In lithium-ion battery electrodes, repeated lithium insertion and extraction generate compositional gradients and volumetric changes that produce evolving stress fields and eigenstrains, accelerating mechanical degradation. While existing diffusion-induced stress models often capture only elastic behavior, they rarely provide a closed-form analytical treatment of irreversible deformation or its connection to cyclic degradation. In this work, a transparent analytical framework is developed for a planar electrode that explicitly couples lithium diffusion with elastic-plastic deformation, eigenstrain formation, and fracture-aware stress relaxation. The framework provides a means to quantitatively model the evolution of residual stress gradients, revealing the formation of a damaging tensile state at the electrode surface after delithiation and demonstrating how path-dependent irreversible deformation establishes a degradation memory. A parametric study is used to demonstrate the framework’s capability to clarify the influence of diffusivity and yield strength on residual stress development. This framework, which unifies diffusion, plasticity, and fracture in closed-form mechanical relations, provides new physical insight into the origins of chemo-mechanical degradation and offers a computationally efficient tool for guiding the design of durable next-generation electrode materials where chemo-mechanical strains are moderate.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
This paper explores the possibility of using a solid oxide fuel cell as part of an energy system with an internal combustion engine running on bioethanol, incorporating thermochemical waste gas heat recovery. The main goal of the research is to determine the efficiency of energy con-version in energy systems with deep waste gas heat recovery. To achieve this goal, the following tasks were set: based on experimental studies of a spark-ignition engine running on bioethanol, determine the parameters of the process for synthesizing gas through thermochemical conver-sion; theoretically investigate the efficiency of using a solid oxide fuel cell in combination with a bioethanol thermochemical conversion reactor. The most significant result is the determination of the volt-ampere characteristic of the solid oxide fuel cell and the identification of the poten-tial heat recovery capacity of the internal combustion engine exhaust gases through deep heat recovery. The significance of the obtained results lies in the theoretical and experimental valida-tion of efficient energy conversion of synthesis gas in a solid oxide fuel cell, achieving a high thermodynamic efficiency of the cell (0.95–0.75). The proposed energy system configuration, based on an internal combustion engine running on bioethanol with thermochemical waste heat recovery, allows for a 6.5% increase in the overall system power output. This contributes to re-duced fuel consumption and improved environmental performance. The research findings can be applied in the design and development of highly efficient energy systems with internal com-bustion engines for various applications.
Electrical engineering. Electronics. Nuclear engineering, Production of electric energy or power. Powerplants. Central stations
High penetration of electrical vehicles (EVs) and renewable distributed generators (DGs) into active distribution networks (ADNs) lead to frequent, rapid and fierce voltages magnitudes violations. A novel two-timescale coordination scheme for different types of adjustable devices in ADNs is put forward in this article by organically integrating data-driven deep reinforce-ment learning (DRL) into physical convex model. A Markov Decision Process (MDP) is formulated on slow timescale, in which ratios/statuses of on load transformer changers (OLTCs) and switchable capacitors reactors (SCRs) and ESSs charging/ discharging power are set hourly to optimize network losses while regulating voltages magnitudes. An improved DRL with relaxation-prediction-correction strategies is proposed for eradicating discrete action components dimension curses. Whereas, on fast timescale (e.g., several seconds or minutes), the optimal reactive power of DGs inverters and static VAR compensators (SVCs) in balanced and unbalanced ADNs are set with physical convex optimization to minimize network losses while respecting physical constraints. Five simulations cases with IEEE 33-node balanced and 123-node unbalanced feeders are carried out to verify capabilities of put forward method.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
Chiara Rubaltelli, Marcello Morini, Eugenio Moro
et al.
Highly dynamic and mobile applications, such as vehicular networks, require stable connectivity, which is often challenging to achieve. Network densification is a key approach to address this issue and can be achieved cost-effectively through mobile base stations and wireless relaying. However, existing solutions rely on rigid and complex architectures that hinder deployment in dynamic scenarios. The recently standardized Wireless Access Backhaul (WAB) architecture represents a key evolution, enabling flexible and modular wireless relay networks with native support for mobility and multi-technology wireless backhaul. This paper presents the first experimental realization of a multi-band WAB testbed, combining an FR2 backhaul and an FR1 access link using open-source software and commercial off-the-shelf components. The proposed framework validates end-to-end WAB operation under mobility and demonstrates the extension of FR2 coverage while maintaining compatibility with legacy FR1 user equipment. Experimental campaigns in vehicular and outdoor-to-indoor scenarios confirm that WAB effectively mitigates FR2 limitations, particularly in uplink and Non-Line-of-Sight conditions. These results highlight WAB as a practical and scalable approach for vehicular and next-generation wireless networks.
Muhammad Umair Danish, Kashif Ali, Kamran Siddiqui
et al.
Accurate energy consumption forecasting is essential for efficient resource management and sustainability in the building sector. Deep learning models are highly successful but struggle with limited historical data and become unusable when historical data are unavailable, such as in newly constructed buildings. On the other hand, physics-based models, such as EnergyPlus, simulate energy consumption without relying on historical data but require extensive building parameter specifications and considerable time to model a building. This paper introduces a Physics-Guided Memory Network (PgMN), a neural network that integrates predictions from deep learning and physics-based models to address their limitations. PgMN comprises a Parallel Projection Layers to process incomplete inputs, a Memory Unit to account for persistent biases, and a Memory Experience Module to optimally extend forecasts beyond their input range and produce output. Theoretical evaluation shows that components of PgMN are mathematically valid for performing their respective tasks. The PgMN was evaluated on short-term energy forecasting at an hourly resolution, critical for operational decision-making in smart grid and smart building systems. Experimental validation shows accuracy and applicability of PgMN in diverse scenarios such as newly constructed buildings, missing data, sparse historical data, and dynamic infrastructure changes. This paper provides a promising solution for energy consumption forecasting in dynamic building environments, enhancing model applicability in scenarios where historical data are limited or unavailable or when physics-based models are inadequate.
With the growing electric vehicles (EVs) charging demand, urban planners face the challenges of providing charging infrastructure at optimal locations. For example, range anxiety during long-distance travel and the inadequate distribution of residential charging stations are the major issues many cities face. To achieve reasonable estimation and deployment of the charging demand, we develop a data-driven system based on existing EV trips in New South Wales (NSW) state, Australia, incorporating multiple factors that enhance the geographical feasibility of recommended charging stations. Our system integrates data sources including EV trip data, geographical data such as route data and Local Government Area (LGA) boundaries, as well as features like fire and flood risks, and Points of Interest (POIs). We visualize our results to intuitively demonstrate the findings from our data-driven, multi-source fusion system, and evaluate them through case studies. The outcome of this work can provide a platform for discussion to develop new insights that could be used to give guidance on where to position future EV charging stations.
Antonio Bracale, Pierluigi Caramia, Pasquale De Falco
et al.
Abstract Secondary and primary substations of networks with electric vehicle (EV) chargers and photovoltaics (PVs) experience net loads characterized by uncertainty. Accurate characterization of EV, PV and net load energy profiles is necessary to plan new installations and to develop forecasting methodologies. This paper provides a novel contribution to the energy profile characterization of EVs and PVs, exploiting clustering techniques in a hierarchical framework to eventually characterize the overall net load profiles. In the proposal, the lower levels of the hierarchy identify clusters of EV load and PV generation profiles at individual installations, alternatively using one clustering technique among DBSCAN, Gaussian mixture models (GMMs), K‐means algorithm (KMA), and spectral clustering (SC). The intermediate levels of the hierarchy reconstruct the overall EV load and PV generation profiles through a proposed frequentist combination of the lower‐level profiles. The upper level of the hierarchy characterizes the overall net load through a novel approach based on the quantile convolution of the intermediate‐level EV and PV profiles. Real EV load and PV generation data are used to evaluate the performance of the presented hierarchical methodology, with relative fitting improvements between 1% and 8% (compared to a two‐level hierarchical benchmark) and between 16% and 29% (compared to a direct, non‐hierarchical benchmark).
Sumit Verma, Esra Yalçın Yılmaz, Laura Ortiz Sanguino
et al.
The Permian Basin is one of the most prolific, and currently one of the most active, oil and gas basins in the USA. The Lower Permian strata in the Permian Basin have produced more than 14 billion barrels of oil (BBO), making it the largest volume of hydrocarbon in the basin. Sedimentation in the Midland Basin during late Leonardian through early Guadalupian (ca. 272–269 Ma) resulted in progradation of shelf edge and ultimately closure of the basin by Middle Permian time. We analyzed a merged seismic survey covering parts of the Permian Basin (i.e., Central Basin Platform and Midland Basin) in Andrews, Ector, and Midland Counties, Texas. The seismic survey and well logs show the presence of gently dipping (ca. 1°) clinoforms in the Upper San Andres and Grayburg Formations on the eastern edge of the Central Basin Platform and western Midland Basin. The seismic attributes, curvature, and spectral decomposition identify low sinuosity slope channels oriented north-south, but such channels do not appear beyond the slope. The shelf edge shifts from north to south during deposition of the Upper San Andres and Grayburg Formations. We identify five basinward shifts noted by the migration of the shelf edge toward the basin center and the presence of channel features along the depositional slope. The petrophysical analysis indicates that channels cut into carbonate rocks and are filled by siliciclastic sediments; this interpretation is supported by the most negative curvature anomalies along the channel axes caused by the differential compaction between the carbonate and siliciclastic rocks. There are a few channels with a northwest-southeast strike, which matches the direction of the Concho Lineament observed by satellite data. Such observations are consistent with previous interpretations of the northern Midland Basin closure during Middle-Late Permian time.
Production of electric energy or power. Powerplants. Central stations
This paper presents a methodology for optimizing the short-term operation of electric vehicle (EV) charging and discharging while considering the potential curtailment of active power due to volt-var control (VVC) prioritizing reactive power output. The proposed approach involves exchanging information between the EV aggregator and the distribution system operator (DSO). This approach allows the EV aggregator to optimize EV charge/discharge schedules while considering voltage-related constraints in the distribution system (DS). Initially, the aggregator shares the optimized schedule with the DSO to estimate the anticipated active power reduction through power flow analysis. Subsequently, the aggregator revises the constraint on active power output to avoid its expected curtailment and performs a second optimization for EV charging and discharging operation. Numerical simulations conducted on a realistic DS model in Japan validate the effectiveness of the proposed method in enhancing profitability in the day-ahead market while ensuring the quality of DS voltage. The results demonstrate an increase in profit by shifting the time of EV charging and discharging based on shared information from the DSO.
Distribution or transmission of electric power, Production of electric energy or power. Powerplants. Central stations
Many stroke patients develop ankle deformities due to neurological or non-neurological factors, resulting in abnormal gait patterns. While Ankle-Foot Orthoses (AFOs) are commonly used to address these issues, few are specifically designed for ankle varus. The Elastic Neutral Ankle-Foot Orthosis (EN-AFO) was developed for this purpose. This study aimed to analyze changes in kinematic and kinetic gait data in stroke patients with ankle varus, comparing those walking with and without EN-AFO in both AFO and No-AFO groups. Initially, 30 stroke patients with ankle varus were screened; after exclusions, 17 were included in the final analysis. In the No-AFO group, EN-AFO significantly improved maximal ankle inversion on the affected side during the swing phase (from 4.63 ± 13.26 to 10.56 ± 11.40, <i>p</i> = 0.025). Similarly, in the AFO group, EN-AFO led to a significant improvement in maximal ankle inversion on the less-affected side during the swing phase (from 7.95 ± 10.11 to 12.01 ± 8.64, <i>p</i> = 0.021). Additionally, ground reaction forces on the affected side of the AFO group significantly increased at both the forefoot (from 182.76 ± 61.45 to 211.55 ± 70.57, <i>p</i> = 0.038) and hindfoot (from 210.67 ± 107.88 to 231.85 ± 105.38, <i>p</i> = 0.038) with EN-AFO. Conversely, maximal and minimal thoracic axial rotation on the affected side improved significantly in the No-AFO group compared to the AFO group with EN-AFO, during both the stance and swing phases (stance phase: max improvement from −1.13 ± 1.80 to 4.83 ± 8.05, min improvement from −1.06 ± 2.45 to 5.89 ± 7.56; swing phase: max improvement from −1.33 ± 2.13 to 5.49 ± 7.82, min improvement from −1.24 ± 2.43 to 5.95 ± 7.12; max <i>p</i> = 0.034, min <i>p</i> = 0.016 during stance; max <i>p</i> = 0.027, min <i>p</i> = 0.012 during swing). Furthermore, both maximal and minimal thoracic axial rotation on the less-affected side during the swing phase improved significantly in the No-AFO group (max improvement from −2.09 ± 4.18 to 6.04 ± 6.90, min improvement from −0.47 ± 2.13 to 8.18 ± 10.45; max <i>p</i> = 0.027, min <i>p</i> = 0.012) compared with the AFO group. These findings suggest that EN-AFO may effectively improve gait in stroke patients with ankle varus in the No-AFO group.
Materials of engineering and construction. Mechanics of materials, Production of electric energy or power. Powerplants. Central stations
Solar-powered base stations are a promising approach to sustainable telecommunications infrastructure. However, the successful deployment of solar-powered base stations requires precise prediction of the energy harvested by photovoltaic (PV) panels vs. anticipated energy expenditure in order to achieve affordable yet reliable deployment and operation. This paper introduces an innovative approach to predict energy harvesting by utilizing a novel conditional Long Short-Term Memory (Cond-LSTM) neural network architecture. Compared with LSTM and Transformer models, the Cond-LSTM model reduced the normalized root mean square error (nRMSE) by 69.6% and 42.7%, respectively. We also demonstrate the generalizability of our model across different scenarios. The proposed approach would not only facilitate an accurate cost-optimal PV-battery configuration that meets the outage probability requirements, but also help with site design in regions that lack historical solar energy data.
The rising integration of variable renewable energy sources (RES), like solar and wind power, introduces considerable uncertainty in grid operations and energy management. Effective forecasting models are essential for grid operators to anticipate the net load - the difference between consumer electrical demand and renewable power generation. This paper proposes a deep learning (DL) model based on long short-term memory (LSTM) networks for net load forecasting in renewable-based microgrids, considering both solar and wind power. The model's architecture is detailed, and its performance is evaluated using a residential microgrid test case based on a typical meteorological year (TMY) dataset. The results demonstrate the effectiveness of the proposed LSTM-based DL model in predicting the net load, showcasing its potential for enhancing energy management in renewable-based microgrids.
Aerial base stations (ABSs) mounted on unmanned aerial vehicles (UAVs) are capable of extending wireless connectivity to ground users (GUs) across a variety of scenarios. However, it is an NP-hard problem with exponential complexity in $M$ and $N$, in order to maximize the coverage rate (CR) of $M$ GUs by jointly placing $N$ ABSs with limited coverage range. The complexity of the problem escalates in environments where the signal propagation is obstructed by localized obstacles such as buildings, and is further compounded by the dynamic GU positions. In response to these challenges, this paper focuses on the optimization of a multi-ABS movement problem, aiming to improve the mean CR for mobile GUs within a site-specific environment. Our proposals include 1) introducing the concept of global connectivity map (GCM) which contains the connectivity information between given pairs of ABS/GU locations; 2) partitioning the ABS movement problem into ABS placement sub-problems and formulate each sub-problem into a binary integer linear programming (BILP) problem based on GCM; 3) and proposing a fast online algorithm to execute (one-pass) projected stochastic subgradient descent within the dual space to rapidly solve the BILP problem with near-optimal performance. Numerical results demonstrate that our proposed method achieves a high CR performance close to the upper bound obtained by the open-source solver (SCIP), yet with significantly reduced running time. Moreover, our method also outperforms common benchmarks in the literature such as the K-means initiated evolutionary algorithm or the ones based on deep reinforcement learning (DRL), in terms of CR performance and/or time efficiency.
Wouter Badenhorst, Christian M. Jensen, Uffe Jakobsen
et al.
Redox flow batteries are an emergent technology in the field of energy storage for power grids with high renewable generator penetration. The copper redox flow battery (CuRFB) could play a significant role in the future of electrochemical energy storage systems due to the numerous advantages of its all-copper chemistry. Furthermore, like the more mature vanadium RFB technology, CuRFBs have the ability to independently scale power and capacity while displaying very fast response times that make the technology attractive for a variety of grid-supporting applications. As with most batteries, the efficient operation of a CuRFB is dependent on high-quality control of both the charging and discharging process. In RFBs, this is typically complicated by highly nonlinear behaviour, particularly at either extreme of the state of charge. Therefore, the focus of this paper is the development and validation of a first-principle, control-appropriate model of the CuRFBs electrochemistry that includes the impact of the flow, charging current, and capacity fading due to diffusion and subsequent comproportionation. Parameters for the proposed model are identified using a genetic algorithm, and the proposed model is validated along with its identified parameters using data obtained from a single-cell CuRFB flow battery as well as a simpler diffusion cell design. The proposed model yields good qualitative fits to experimental data and physically plausible concentration estimates and appears able to quantify the long-term state of health due to changes in the diffusion coefficient.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Although originally developed to describe the magnetic behavior of matter, the Ising model represents one of the most widely used physical models, with applications in almost all scientific areas. Even after 100 years, the model still poses challenges and is the subject of active research. In this work, we address the question of whether it is possible to describe the free energy <i>A</i> of a finite-size 2D-Ising model of arbitrary size, based on a couple of analytically solvable 1D-Ising chains. The presented novel approach is based on rigorous statistical-thermodynamic principles and involves modeling the free energy contribution of an added inter-chain bond <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><msub><mi>A</mi><mi>bond</mi></msub><mrow><mo>(</mo><mi>β</mi><mo>,</mo><mi>N</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> as function of inverse temperature <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula> and lattice size <i>N</i>. The identified simple analytic expression for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><msub><mi>A</mi><mi>bond</mi></msub></mrow></semantics></math></inline-formula> is fitted to exact results of a series of finite-size quadratic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>×</mo><mi>N</mi></mrow></semantics></math></inline-formula>-systems and enables straightforward and instantaneous calculation of thermodynamic quantities of interest, such as free energy and heat capacity for systems of an arbitrary size. This approach is not only interesting from a fundamental perspective with respect to the possible transfer to a 3D-Ising model, but also from an application-driven viewpoint in the context of (Li-ion) batteries where it could be applied to describe intercalation mechanisms.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry