Search Results (5,633)

This work develops a methodology for operating Battery Energy Storage Systems (BESSs) in distribution networks, connected in parallel with a medium- and small-scale photovoltaic Distributed Generator (PMGD), focusing on a real project located in the O’Higgins region of Chile. The objective is to increase energy sales by the PMGD while ensuring compliance with operational constraints related to the grid, PMGD, and BESSs, and optimizing renewable energy use. A real distribution network from Compañía General de Electricidad (CGE) comprising 627 nodes was simplified into a validated three-node, two-line equivalent model to reduce computational complexity while maintaining accuracy. A mathematical model was designed to maximize economic benefits through optimal energy dispatch, considering solar generation variability, demand curves, and seasonal energy sales and purchasing prices. An energy management system was proposed based on a master–slave methodology composed of Particle Swarm Optimization (PSO) and an hourly power flow using the successive approximation method. Advanced optimization techniques such as Monte Carlo (MC) and the Genetic Algorithm (GAP) were employed as comparison methods, supported by a statistical analysis evaluating the best and average solutions, repeatability, and processing times to select the most effective optimization approach. Results demonstrate that BESS integration efficiently manages solar generation surpluses, injecting energy during peak demand and high-price periods to maximize revenue, alleviate grid congestion, and improve operational stability, with PSO proving particularly efficient. This work underscores the potential of BESS in PMGD to support a more sustainable and efficient energy matrix in Chile, despite regulatory and technical challenges that warrant further investigation. Full article

This work presents the design, modeling, and experimental validation of an innovative, highly autonomous, and economically viable photovoltaic solar cooker, integrating a robust battery storage system. The system combines 1200 Wp photovoltaic panels, a control block with DC/DC power converters and digital control for intelligent energy management, and a thermally insulated heating plate equipped with two resistors. The objective of the system is to reduce dependence on conventional fuels while overcoming the limitations of existing solar cookers, particularly insufficient cooking temperatures, the need for continuous solar orientation, and significant thermal losses. The optimization of thermal insulation using a ceramic fiber and glass wool configuration significantly reduces heat losses and increases the thermal efficiency to 64%, nearly double that of the non-insulated case (34%). This improvement enables cooking temperatures of 100–122 °C, heating element surface temperatures of 185–464 °C, and fast cooking times ranging from 20 to 58 min, depending on the prepared dish. Thermal modeling takes into account sheet metal, strengths, and food. The experimental results show excellent agreement between simulation and measurements (deviation < 5%), and high converter efficiencies (84–97%). The integration of the batteries guarantees an autonomy of 6 to 12 days and a very low depth of discharge (1–3%), allowing continuous cooking even without direct solar radiation. Crucially, the techno-economic analysis confirmed the system’s strong market competitiveness. Despite an Initial Investment Cost (CAPEX) of USD 1141.2, the high performance and low operational expenditure lead to a highly favorable Return on Investment (ROI) of only 4.31 years. Compared to existing conventional and solar cookers, the developed system offers superior energy efficiency and optimized cooking times, and demonstrates rapid profitability. This makes it a sustainable, reliable, and energy-efficient home solution, representing a major technological leap for domestic cooking in rural areas. Full article

The transition to renewable energy is generating numerous changes across different continents, some with greater impact than others, but the progress achieved is recognized and widely accepted. In particular, there are various solutions that include electric vehicles as elements that influence grid behavior when connected. Higher levels of electric vehicle penetration can present opportunities and solutions related to energy storage, V2G connections encompassing the distribution system, and long-term evaluation. High participation in V2G connections maintains the availability of the electrical system, while the high proportion of variable renewable energy sources forms the backbone of the overall electrical system. This study presents a systematic review of V2G systems in the Americas. The design of the Sustainable Mobility scenario and the high participation of V2G maintain the balance of the electrical system for most of the day, simplifying storage equipment requirements. Consequently, the influence of V2G systems on energy storage is an important outcome that must be considered in the energy transition and presents development opportunities for the various countries that make up the Americas. The stored electricity will not only serve as storage for future grid use, but V2G batteries will also act as a buffer between generation from diversified renewable sources and the end-use stage. This article shows that research on the design of V2G energy systems in scientific publications is relatively recent, but it has gained increasing attention in recent years. In total, 151 articles published since 1995 have been identified and analyzed. The overall result indicates that North American countries have developed the most V2G applications, and their deployment in the coming years will be significant. Meanwhile, in South and Central America, these systems are not yet being fully utilized due to the lack of growth in the electric vehicle market. Full article

As the core technology of the new energy revolution, lithium-ion batteries have broad development prospects and significant strategic importance. With continuous improvements in energy density, enhanced safety, and breakthroughs in fast-charging technology, lithium-ion batteries will play a more substantial role in fields such as new energy vehicles and energy storage. Nevertheless, the development of the lithium-ion battery industry still faces safety issues related to thermal runaway risks. The intense exothermic reactions during thermal runaway can release flammable gases, potentially leading to uncontrolled combustion or explosions, thereby posing major safety threats. This paper reviews the analysis of gas composition and patterns during lithium-ion battery thermal runaway under different conditions, as well as research on gas explosion characteristics. It introduces advanced methods for gas detection and suppression during thermal runaway and summarizes studies on the chemical kinetic mechanisms and predictive models of gas generation during thermal runaway. These studies provide a scientific basis for improving the reliability of renewable energy storage systems and formulating and refining battery safety standards. Full article

The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium’s abundance, necessitates materials like hard carbon for the anode, as sodium’s larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal–nitrogen–carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel–iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes—two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance—and Single-Atom Catalysts (SACs)—which maximize metal utilization—are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Full article

Ionic liquids (ILs) have attracted considerable attention for light olefin separation owing to their negligible vapor pressure, excellent thermal stability, and tunable molecular structures. However, their intrinsically high viscosity severely restricts gas diffusion, leading to poor mass-transfer efficiency and limited separation performance in bulk form. Herein, we report the develop a high-performance adsorbent by immobilizing a silver-functionalized ionic liquid within ordered mesoporous MCM-41 to overcome the diffusion limitations of bulk ILs. The IL@MCM-41 composites were prepared via an impregnation–evaporation strategy, and their mesostructural integrity and textural evolution were confirmed by XRD and N₂ sorption analyses. Their C₂H₄/C₂H₆ separation performance was subsequently evaluated. The composite with a 70 wt% IL loading achieves a high C₂H₄ uptake of 25.68 mg/g and a C₂H₄/C₂H₆ selectivity of 15.59 in breakthrough experiments (298 K, 100 kPa). X-ray photoelectron spectroscopy results are consistent with the presence of reversible Ag⁺–π interactions, which governs the selective adsorption of C₂H₄. Additionally, the composite exhibits excellent thermal stability (up to 570 K) and maintains stable separation performance over 10 adsorption–desorption cycles. These IL@MCM-41 composites have significant potential for designing sorbent materials for efficient olefin/paraffin separation applications. Full article

Current scheduling strategies for energy storage batteries in agricultural parks generally overlook the issue of battery lifespan degradation, which significantly undermines the system’s economic efficiency and long-term reliability. To address this problem, this paper proposes an optimal scheduling method for integrated energy systems in agricultural parks that takes into account the operational reliability of energy storage batteries. First, a battery capacity degradation model integrating both cycle aging and calendar aging is established, and the reliability of multiple components within the energy storage system is evaluated using Monte Carlo simulation. On this basis, an optimization scheduling model aimed at minimizing the total system operating cost is developed, dynamically balancing economic performance and battery service life. Finally, the proposed method is validated through a practical case study of a facility-based agricultural industrial park. The results demonstrate that, while ensuring stable system operation, the approach effectively extends the service life of energy storage equipment by 8–9 years, reduces the average daily operating cost by 61.94 yuan, and increases the power supply reliability rate to 99.921%. Full article

Developing cost-effective yet high-performance hard carbon anodes is critical for advancing the commercialization of sodium-ion batteries (SIBs), as they offer a balance of low cost, high capacity, and compatibility with Na⁺ storage mechanisms. Herein, waste towels, an abundant, low-cost precursor with a high carbon yield (>49%), were utilized to synthesize hard carbons via a two-step process: pre-oxidation at 250 °C to stabilize the fibrous structure, followed by carbonization at 1100 °C (THC-1100), 1300 °C (THC-1300), or 1500 °C (THC-1500). Electrochemical evaluations revealed that THC-1300, carbonized at an intermediate temperature, exhibited superior Na⁺ storage performance compared to its counterparts: it delivered a high reversible specific capacity of ~320 mAh/g at 1.0 C (1 C = 320 mA/g), with 78% capacity retention after 200 cycles, demonstrating excellent long-term cyclic stability. Its rate capability was equally impressive, achieving specific capacities of 341.5, 331.2, 302.0 and 234.8 mAh/g at 0.2, 0.5, 2.0 and 5.0 C, respectively, indicating efficient Na⁺ diffusion even at high current densities. Notably, THC-1300 also showed an improved initial Coulombic efficiency (ICE) of 75.4%, reflecting reduced irreversible Na⁺ consumption during the first cycle. These enhancements are attributed to the synergistic effects of THC-1300’s optimized structural and textural properties: a balanced interlayer spacing (d₍₀₀₂₎ = 0.387 nm) that facilitates rapid Na⁺ intercalation, a low BET surface area (1.62 m²/g) helps to minimize electrolyte side reactions. The combined advantages of high specific capacity, improved ICE, and remarkable cycling stability position this waste-towel-derived hard carbon as a highly viable and sustainable candidate for anode materials in next-generation SIBs, addressing both performance and cost requirements for large-scale energy storage applications. Full article

Rapid deployment of rooftop photovoltaics (PV), electric heating, and electric vehicles (EVs) is stressing low-voltage feeders in cold climates, where winter peaks push aging transformers to their limits. This paper quantifies how much stationary and mobile storage is required to keep feeder power nearly flat over a full year in such conditions. A mixed-integer linear programming (MILP) model co-optimizes stationary battery energy storage systems (BESSs) and EV flexibility, including lithium-ion degradation, under a flatness constraint on transformer loading, i.e., the magnitude of feeder power exchange (import or export) around a seasonal target. The framework is applied to a 48-dwelling neighborhood in Ardahan, northeastern Turkey (mean January ≈ −8 °C) with rooftop PV and an emerging EV fleet. Three configurations are compared: unmanaged EV charging, optimized smart charging, and bidirectional vehicle-to-grid (V2G). Relative to the unmanaged case, smart charging reduces optimal stationary BESS capacity from 4.10 to 2.95 MWh, while V2G further cuts it to 1.23 MWh (≈70% reduction) and increases flat-compliant hours within ±0.5 kW of the target transformer loading level from 92.4% to 96.1%. The levelized cost of demand equalization falls from 0.52 to 0.22 EUR/kWh, indicating that combining modest stationary BESSs with V2G can make feeder-level demand flattening technically and economically viable in cold-climate residential districts. Full article

A growing number of fires and explosions in energy storage plants have been triggered by the thermal runaway of lithium-ion batteries. Owing to the complex physicochemical properties of these batteries, their fire safety issues remain unresolved and constitute a major obstacle to the large-scale deployment of energy storage systems. Compared with conventional extinguishing media, liquid nitrogen (LN₂) offers a dual suppression mechanism, i.e., rapid endothermic vaporization and oxygen displacement by inert nitrogen gas, making it highly suitable for lithium-ion battery fire control. However, the key operational parameters governing its suppression efficiency remain unclear, leading to excessive or insufficient LN₂ use in practice. This study established a dedicated experimental platform and designed 10 experimental conditions, each repeated three times, to investigate the propagation of thermal runaway between adjacent batteries and to quantify the suppression performance of LN₂ under varying nozzle diameters and injection strategies. Results demonstrate that under identical injection pressures, larger nozzle diameters significantly outperform smaller ones in cooling and suppression efficiency. The optimal nozzle diameter was found to be 14 mm, achieving a cooling efficiency of 40%. Furthermore, intermittent LN₂ injection of equal total mass outperformed continuous injection, with a 45 s intermittent duration achieving a cooling efficiency of 63%, 23% higher than continuous injection. These findings provide quantitative guidance for the design of LN₂-based suppression systems in large-scale lithium-ion battery energy storage modules. Full article

Rechargeable lithium-, sodium-, and potassium-ion batteries are utilized as essential energy storage devices for portable electronics, electric vehicles, and large-scale energy storage systems. In these systems, anode materials play a vital role in determining energy density, cycling stability, and safety of various batteries. However, the complex electrochemical reactions and dynamic changes that occur in anode materials during charge–discharge cycles generate major challenges for performance optimization and understanding failure mechanisms. In situ characterization techniques, capable of real-time tracking of microstructures, composition, and interface dynamics under operating conditions, provide critical insights that bridge macroscopic performance and microscopic mechanisms of anodes. This review systematically summarizes the applications of such techniques in studying anodes for lithium-, sodium-, and potassium-ion batteries, with a focus on their contributions across different anode types. It also indicates current challenges and future directions of these techniques, aiming to offer valuable references for relevant applications and the design of high-performance anodes. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for the safe operation of electric vehicles and energy storage systems. However, most existing methods rely on complete charging curves or manual feature engineering, making them difficult to adapt to practical scenarios where only limited charging segments are available. To fully exploit degradation information from limited charging data, this paper proposes a dual-modal mixture of Kolmogorov–Arnold network (DM-MoKAN) for lithium-ion battery SOH estimation using only early-stage constant-current charging voltage data. The proposed method incorporates three synergistic modules: an image branch, a sequence branch, and a dual-modal fusion regression module. The image branch converts one-dimensional voltage sequences into two-dimensional Gramian Angular Difference Field (GADF) images and extracts spatial degradation features through a lightweight network integrating Ghost convolution and efficient channel attention (ECA). The sequence branch employs a patch-based Transformer encoder to directly model local patterns and long-range dependencies in the raw voltage sequence. The dual-modal fusion module concatenates features from both branches and feeds them into a MoKAN regression head composed of multiple KAN experts and a gating network for adaptive nonlinear mapping to SOH. Experimental results demonstrate that DM-MoKAN outperforms various baseline methods on both Oxford and NASA datasets, achieving average RMSE/MAE of 0.28%/0.19% and 0.89%/0.71%, respectively. Ablation experiments further verify the effective contributions of the dual-modal fusion strategy, ECA attention mechanism, and MoKAN regression head to estimation performance improvement. Full article

Rapid decarbonization and decentralization of power systems are driving the integration of renewable generation, energy storage and digital technologies into unified energy ecosystems. In this context, photovoltaic (PV) systems combined with battery and hydrogen storage and blockchain-based platforms represent a promising pathway toward sustainable and transparent energy management. This study evaluates the techno-economic performance and operational feasibility of integrated PV systems combining battery and hydrogen storage with a blockchain-based peer-to-peer (P2P) energy trading platform. A simulation framework was developed for two representative consumer profiles: a scientific–educational institution and a residential household. Technical, economic and environmental indicators were assessed for PV systems integrated with battery and hydrogen storage. The results indicate substantial reductions in grid electricity demand and CO₂ emissions for both profiles, with hydrogen integration providing additional peak-load stabilization under current cost constraints. Blockchain functionality was validated through smart contracts and a decentralized application, confirming the feasibility of P2P energy exchange without central intermediaries. Grid electricity consumption is reduced by up to approximately 45–50% for residential users and 35–40% for institutional buildings, accompanied by CO₂ emission reductions of up to 70% and 38%, respectively, while hydrogen integration enables significant peak-load reduction. Overall, the results demonstrate the synergistic potential of integrating PV generation, battery and hydrogen storage and blockchain-based trading to enhance energy independence, reduce emissions and improve system resilience, providing a comprehensive basis for future pilot implementations and market optimization strategies. Full article

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

This paper addresses the critical challenge of multi-objective optimization in residential Home Energy Management Systems (HEMS) by proposing a novel framework based on an Improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. The study specifically targets the low convergence efficiency of Multi-Agent Deep Reinforcement Learning (MADRL) for coupled Battery Energy Storage System (BESS) and Air Source Heat Pump (ASHP) operation. The framework synergistically integrates an action constraint projection mechanism with an economic-performance-driven dynamic learning rate modulation strategy, thereby significantly enhancing learning stability. Simulation results demonstrate that the algorithm improves training convergence speed by 35–45% compared to standard MAPPO. Economically, it delivers a cumulative cost reduction of 15.77% against rule-based baselines, outperforming both Independent Proximal Policy Optimization (IPPO) and standard MAPPO benchmarks. Furthermore, the method maximizes renewable energy utilization, achieving nearly 100% photovoltaic self-consumption under favorable conditions while ensuring robustness in extreme scenarios. Temporal analysis reveals the agents’ capacity for anticipatory decision-making, effectively learning correlations among generation, pricing, and demand to achieve seamless seasonal adaptability. These findings validate the superior performance of the proposed centralized training architecture, providing a robust solution for complex residential energy management. Full article