Abstract

The current transition toward carbon neutrality creates several previously unforeseen challenges. The intermittent and unpredictable nature of renewable energy can potentially be harmful regarding important factors such as flexibility. The most recent report by the International Energy Agency guarantees an absolute need to double infrastructure equipment by 2030, and they also predict that the system flexibility will need to almost double by the same time. Thus, planning network expansions appropriately is key. This paper will focus on the geospatial analysis of terrain according to several environmental, soil, location, social, and technical constraints. This task is often overlooked in theoretical network planning papers, but it is extremely crucial to showcase practical applicability. For an exposition case study, an excerpt of a real 180-bus network in Portugal will be used and analyzed for a potential expansion.

Abstract

In the development of unconventional oil and gas resources, dynamic production forecasting plays a crucial role in guiding production optimization and resource allocation. However, production variations are influenced by a combination of multi-source heterogeneous factors, including geological conditions, well control parameters, and historical production data. Consequently, forecasting models must handle high-dimensional, nonlinear, and temporally complex data while maintaining strong generalization ability and adaptability.
To address these challenges, this study proposes a dynamic production forecasting method tailored for multi-source heterogeneous data. The approach applies Empirical Mode Decomposition (EMD) to perform multi-scale decomposition of the original time series, enabling the extraction of key features across different frequency levels. These features are then integrated into an iTransformer-based predictive model, enhancing the model’s capability to process non-stationary and multivariate dynamic data. This chapter first establishes and preprocesses the multi-source dataset, covering key dimensions such as well control parameters, geological attributes, and production data, followed by data cleaning and normalization. Subsequently, the structure and optimization process of the dynamic forecasting model are presented in detail, with a focus on the EMD decomposition strategy and enhancements to the iTransformer architecture. Experiments are conducted under both single-well and multi-well scenarios. The results demonstrate that the proposed method achieves high prediction accuracy and strong robustness in dynamic production forecasting tasks. Notably, it exhibits excellent trend-capturing capability in forecasting newly drilled wells. This study provides a novel approach and technical foundation for multi-source data-driven production prediction, offering promising potential for digital modeling and intelligent forecasting of complex reservoir production processes.

Abstract

A major challenge in fast charging is to reduce the charging time without accelerating battery aging. The interaction between different aging mechanisms has not been investigated in the fast-charging optimization. To address this issue, we propose an adaptive, aging-aware fast charging method utilizing deep reinforcement learning to balance charging speed with the aging effects of multiple degradation mechanisms. This method incorporates a coupled degradation model to capture the dynamic characteristics of batteries throughout their lifecycle. Using deterministic policy gradient and Markov decision process (MDP) frameworks, the algorithm optimally adjusts charging currents based on safety constraints and internal coupled aging. We demonstrate the effectiveness of our approach through simulations using the PyBAMM model. Results indicate significant adaptability to variations in battery characteristics at different aging stages.

Abstract

As societies transition to renewable energy sources and combat climate change, understanding the factors
that drive the adoption of new technologies becomes increasingly important. This paper focuses on how energy literacy—an individual’s understanding of energy concepts and technologies—affects the adoption of Electric Vehicles (EVs), Photovoltaic (PV) systems, and Home Energy Monitoring (HEM), using household-level survey data. The logistic regression analysis reveals that an increase in energy literacy significantly boosts the probability of adopting these technologies. Specifically, a one-point rise in energy literacy increases the likelihood of adopting any of the three technologies by at least 20.2%. Notably, energy literacy has a heterogeneous impact on individual technologies, significantly enhancing EV adoption by 38.9%, while showing no statistically significant effect on PV adoption. Our findings highlight the importance of targeted energy education in promoting specific technologies, suggesting that tailored informational campaigns could be more effective in driving the adoption of EVs and HEM.

Abstract

As hydrogen energy increasingly permeates the power system, complex relationships emerge among the power grid, hydrogen energy storage (HES), and hydrogen vehicle (HV). These relationships are influenced by numerous factors, necessitating a systematic analysis. This study utilizes system dynamics (SD) to investigate the grid-hydrogen-vehicle interaction process. A causal loop diagram for the grid-hydrogen-vehicle interaction process is constructed, the flow relationships between internal factors within the power grid and hydrogen modules are refined, and an exploration of how price changes affect the interaction process is conducted. Case study analysis demonstrates that the SD model can simulate the dynamic evolution of grid-hydrogen-vehicle interaction. By guiding interaction behaviors through price adjustments, the model can increase benefits for all parties involved, thereby providing an effective method for establishing practical pricing adjustment mechanisms.

Abstract

In the present study, the streamwise ribs are arranged in the buffer layer without destroying the viscous sublayer of the turbulent boundary layer in the rectangular channel, which aim to achieve the drag reduction and maintain the heat transfer performance simultaneously. The numerical calculation is carried out at Re = 3745, 4745, and 5745. The ribs induce plenty of small-scale secondary vortices in the viscous sublayer and the buffer layer. These vortices can suppress the sweep motions of the high-speed fluid carried by large-scale vortices. The friction loss on the bottom of the channel significantly decreases and the drag reduction efficiency can be up to 26.282% at Re = 5745. Meanwhile, the sweeping motion of the fluid between the adjacent ribs destroys the temperature boundary layer and the heat transfer performance is intensified. The excess heat transfer can compensate the local heat loss below the ribs so that the heat transfer performance maintains at the original level at Re =3745. The Nusselt number on the bottom even increases at higher Re. The maximum comprehensive performance coefficient is up to 1.112.

Abstract

The thermal accumulation caused by low-temperature oxidation(LTO) reactions in the formation significantly impacts the progression of these reactions. To clarify the blocking pattern of pores due to the deposition of heavy components generated by crude oil oxidation during the thermal accumulation process, this study employed serial core displacement and nuclear magnetic resonance (NMR) scanning experiments. It investigated the effect of thermal accumulation on the distribution pattern of residual oil in the core. Additionally, chromatographic experiments were used to explore the degree of crude oil oxidation and the pattern of oxygen consumption along the path under different thermal accumulation conditions. The results of the core displacement experiments show that as the temperature increases, the amount of heavy components produced by oxidation increases, and the degree of oxygen consumption per unit volume of the formation also increases. The oxygen consumption after core displacement is 16.92% at 89℃, 29.78% at 100℃, and 51.2% at 120℃. The results of NMR scanning indicate that the heavy components produced by oxidation can block the dominant airflow channels and increase the gas sweep range. The recovery rates at 89℃, 100℃, and 120℃ are 51.27%, 58.62%, and 59.37%, respectively. The recovery rates for pores smaller than 0.05μm are 0.9%, 0.95%, and 0.96%, while the recovery rates for pores larger than 0.48μm are 47.34%, 53.57%, and 54.29%, respectively. This paper can provide theoretical guidance for improving recovery efficiency through oxygen-reduced air flooding.

Abstract

The use of hydrogen instead of coke for iron reduction, known as hydrometallurgy, is a potential route to reduce carbon emissions in the iron and steel industry. Hydrogen is used as a heat source and reactant, which is governed by both chemical equilibrium and heat supply. Current processes face the challenge of high energy consumption arising from the heat demand and gas consumption. To alleviate this challenge, we used different H2/CO at different stages of iron oxide reduction to utilize the heat of reaction to drive endothermic reactions. In this way, it is possible to achieve auto-thermal process or less energy consumption. Based on this idea, we proposed a methane carbon cycling reforming system integrated iron direct reduction. This system yields H2 and CO separately, and reducing gas can be directly delivered to the ironmaking system in special proportions. The energy consumption is 8.73 GJ/t DRI. This study provides a promising way for the construction and integration of efficient and low-carbon emission hydrogen ironmaking systems.

Abstract

The focus of this study is to conduct a techno-economic evaluation of a chemical production plant that utilizes carbon-, nitrogen- and hydrogen-containing steel mill off-gases as a material feedstock. The chemical products analyzed are acetic acid, ammonia, methanol, and urea, all of which, except ammonia, are classified under the carbon capture and utilization (CCU) concept. In order to support the investment decision-making process, a superstructure optimization model is employed, which includes the main technical plants (e.g. reactors, storages and separators) and plant layout decisions (e.g. the choice between two reactors). To determine the impact of future developments, five scenarios are considered, focusing on environmental, technical, and economic key factors for the target operational year of 2040. The scenario-dependent optimization results represent the optimal CCU concept with the highest net present value (NPV) for a German steel plant in Duisburg in 2025. These results, including the chemical plant layout with selected chemicals and plants, demonstrate that different scenarios can lead to significant changes in the NPV and chemicals produced and subsequently exported. The results of this study can be utilized as a foundation for investment decisions.

Abstract

With the integrated energy system (IES) that considers multi-energy complementarity and energy cascade utilization becoming an important trend in energy innovation, aiming at the current IES carbon emission reduction task and safe and stable operation requirements, accurately solving the maximum carbon emission capacity under the safe operation of IES can warn the working state of the system and provide references for carbon emission reduction tasks. Firstly, the steady-state IES energy flow and carbon flow model of electric-gas coupling is established. On this basis, the calculation model of IES maximum carbon emission capacity was established. Then, the mathematical model of IES carbon emission capacity curve is established. Finally, a numerical example is given to verify the effectiveness of the proposed model.