Distributed energy resources (DERs) are transforming power networks, challenging traditional operational methods, and requiring new coordination mechanisms. To address this challenge, this paper introduces SecuLEx (Secure Limit Exchange), a market-based paradigm for allocating and trading power injection and withdrawal limits, known as dynamic operating envelopes (DOEs). Under this paradigm, distribution system operators (DSOs) first assign initial DOEs to customers through a fair allocation mechanism. These limits can be exchanged afterward through a market, allowing customers to reallocate them according to their needs while ensuring network operational constraints. We formalize SecuLEx and illustrate DOE allocation and market exchanges on a small-scale low-voltage (LV) network. In this case study, SecuLEx reduces renewable curtailment and improves grid utilization and social welfare compared to traditional approaches.
The maritime industry is among the key players in global trade, transporting the largest portion of the world’s goods. It connects economies, supports millions of jobs, and enables the movement of raw materials, energy resources, and manufactured products efficiently across continents. However, this industry is also a major source of greenhouse gas emissions. Considering these emissions, this industry is undergoing a transition to maritime electrification with net-zero emissions. In order to achieve that goal, batteries are the main tool, including the development of fully electric and hybrid vessels. To this end, this paper provides a structured overview of battery systems in marine electrification, covering various aspects of this rapidly evolving field. It presents an analysis of different ship types and the power topologies of electric and hybrid vessels, supported by updated examples of real-world commercialized and produced vessels across different regions. Additionally, the evolution of battery technology is demonstrated by detailing both commercialized and early-stage batteries, with a focus on their power rating, energy density, specific energy, cycle life, and management systems. Furthermore, the applications of Artificial Intelligence (AI) and digital twin technologies in marine electrification are presented with a detailed review of recent developments. The Artificial Intelligence (AI) strategies are classified into several categories, and a case study from a representative strategy of each class in battery and marine electrification is presented. It also includes guidelines and standards issued by official organizations to ensure compliance and safety in the sector. Additionally, it highlights recently completed and ongoing funded projects in marine electrification in the U.S. and the EU, as well as the related software tools and industry white papers. Moreover, the correlation between battery systems and marine electrification is provided with the United Nation (UN) Sustainable Development Goals (SDGs) and which SDGs they are effective and aligned. Finally, the updated challenges and future research directions are presented for a forward-looking perspective on the marine electrification sector and its transition to a net-zero industry.
Ammonia is a promising carbon-free fuel for future energy systems, but its practical implementation is chal lenged by low laminar flame speeds and elevated NOx emissions. Computational fluid dynamics (CFD) provides a powerful tool for analysing ammonia combustion systems; however, the high computational cost associated with detailed chemical kinetics remains a key limitation for industrial-scale applications. In this study, a Large Eddy Simulation (LES) framework is developed for a 4.6 kW ammonia burner and validated against axial velocity and OH measurements, demonstrating good agreement with experimental data. Building on a recent ammonia reac tion mechanism, a reduced mechanism consisting of 17 species and 79 reactions is derived and implemented in the CFD framework. The reduced mechanism decreases the computational cost from 11,000 to 6279 core-hours/s relative to the original mechanism, and by approximately 10% compared to an alternative reduced mechanism, while retaining accuracy in flame structure, OH fields, and axial velocity. At the same time, the reduced mecha nism preserves higher NO predictions characteristic of the original mechanism and captures NH3 decomposition into H2, which is relevant for flame stabilisation in the burner. These results highlight the potential of targeted mechanism reduction for achieving cost-efficient CFD mod elling of ammonia combustion, thereby supporting future industrial-scale modelling and the adoption of ammonia as an energy carrier.
High penetration of photovoltaic (PV) generation introduces intermittency and uncertainty, challenging the stability and dispatch of modern power systems. Accurate PV power forecasting is crucial for mitigating these challenges. While existing hybrid deep learning models have advanced spatiotemporal feature extraction, they still struggle with modeling long-range dependencies and cross-hourly irradiance-power dynamic couplings, which has become a critical bottleneck limiting long-term forecasting accuracy.To address this issue, this paper proposes an innovative Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-enabled Global Attentive Fusion Network (CGAFN) for refined PV power forecasting. The CGAFN leverages CEEMDAN to decompose and capture both the regular and stochastic components of PV power. It introduces a novel global multi-dimensional coordinate attention module specifically designed to capture spatial heterogeneity and channel correlations in multi-source meteorological data for fine-grained feature extraction, and models global temporal dependencies using a hybrid Long Short-Term Memory (LSTM)-Transformer network. Experimental results on a real-world 20 MW PV plant dataset demonstrate that the proposed model achieves superior performance in day-ahead multi-step PV power forecasting, with average mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) reaching 1.1677 MW, 3.1143 MW2, and 0.8843, respectively. Compared with the mainstream LSTM benchmark, it achieves significant accuracy improvements: MAE and MSE decrease by 22.7% and 38.7%, respectively, and R2 increases by 9.0%.Ablation analysis further demonstrates the effectiveness of each core module and their synergistic effects. The hybrid model provides methodological reference for multi-source time series data-driven energy system forecasting tasks.
The self-heating of biomass piles poses a significant risk of spontaneous ignition during storage. Studies on medium- to large-scale stockpiles remain relatively scarce and lack a systematic analysis of the evolutionary characteristics of various heat sources. This research conducted a 74-day experiment with the objective of accurately measuring the distributions of temperature, humidity, and oxygen concentration inside the biomass pile. The study provided comprehensive and reliable data for the purpose of verifying the accuracy of subsequent numerical simulations. In addition, a Matlab-based numerical calculation platform was developed, which coupled the biomass self-heating sub-models previously developed by the author's team and incorporated the internal convection effect by directly solving the gas velocity distribution within the piles, which is described by the modified Ergun model. This quantitative approach revealed the evolution rules of various heat sources and the influence mechanism of internal convection on the self-heating characteristics of the biomass pile. The experimental results indicate that the maximum temperature within the pile was 79.4 degrees C and that the minimum oxygen concentration was less than 11% during the first three days. After two months, the internal temperature remained at approximately 60 degrees C, with an internal oxygen concentration of approximately 16%. High temperatures were predominantly concentrated in the upper-central region of the pile due to internal convection. The simulation results show deviations of less than 5 degrees C for the temperature and less than 2% for the oxygen concentration. The heat generation from anaerobic metabolism in the central region of the pile was approximately 25 W/m3, whereas that from aerobic metabolism in the periphery reached approximately 90 W/m3. The maximum temperature did not increase further, as heat released from chemical oxidation processes was suppressed by relatively low internal temperatures and moisture evaporation absorbed substantial heat. Neglecting the internal convection effect resulted in a calculated maximum temperature 5 degrees C higher than the experimental data and an unrealistic estimation of the oxygen concentration at 0%, which deviated significantly from the actual measurements. Consequently, incorporating convection is imperative for enhancing the prediction accuracy of self-heating in biomass.
