To improve the inherently low electronic and ionic conductivity of lithium iron phosphate (LFP) cathode materials, the synergistic modification of Al doping and carbon coating has been proven to be an effective strategy. However, the doping effects of different aluminum (Al) sources have not been systematically compared, and the mechanism of the synergistic effect between the characteristics of the Al source and the synthesis process remains poorly understood. To address this, the present study systematically investigated the effects of three Al sources on the structure and electrochemical performance of LFP/C composites under two sintering processes: static and dynamic. Phase and microstructure characterizations confirmed the successful doping of Al3+ and the formation of an effective carbon coating. Electrochemical tests indicated that the choice of Al source and sintering process was strongly coupled: in the dynamic fluidized-bed process, which is highly characterized by efficient mass and heat transfer, Al(OH)3, due to its lower thermal decomposition temperature and the release of active H2O, promoted uniform Al3+ doping and optimized the quality of the carbon coating, thereby achieving the best overall performance. By contrast, under the sluggish reaction kinetics of static sintering, the chemically stable Al2O3 achieved ordered doping through slow solid-state diffusion, demonstrating the best cycling stability. In both processes, the overly stable AlPO4 failed to release Al3+ effectively, resulting in limited performance improvement. This work reveals the key principle that the intrinsic reactivity of the Al source must be matched with the kinetics of the sintering process, deepens mechanistic understanding of the doping modification, and provides clear experimental evidence for the selection of the optimal Al source under different synthesis processes.
The growing demand for sustainable, flexible, and high-performance electrode materials for energy storage has motivated the development of polymer-based composite electrodes with enhanced electrochemical properties. In this study, flexible cobalt oxide (Co3O4)-iron oxide (Fe2O3) nanoparticle-impregnated sodium alginate (NaAlg) as the polymer matrix composite membranes were developed via a simple solution-casting method to exploit the synergistic pseudocapacitive behaviour of mixed metal oxides together with the excellent film-forming ability, flexibility, and eco-friendly nature of NaAlg. The prepared membranes’ structural features, morphology, and electrochemical properties were examined through a set of techniques, such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Universal Testing Machine (UTM), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), potentiodynamic polarisation (PDP), and galvanostatic charge–discharge (GCD). Characterisation techniques validated the effective loading of Co3O4 and Fe2O3 nanoparticles within the NaAlg matrix, and revealed the efficient interfacial interactions, structural integrity, and electrochemical properties of the composites. GCD tests showed a very high specific capacitance of 571.43 F g−1 at 1.2 A g−1. The best-performing electrode produced a top energy density of 155.56 Wh kg−1 at a power density of 2800 W kg−1 and still showed around 91% capacitance retention after 2500 charging–discharging cycles with coulombic efficiency close to 100%. Boosted electrochemical performance is due to the synergistic effect of Co3O4-Fe2O3 nanoparticles that not only offer plenty of electroactive sites but also help in effective electron and ion transport within the polymer matrix. The results obtained here confirmed the capabilities of Co3O4-Fe2O3@NaAlg composite membranes as green and potent electrode materials for future supercapacitor devices.
Accurate state of health (SOH) evaluation is essential for lithium-ion battery safety. Conventional prediction models suffer high computation overhead and insufficient multidimensional feature fusion, leading to poor generalization across working conditions. This work develops the PSA-DMCF-SVAE framework. Ten health factors are extracted from cycling data, and four high-correlation indicators are selected via Pearson screening. The method combines stacked ensemble learning and sparse variational autoencoder (SVAE) for multi-scale aging feature extraction, with an embedded ProbSparse self-attention module to adaptively weight base learners and cut computational complexity. Experiments on NASA battery data reveal over 40% lower prediction error and 42% less feature redundancy than classic stacking architectures. Cross-domain validation on an MIT fast-charging dataset confirms the model’s zero-shot generalization under high-rate, noisy discharge conditions, offering a practical solution for battery management systems.
The volatility of renewable energy generation and nodal electricity prices provides an arbitrage opportunity for Mobile Battery Energy Storage Systems (MBESS) leveraging both temporal and spatial advantages. However, the inherent high complexity and strong randomness of both power and transportation systems lead to complex risks for MBESS control. Existing works mainly consider the market price risk and ignore the transportation system risk caused by traffic congestion. Specifically, they are constrained by two critical limitations: (1) decisions can only be made upon arrival at a destination, making the agent unresponsive on the road, and (2) traffic congestion risk is neither quantified nor controlled, leading to suboptimal routing strategies. To address these limitations, the MBESS needs more flexible “on the road” decision making and multiple risk management capabilities. Guided by this objective, a flexible deep reinforcement learning-based MBESS control framework is proposed, considering both market and traffic congestion risk. First, dynamic routing ability is integrated with the MBESS agent to provide more flexibility in making decisions, regardless of whether the agent has reached the designated location or not. Second, two risk metrics are proposed to quantitatively assess the traffic congestion risk based on moving time, and then the agent can make decisions considering both market and traffic congestion risk. Finally, considering the inefficiency of learning caused by introducing multiple risks, a risk curriculum learning method is proposed to improve the training efficiency and reduce learning costs. These components are unified in the Multiple Risk Estimation SDDPG (MRE-SDDPG) algorithm, which jointly maximizes profitability while controlling electricity price and traffic congestion risk. Simulations in the IEEE 30 bus environment show that the proposed framework can increase profit by 8.6% while reducing the traffic time by 15.8% on average, demonstrating the superiority of our design in considering traffic congestion risk.
The development of battery-powered electric aircraft for use in commercial aviation involves various performance- and safety-based challenges. Cells with high energy densities (for instance Li-Ion pouches) are required to meet the performance requirements, with those currently available encompassing a risk of thermal runaway and other failures. To enable their use, this work provides an investigation into battery abuse conditions and their effect on the overall safety of the system, tailored to electric aviation. A system architecture is presented for a battery-powered electric aircraft, including recommended mitigation methods for the presented failure modes. A framework for determining the overall State of Safety based on the relevant abuse conditions is presented. It is found that the influence of atmospheric operation conditions, flight profiles and vibrations provide the most notable differences between battery-associated risks for aircraft and ground vehicle purposes. Quantification and experimental validation of the impact of these conditions is recommended as a direction for future work.
Hydrogen storage is vital to the development of renewables, especially in low-infrastructure countries. Metal hydrides offer a small but safe solid-state candidate for hydrogen storage at medium pressures and near-ambient temperature, yet large-scale applications face heat-management challenges. In this article, we numerically analyze examples of two large-scale lanthanum pentanickel (LaNi5)-based metal hydride reactor configurations with shell-and-tube heat exchangers. This research studies two large-scale shell-and-tube metal hydride reactor configurations: a tube-side cooling reactor with hydride powder packed in the shell and coolant flowing through internal tubes, and a shell-side cooling reactor using annular hydride pellets with coolant circulating through the shell. The thermal and kinetic performance of these large-scale reactors was simulated using COMSOL Multiphysics (version 6.1) and analyzed under different geometries and operating conditions typical of industrial scales. The tube-side solution provided 90% hydrogen absorption in 1500–2000 s at 30 bar, while the shell-side solution reached the same level of absorption in 430 s at 10 bar. Results show that tube-side cooling has higher storage, while shell-side cooling improves heat removal and kinetics. For energy and maritime transport applications, these findings reveal optimization insights for large-scale, efficient hydrogen storage systems.
The waste heat generated by high-performance computing (HPC) represents an opportunity for advancing the decarbonization of energy systems. Seasonal storage is necessary to regulate the balance between waste heat production and demand. High-temperature aquifer thermal energy storage (HT-ATES) is a particularly well-suited technology for this purpose due to its large storage capacity. However, integrating HT-ATES into energy systems for district heating is complex, affecting existing components. Therefore, this study applies a bi-objective mixed-integer quadratically constrained programming (MIQCP) approach to optimize the energy system at Forschungszentrum Jülich (FZJ) regarding total annualized costs (TAC) and global warming impact (GWI). The exascale computer Jupiter, which is hosted at FZJ, generates a substantial amount of renewable waste heat that is suitable for integration into district heating networks and seasonal storage. Case studies show that HT-ATES integration into the investigated system can reduce GWI by 20% and increase TAC by 1% compared to the reference case. Despite increased TAC from investments and heat pump (HP) operation, summer charging of the HT-ATES remains flexible and cost-effective. An idealized future scenario indicates that HT-ATES with a storage capacity of 16,990 MWh and HPs could cover most of the heating demand, reducing GWI by up to 91% while TAC increases by 6% relative to the reference system.
This paper addresses the operational challenges introduced by the growing share of intermittent renewable energy sources in islanded microgrids. Traditional unit commitment (UC) methods struggle to manage the continuous variations in demand and renewable generation effectively because dispatch setpoints remain fixed between scheduling intervals. To overcome these limitations, a dynamic voltage and frequency controller (DVFC) is proposed. The DVFC uses adaptive critic control and approximate dynamic programming to update mid-level control actions based on measured microgrid states, technical constraints, and look-ahead utility functions. The proposed method is applied to short-term UC, ensuring frequency and voltage regulation while maintaining microgrid stability. Simulation results on the modified CIGRE test system demonstrate that the DVFC reduces frequency deviations by up to 40–50% and voltage deviations by 60–65% compared to conventional UC. In addition, the method lowers operating costs by up to 6% and extends the effective battery lifecycle by nearly twofold by reducing stress and cycling. These results confirm that the DVFC significantly outperforms conventional UC algorithms in both technical performance and economic efficiency.
Current CO2-based energy storage systems still face several unsolved technical challenges, including strong thermal destruction between the multi-stage compression and expansion processes, significant exergy destruction in heat exchange units, limited utilization of low-grade heat, and the lack of an integrated comprehensive performance framework capable of simultaneously evaluating thermodynamic, economic, and environmental performance. Although previous studies have explored various compressed CO2 energy storage (CCES) configurations and CCES–Organic Rankine Cycle (ORC) couplings, most works treat the two subsystems separately, neglect interactions between the heat exchange loops, or overlook the combined effects of exergy losses, cost trade-offs, and CO2-emission reduction. These gaps hinder the identification of optimal operating conditions and limit the system-level understanding needed for practical application. To address these challenges, this study proposes an innovative system that integrates a multi-stage CCES system with ORC. The system introduces ethylene glycol as a dual thermal carrier, coupling waste-heat recovery in the CCES with low-temperature energy utilization in the ORC, while liquefied natural gas (LNG) provides cold energy to improve cycle efficiency. A comprehensive 4E (energy, exergy, economic, and environmental) assessment framework is developed, incorporating thermodynamic modeling, exergy destruction analysis, CEPCI-based cost estimation, and environmental metrics including primary energy saved (PES) and CO2 emission reduction. Sensitivity analyses on the high-pressure tank (HPT) pressure, heat exchanger pinch temperature difference, and pre-expansion pressure of propane (P30) reveal strong nonlinear effects on system performance. A multi-objective optimization combining NSGA-II and TOPSIS identifies the optimal operating condition, achieving 69.6% system exergy efficiency, a 2.07-year payback period, and 1087.3 kWh of primary energy savings. The ORC subsystem attains 49.02% thermal and 62.27% exergy efficiency, demonstrating synergistic effect between the CCES and ORC. The results highlight the proposed CCES–ORC system as a technically and economically feasible approach for high-efficiency, low-carbon energy storage and conversion.
Molten salt heat exchangers are crucial components in systems requiring high-temperature heat transfer and energy storage, especially in renewable energy and advanced nuclear technologies. Their ability to operate efficiently at high temperatures while offering significant energy storage capacity makes them highly valuable in modern energy systems. They have high thermal stability. In the framework of this research, a computational fluid dynamics (CFD) simulation model of the HITEC molten salt cooling system has been developed. HITEC molten salt is a specialized heat transfer and thermal energy storage medium primarily used in industrial processes and solar thermal power plants. It is a eutectic blend of sodium nitrate, sodium nitrite, and potassium nitrate. COMSOL multi-physics code has been employed in this research. It simultaneously solves the fluid flow, energy, and heat conduction transport equations. Two cases have been investigated in this paper: a water flowing velocity of 1 [m/s] and a water flowing velocity of 10 [m/s]. The results indicate that the maximal surface temperature of the Crofer®22 H reached 441.2 °C in the first case. The maximal surface temperature of the Crofer®22 H reached 500 °C in the second case. Crofer®22 H alloy provides excellent steam oxidation, high corrosion resistance, and thermal creep resistance. The proposed HITEC molten thermal system may be applied in the oil and gas industries and in power plants (such as the Organic Rankine Cycle).
The global decarbonisation strategy has accelerated the shift toward renewable energy and electric transport, demanding advanced electrochemical energy storage systems. Conventional anodes such as graphite and silicon composites face challenges in conductivity, stability and cycling performance. MXenes, a class of two-dimensional (2D) materials, offer promising alternatives owing to their metallic conductivity, tunable surface chemistry and high theoretical capacity. Here, we synthesise and characterise Mo2TiC2Tx and V2CTx (T = O, OH, F and/or Cl) MXenes for lithium-ion battery anodes and supercapacitors. Unlike Ti3C2Tx, which stores charge via intercalation and surface redox reactions, Mo2TiC2Tx and V2CTx exhibit conversion-type mechanisms. We also identify novel V2C–VOx heterostructures, achieving a specific capacitance of 532.4 F g−1 at 2 mV s−1 and an initial capacity of 493.3 mAh g−1 at 50 mA g−1 in lithium half-cells, with a low decay rate of 0.071% per cycle over 200 cycles. Pristine Mo2TiC2Tx shows 391.7 mAh g−1 at 50 mA g−1, decaying by 0.109% per cycle. These results experimentally validate theoretical predictions, revealing how MXene structure and transition metal chemistry govern electrochemical behaviour, thus guiding electrode design for next-generation batteries and supercapacitors.
The paper examines the experimental performance of air–rock bed, oil only, and oil–rock bed systems for storing heat suitable for cooking applications. The air–rock bed system is charged using hot air from a compressed air tank, while the oil–rock bed system employs a resistive heating element to heat a small volume of oil, which then circulates naturally. The charging process for the oil systems was controlled by adjusting funnel heights, and temperature measurements were taken using thermocouples connected to a data logger. Both systems can store thermal energy ranging from 4.5 kWh to 8 kWh and achieve temperatures between 150 °C and 300 °C, depending on supply temperatures. The simpler oil–rock bed allows for the direct boiling of water using the high temperature produced, and tests indicated comparable boiling times between systems. The findings suggest that these heat storage systems could enhance the advancement and integration of solar cookers, enabling more flexible cooking options.
The challenge of optimally controlling energy storage systems under uncertainty conditions, whether due to uncertain storage device dynamics or load signal variability, is well established. Recent research works tackle this problem using two primary approaches: optimal control methods, such as stochastic dynamic programming, and data-driven techniques. This work’s objective is to quantify the inherent trade-offs between these methodologies and identify their respective strengths and weaknesses across different scenarios. We evaluate the degradation of performance, measured by increased operational costs, when a reinforcement learning policy is adopted instead of an optimal control policy, such as dynamic programming, Pontryagin’s minimum principle, or the Shortest-Path method. Our study examines three increasingly intricate use cases: ideal storage units, storage units with losses, and lossy storage units integrated with transmission line losses. For each scenario, we compare the performance of a representative optimal control technique against a reinforcement learning approach, seeking to establish broader comparative insights.
This article analyses the possibility of using Li-ion batteries removed from battery electric vehicles (BEVs) as short-term energy storage devices in a near-zero energy building (nZEB) in conjunction with a rooftop photovoltaic (PV) system. The technical and economic feasibility of this solution was compared to that of a standard commercial LIB (Lithium-Ion battery) BESS Battery Energy Storage System). Two generations of the same BEV model battery were tested to analyse their suitability for powering a building. The necessary changes to the setup of such a battery for building power supply purposes were analysed, as well as its suitability. As a result, analyses of profitability over the predicted life span and NPV (net present value) of SLEVBs (second-life BEV batteries) for building power were carried out. The study also conducted preliminary research on the effectiveness of such projects and their pros and cons in terms of security. The author calculates the profitability of a ready-made PV BESS with a set of SLEVBs, estimating the payback periods for such investments relative to electricity prices in Poland. The article concludes on the potential of SLEVBs to support self-consumption in nZEB buildings and its environmental impact on the European circular economy.
Methane, transported as Liquefied Natural Gas (LNG) at −163 °C, is becoming the leading fuel in the decarbonization of the maritime sector within the low-carbon fuels. More than 30 years of knowledge has allowed the development of an extensive offshore supply network that includes regasification plants to store and supply it to the grid, both onshore and offshore. This article first reviews the current state of the sector. Then, the operation of a typical onshore regasification plant and the heat transfer through the storage tanks that causes the generation of boil-off gas (BOG) are analyzed by means of two different methodologies. Finally, and based on the results obtained, the different improvements that can be implemented in this type of installation to improve its energy efficiency and insulation are established, such as, for example, an improvement of more than 4 W/m2 by reinforcing the thickness of the materials of the tank dome.
This paper presents a comparative study of data-driven modeling approaches for vanadium redox flow batteries (VRFBs), utilizing Multiple Linear Regression (MLR) and Random Forest (RF) algorithms. Experimental voltage–capacity datasets from a 1 kW/1 kWh VRFB system were digitized, processed, and used for model training, validation, and testing. The MLR model, built using eight optimized features, achieved a mean error (ME) of 0.0204 V, a residual sum of squares (RSS) of 8.87, and a root mean squared error (RMSE) of 0.1796 V on the test data, demonstrating high predictive performance in stationary operating regions. However, it exhibited limited accuracy during dynamic transitions. Optimized through out-of-bag (OOB) error minimization, the Random Forest model achieved a training RMSE of 0.093 V and a test RMSE of 0.110 V, significantly outperforming MLR in capturing dynamic behavior while maintaining comparable performance in steady-state regions. The accuracy remained high even at lower current densities. Feature importance analysis and partial dependence plots (PDPs) confirmed the dominance of current-related features and SOC dynamics in influencing VRFB terminal voltage. Overall, the Random Forest model offers superior accuracy and robustness, making it highly suitable for real-time VRFB system monitoring, control, and digital twin integration. This study highlights the potential of combining machine learning algorithms with electrochemical domain knowledge to enhance battery system modeling for future energy storage applications.
This study analyzed the frequency control challenges within the West Africa Power Pool Interconnected Transmission System (WAPPITS) as it plans to incorporate variable renewable energy (VRE) resources, such as wind and solar energy. Concerns center on the ability of WAPPITS primary frequency control reserves to adapt to high VRE penetration given the synchronization and frequency control problems experienced by the three separate synchronous blocks of WAPPITS. Optimizing solutions requires a better understanding of WAPPITS’ current frequency control approach. This study used questionnaires to understand operators’ practical experience with frequency control and compared these observations to field tests at power plants and frequency response metrics during system events. Eight (8) of ten (10) Transmission System Operators (TSOs) indicated that primary frequency control service was implemented in the TSO, but nine (9) of ten TSOs indicated that the reserves provided were inadequate to meet system needs. Five (5) of ten (10) respondents answered “yes” to the provision of secondary frequency control service, while only one (1) indicated that secondary reserves were adequate. Three (3) TSOs indicated they have AGC (Automatic Generation Control) installed in the control room, but none have implemented it for secondary frequency control. The results indicate a significant deficiency in primary control reserves, resulting in a reliance on under-frequency load shedding for primary frequency control. Additionally, the absence of an AGC system for secondary frequency regulation required manual intervention to restore frequency after events. To ensure the effectiveness of battery energy storage systems (BESSs) and the reliable operation of the WAPPITS with a higher penetration of inverter-based VRE, this paper recommends (a) implementing and enforcing basic primary frequency control structures through regional regulation and (b) establishing an ancillary services market to mobilize secondary frequency control resources.
Green hydrogen, produced through renewable-powered electrolysis, has the potential to revolutionize energy systems; however, its widespread adoption hinges on achieving competitive production costs. A critical challenge lies in optimising the hydrogen production process to address solar and wind energy’s high variability and intermittency. This paper explores the role of artificial intelligence (AI) in reducing and streamlining hydrogen production costs by enabling advanced process optimisation, focusing on electricity cost management and system-wide efficiency improvements.
This review explores the development of energy storage technologies and governance frameworks in the Asia-Pacific region, where rapid economic growth and urbanisation drive the demand for sustainable energy solutions. Energy storage systems (ESS) are integral to balancing renewable energy fluctuations, improving grid resilience, and reducing greenhouse gas emissions. This paper examines the role of international organisations, including the United Nations, International Energy Agency (IEA), and International Renewable Energy Agency (IRENA), in promoting energy storage advancements through strategic initiatives, policy frameworks, and funding mechanisms. Regionally, the Asia-Pacific Economic Cooperation (APEC), the Association of Southeast Asian Nations (ASEAN), and the Asian Development Bank (ADB) have launched programs fostering collaboration, technical support, and knowledge sharing. Detailed case studies of Japan, Thailand, and China highlight the diverse policy approaches, technological innovations, and international collaborations shaping energy storage advancements. While Japan emphasises cutting-edge innovation, Thailand focuses on regional integration, and China leads in large-scale deployment and manufacturing. This analysis identifies key lessons from these frameworks and case studies, providing insights into governance strategies, policy implications, and the challenges of scaling energy storage technologies. It offers a roadmap for advancing regional and global efforts toward achieving low-carbon, resilient energy systems aligned with sustainability and climate goals.
We report on the first stage of an energy systems integration project to develop hybrid renewable energy generation and storage of hydrogen for subsequent use via research-based low regret system testbeds. This study details the design and construction of a flexible plug-and-play hybrid renewable power and hydrogen system testbed with up to 50 kW capacity aimed at addressing and benchmarking the operational parameters of the system as well as key components when commissioned. The system testbed configuration includes three different solar technologies, three different battery technologies, two different electrolyser technologies, hydrogen storage, and a fuel cell for regenerative renewable power. Design constraints include the current limit of an AC microgrid, regulations for grid-connected inverters, power connection inefficiencies, and regulated hazardous area approval. We identify and show the resolution of systems integration challenges encountered during construction that may benefit planning for the emerging pilot, or testbed, configurations at other sites. These testbed systems offer the opportunity for informed decisions on economic viability for commercial-scale industry applications.
