 to 20.13 mmol/g), intrinsic methane adsorption capabilities were simultaneously exhibited. Pressure-dependent behavior revealed that low-pressure conditions were dominated by competitive adsorption (GUAR>PAM>CTAB), whereas high-pressure regimes were governed by pore-occupation effects (PAM>CTAB>GUAR). Furthermore, significant differences in additive retention stability were observed, with PAM demonstrating the strongest kerogen adhesion and GUAR exhibiting optimal flowback potential, both of which were directly correlated with their respective interaction energies.

Abstract

China’s offshore wind power experienced rapid growth but a subsequent deceleration. Regaining momentum requires improving lifecycle efficiency, defined as maximizing outputs (i.e., sea-depth, distance-from-shore, electricity generation, and capacity factor) relative to inputs (i.e., turbine number, investment, subsidies, and area) under specified technical and scale conditions. This study analyzes 117 offshore wind farms, representing over 96% commissioned capacity during 2010-2023, using output-oriented Data Envelopment and Malmquist Productivity Index (DEA-Malmquist) assessment to evaluate multi-level efficiencies. At the project level, technical efficiency improved, largely due to enhanced lifecycle electricity generation and optimized spatial utilization. Nevertheless, scale inefficiency remains, reflecting the depletion of high-quality nearshore wind resources and unresolved technological challenges in deep-sea, far-offshore deployments. At the provincial level, Liaoning, Zhejiang, and Hebei exhibit lower technical and scale efficiencies compared to other coastal provinces, primarily attributed to suboptimal nearshore wind resource endowments. At the national level, efficiency evolved from fluctuations (2010-2016) to continuous growth (2017-2019) then declines (2020-2023), with strategic priorities shifting from site selection to turbine layout optimization, and ultimately to far-offshore technology adoption. These findings contribute to theoretical understandings of generation technology evolution and offer policy implications to address technical, financial, and regulatory constraints on sustainable offshore wind development.

Abstract

Urban low-carbon development is a dynamic process requiring a systematic method to observe its progress continuously and quickly. The Energy Allocation Analysis (EAA) and TRO index established at national level can meet this requirement. The paper aims to develop an EAA-TRO method suitable for quickly analyzing urban low-carbon development and validate it by a case study of Beijing. Firstly, an EAA method is established for accounting the responsibilities of energy consumption and carbon emissions in the whole energy system. Secondly, TRO index is established to identify driving forces. The results of the case study indicate that Beijing‘s energy consumption rises from 69.9 Mtce (2017) to 71.2 Mtce (2021), with carbon emissions increasing from 146.6 to 154.8 Mt CO2. The energy supply shifts from coal to electricity and gas which totally account for 70.3% of emission responsibilities in 2021 and are mainly purchased from outside. Meanwhile, the energy consumption becomes more concentrated in buildings which accounts for 57.8% of emission responsibilities. Therefore, more attention must be paid to control building energy consumption and improve energy conversion efficiency while promoting electrification and coal reduction.

Abstract

The Shared Socioeconomic Pathways (SSPs) provide an essential exogenous hypothesis for the Integrated Assessment Models (IAMs) to explore long-term energy transitions and low-carbon trajectories. Nevertheless, their global orientation often neglects national heterogeneities that are critical for effective policymaking. Here, this study proposes the fairness—efficiency challenges as a new research paradigm for redefining SSP narratives at the national level. The socioeconomic governance framework situates energy transition pathways along two normative dimensions: fairness, denoting the equitable distribution of costs and benefits across various regions, social groups, and generations; and efficiency, denoting the technological, institutional, and economic capacity to achieve carbon neutrality in a timely and cost-effective manner. It preserves the original SSP logic while contextualizing it within unique regional governance, developmental, and socioeconomic system structures. The resulting scenario narratives not only supports heterogenetic vision, but also provides carbon neutrality modelers with a transparent, replicable framework for scenario parameterization, uncertainty and risk management, and policy evaluation, strengthening policy relevance of national low carbon energy system projections.

Abstract

Compressed carbon dioxide energy storage has attracted considerable academic interest due to its benefits, including high efficiency and low investment costs. This paper investigates an isobaric carbon dioxide energy storage system and establishes a thermodynamic model to evaluate its system performance. The results indicate that higher pressure in the carbon dioxide high-pressure storage tank can increase the energy density of the system; however, it will reduce the system’s round-trip efficiency. Increasing the reheater’s preheating temperature during the energy storage process improves the system round-trip efficiency and energy density. At the same time, improving the reheater’s split ratio, the low-pressure compressor’s outlet pressure, and the cooling temperature of the first-stage cooler can improve the system round-trip efficiency and energy density.

Abstract

Enhancing the performance of transparent building envelopes is a key strategy for improving building energy efficiency. This study presents a photovoltaic liquid flow window (PV-LFW) as an innovative building-integrated photovoltaic/thermal (BIPV/T) solution. Compared to a conventional single-layer cadmium telluride (CdTe) photovoltaic window (SPV), the PV-LFW not only reduces glazing temperatures under intense solar radiation but also enables full-spectrum solar energy utilization, thereby lowering energy consumption for both air conditioning and hot water systems. The research details the conceptual design and working mechanism of the PV-LFW, followed by a comparative experimental evaluation of its thermoelectric performance against the SPV. Results demonstrate that the PV-LFW effectively functions as a solar thermal collector, achieving an approximate 10°C reduction in CdTe solar cell layer temperature and a 14°C reduction in innermost glass temperature at noon. While the PV-LFW offered only marginal improvements in photovoltaic power generation—due to the low temperature sensitivity of CdTe cells—it exhibited high thermal efficiencies (46.1-61.2%), resulting in overall PV/T efficiencies of up to 68.8%. These findings confirm the potential of PV-LFW for integrated solar energy harvesting and its promise for advancing energy-efficient building envelope technologies.

Abstract

Energy piles integrate building foundations with ground heat exchangers to harvest shallow geothermal energy, providing low-carbon heating and cooling. Subsurface geological conditions—particularly vertically stratified soil layers with contrasting thermal properties—can influence the heat-exchange performance of piles; yet their impact on the accuracy of neural-network predictions remains underexplored. This study quantifies how geological layering affects predictive performance by employing a hybrid spatio-temporal neural network that captures both spatial and temporal characteristics of a pile’s thermal field. The model maintains high predictive accuracy even in layered soils. These findings indicate that, in the absence of groundwater flow or other perturbations, geological layering alone exerts a limited effect on neural-network-based prediction accuracy of energy-pile performance.