Design Of Energy Storage System Research Articles

Internal temperature estimation for lithium-ion batteries through distributed equivalent circuit network model

Lithium-ion cells experience significant internal thermal gradients during operation, with a direct impact on their safety, performance, cost and lifetime. The estimation of the internal temperature of cells is therefore particularly important. In this work, a 3D distributed electro-thermal model for internal temperature estimation is developed for a cylindrical cell (LG M50T, NMC811). The model is parameterized and comprehensively validated against experimental data for 21700 cells, including direct core temperature measurements. Multiple types of electrical load are considered, including constant current discharge, pulse discharge, drive cycle and instant discharge/charge switching. The developed model is used to estimate core temperature based on surface temperature measurement. The predictions are shown to have good accuracy at relatively low computational cost. We show that the widely adopted two-node lumped thermal estimation model is increasingly inaccurate for more aggressive discharges, when thermal gradients become higher. Compared to the standard two-node model, the distributed equivalent circuit network model predicts the effects of detailed internal cell structure (electrode, current collector, metal can and tab) and distributed internal heat generation. The results are of immediate interest to cell manufacturers and battery pack designers, while the modelling and parameterization framework is a useful tool for energy storage systems design.

Frequency and power shaving controller for grid-connected vanadium redox flow batteries for improved energy storage systems

In this research, the performance of vanadium redox flow batteries (VRFBs) in grid-connected energy storage systems centering on frequency and power sharing using voltage source inverters was evaluated. VRFBs are increasingly promising due to their scalability and long lifespan. We explore the impact of voltage source inverters on the frequency of the power supply when there is a change in load and power sharing between the grid and VRFB through MATLAB simulations. The test system demonstrates a storage system at an 11-kV substation and considers a load profile commencing from residential and commercial consumers. The outcome of this study reveals that VRFBs provide the required power to maintain load demand. Due to fast response time, VRFBs also regulate the frequency efficiently during the change in load demand and maintaining current total harmonic distortions (THDs). This research confirms the design and operation of VRFB-based energy storage systems, contributing to resilient grid infrastructure and reliable power distribution systems.

Voltage Optimization on Low Voltage Distribution Transformer Zones Using Batteries in Uganda

In the context of Uganda's rapidly growing energy demands and the need for sustainable solutions, this study explores the implementation of voltage optimization techniques in Low Voltage (LV) distribution transformer zones. The research focuses on the innovative integration of batteries to optimize voltage levels, thereby enhancing the efficiency and reliability of the electrical distribution system. By analyzing real-time data from various LV transformer zones in Uganda, this study investigates the impact of voltage fluctuations on the overall power distribution network. The research methodology involves the design and deployment of battery energy storage systems (BESS) strategically placed within LV distribution transformer zones. These BESS units are utilized to store excess energy during periods of low demand and release it during peak hours, ensuring consistent voltage levels and minimizing losses in the distribution network. The study evaluates the effectiveness of this approach through extensive simulations and on-site experiments, considering factors such as battery capacity, charging/discharging rates, and load variations. A comprehensive cost-benefit analysis is conducted to evaluate the potential financial savings and environmental impact associated with this sustainable energy solution. The findings of this research indicate significant improvements in voltage regulation, reduced system losses, and enhanced reliability in LV distribution transformer zones. Additionally, the study demonstrates the feasibility of integrating batteries into the existing infrastructure, thereby contributing to the optimization of the energy distribution system in Uganda. The outcomes of this research provide valuable insights for policymakers, utility companies, and researchers, emphasizing the importance of embracing innovative technologies to address the energy challenges faced by developing nations like Uganda.

Evaluating economic feasibility of liquid air energy storage systems in US and European markets

Liquid air energy storage is a clean and scalable long-duration energy storage technology capable of delivering multiple gigawatt-hours of storage. The inherent locatability of this technology unlocks nearly universal siting opportunities for grid-scale storage, which were previously unavailable with traditional technologies such as pumped hydro energy storage and compressed air energy storage. While the technical viability of liquid air energy storage has been established, its economic viability has not yet been rigorously assessed across diverse electricity markets. In this work, a mixed-integer linear program was used to conduct a high-level economic analysis of this technology in US and European markets. By simultaneously optimizing the design and operation of liquid air energy storage systems to maximize their net present value, a yes/no indication of their economic viability can be obtained. Using this approach, the economic viability of this technology was established for the West load zone of Texas through identification of a system with an optimal net present value of $10.4 million and a levelized cost of storage of $119/MWh. Sensitivity analyses were also performed to elucidate how improved technical performance and economic incentives might affect the economic feasibility and, consequently, the adoption of this technology more broadly in the future.

Effect of geothermal heat transfer on performance of the adiabatic compressed air energy storage systems with the salt cavern gas storage

The temperature and pressure of compressed air influence the output performance of the adiabatic compressed air energy storage system with salt cavern gas storage. However, current studies often overlook the impact of geothermal heat transfer on system performance. In this paper, a mathematical model of the energy storage system with salt cavern gas storage is developed, considering the geothermal heat transfer in wellbore and salt carven. The model is used to illustrate the effects of geothermal heat transfer on the system performance through a sensitivity analysis of some key parameters. The results show that geothermal heat transfer will significantly reduce the gas and energy storage capacity, with a maximum reduction of 15.3 % and 11.1 % at a depth of 2000 m, respectively. Furthermore, this reduction would increase with the depth of the salt cavern. The geothermal energy reduces the round-trip efficiency from 68.7 % to 66.26 % and the energy storage density from 5.18 kWh·m−3 to 4.32 kWh·m−3. For common salt cavern gas storage at a depth of about 600 m ∼ 1200 m, the negative impacts of geothermal heat transfer on round-trip efficiency and energy storage density are less than 2.2 % and 0.5 kWh·m−3, respectively. Sensitivity analysis also demonstrates that the primary factors influencing the system’s performance are the thermal conductivity of the surrounding rock, the volume of the salt cavern, and the length of the wellbores. The secondary factors include the heat transfer coefficient on the inner wall of the salt cavern, the inner pipe radius, and the roughness of the inner pipe. Irrelevant factors include the thermal conductivity of the protective fluid and the thermal conductivity of the cement layer. The proposed comprehensive mathematical model of the system and simulation findings aid in forecasting the performance of storage systems and offer theoretical guidance for the design of large-scale compressed air energy storage systems.

Stochastic fast charging scheduling of battery electric buses with energy storage systems design

Under the background of urban green and low-carbon economic development, battery electric buses (BEBs) together with fast charging technologies are considered as an effective way in promoting carbon emissions reduction and improving energy efficiency. However, challenges arise during daily peak periods, at which BEB charging activities cause increased operation costs and substantial stress on the power grid. To fill the gaps, this work introduces energy storage systems (ESSs) into the BEB fast-charging scheduling problem. A stochastic programming model considering uncertain discharge efficiencies of ESSs is established, aiming to minimize total operation costs of fast charging stations. It is then transformed into a deterministic one with a manageable scenario sample size, using an integrated sample average approximation (SAA) and K-means approach, as well as an integrated SAA and K-means++ approach. We also present a heuristic algorithm and an improved genetic algorithm to solve the transformed model. Numerical studies based on a real-life urban transit network in Shanghai, China, are conducted to demonstrate the effectiveness of the proposed methods. Sensitivity analysis further reveals the impact of charging power on total operation cost.

Design, analysis, and testing of a prototype-scale latent heat thermal energy storage (LTES) system

A parametric analysis was performed to design a prototype-scale latent heat thermal energy storage (LTES) system using commercial grade hexahydrate calcium chloride as phase change material (PCM) in a staggered tube array configuration, placed horizontally. The study involved numerical analysis of melting and solidification processes of the PCM within the tube array, considering transient two-dimensional Navier-Stokes equations and a Realizable k-ɛ turbulence model to predict fluid flow and heat transfer. The enthalpy-porosity technique was used to model PCM melting and solidification. The study included a comparison between numerically predicted and experimentally obtained air temperatures at the tube array outlet, as well as PCM temperatures. This comparison revealed an excellent level of agreement. Additionally, the study compared the pressure drop within the array and the average peripheral heat transfer coefficient calculated from the numerical results to the well-established Zukauskas correlation. The resulting design successfully met the predefined performance criteria: achieving a cooling effect of 4 °C for a four-hour duration while maintaining a pressure drop of <100 Pa. The proposed prototype-scale tube array design can be efficiently cooled down and PCM frozen overnight. The energy storage density of the system falls within the range of 22 to 27 kWh/m3.

Optimizing superconducting magnetic bearings of HTS flywheel systems based on 3D H-[formula omitted] formulation

The superconducting flywheel system exploiting the magnetic coupling between the bulk high temperature superconductors (HTSs) and permanent magnets (PMs) exhibits excellent performance of self-stable levitation, and is promising for power applications. In this paper, we use the H-ϕ formulation combined with moving mesh to establish a full 3D model for the thrust type and journal type bearings in the HTS flywheel system. We then proposed different Halbach schemes to enhance the magnetic flux density of the rotor and thus the coupling, and investigated the levitation force, relaxation characteristics, electromagnetic transient distribution, and temperature characteristics of the bearings. Results show that, under axial zero field-cooled (ZFC) condition, the optimized PM rotor scheme can significantly improve the maximum levitation force and stiffness by 4.0 and 2.3 times respectively for the thrust type bearing, and the maximum levitation force of journal type bearing can be improved by a factor of 5.5. Under the radial ZFC condition, the maximum levitation force and stiffness of the journal type bearing have been increased by 4.9 times and 2.9 times. For the relaxation of both bearings during operation, the optimized PM rotors lead to relatively greater attenuation of levitation force. The proximity of the optimized PM rotors intensifies the magnetic flux movement of the HTS bulks but only brings about a limited temperature rise, and the superconductors still maintain a good low-temperature working environment. This study provides an effective methodology for analyzing the HTS bearing systems and good references for the optimal design of compact HTS flywheel energy storage systems (FESSs).

Investigations of Mechanical Properties and Maximum Load Capacities under Abuse Testing of Lithium Ion Battery

Lithium-ion batteries are crucial in driving the various technological applications’ innovation. In this regard, it is important to ensure the safety and reliability of LIBs as a matter of public concern. This paper explores the mechanical properties and abuse testing’s maximum load capacities, highlighting the testing’s abusive conditions. The focus is to deepen a broader understanding of the extreme mechanical load and the battery behavior responses. Based on the extensive abusive testing procedures, drop, impact, crush or penetration, and thermal runaway tests, the mechanical responses and failure modes of LIBs at the cell, module, and pack levels were analyzed. This work, is based on the drop and impact test, using solidworks simulation to establish the abuse testing impacts on battery mechanical and electrical properties. The model for conducting the tests was the 18650 lithium ion cell battery, subjected to the front, side and rear impact at various heights and velocities with reference safety standard AIS 156 and AIS 048. Mechanical properties and maximum load capacities’ correlation was described, illuminating the crucial failure mechanisms and feasible mitigation approaches. The derived insights from the abuse testing have a significance to the design, safety and reliability of the LIB-based energy storage systems to guide the future research and development in safer and robust battery use.

Optimal design and operation of battery energy storage systems in renewable power plants to reach maximum total electric sale revenues

Optimal design and operation of battery energy storage systems in renewable power plants to reach maximum total electric sale revenues

Machine Learning-Driven Energy Harvesting and Storage System Design for IoT Applications in Smart Buildings.

The advent of Internet of Things (IoT) technologies has revolutionized the concept of smart buildings, integrating diverse sensors and devices for enhanced automation and efficiency. Avinash B. Raut, Energy harvesting and storage technologies have emerged as promising solutions to address the power requirements of IoT devices in smart buildings. This abstract provides a comprehensive overview of machine learning-driven energy harvesting and storage system design tailored specifically for IoT applications in smart buildings. Traditional energy management systems often face challenges such as suboptimal energy utilization, limited scalability, and lack of adaptability to dynamic environmental conditions. In contrast, machine learning techniques offer adaptive, data-driven solutions to optimize energy harvesting, storage, and distribution in smart buildings. [1] This paper explores various machine learning algorithms, including supervised learning, reinforcement learning, and deep learning, and their application in optimizing energy harvesting from ambient sources such as solar, kinetic, and thermal energy. Moreover, machine learning enables predictive energy demand modeling by analyzing historical data and environmental factors, thus enhancing the efficiency of energy storage and distribution systems. Real-world case studies and experimental results are presented to demonstrate the effectiveness and potential of machine learning-driven energy management systems in improving energy efficiency, reliability, and autonomy in IoT-enabled smart buildings DOI: https://doi.org/10.52783/tjjpt.v45.i02.6318

Performance of a rotating latent heat thermal energy storage unit with heat transfer from different surfaces

Due to the rapid growth in the demand for fast and efficient latent heat thermal energy storage system, multiple heat transfer enhancement techniques have been proposed and widely investigated. Actively or passively, rotation of the energy storage unit affects the internal natural convection and the heat transfer performance. Hence, this study is conducted to evaluate the potential of the heat transfer enhancement by rotation. A two-dimensional model with the phase change material filled in the annular space of a double tube energy storage unit is developed. The numerical simulations for the heat transfer through the internal or external surfaces of the annular space are respectively conducted. Both solidification and melting of the phase change material are taken into account by the enthalpy-porosity method. According to different heat transfer surfaces and the occurrence of solidification or melting, four arrangements are formed. It is found that the rotation of the thermal energy storage unit can reduce the solidification time as high as 46 % compared to its still counterpart, but variation of the rotation speed only produces a minor influence. The effect of rotation on the melting depends on the heat transfer direction (or surface). When the heat is transferred into the solid phase change material through the internal wall, the melting time can be gradually reduced as the rotation speed increases with a maximum of 69 %. However, when the heat is transferred through the external wall, influence of the rotation results in adverse effect. This study can guide the design of innovative high performance latent heat thermal energy storage system containing rotation unit.

Enhancement in supercapacitive performance of calcium phosphate nanoneedles through Ni-ion incorporation: Insights into atomic and electronic structures

Phosphates have emerged as promising materials for electrochemical charge storage applications. However, a comprehensive understanding of their storage mechanism remains elusive. The present work reports the impact of nickel ion doping on hydroxyapatite (PH) prepared by a wet-chemical route. X-ray diffraction (XRD) reveals the formation of NiO and Raman and Fourier-transform infrared (FTIR) analyses demonstrate the suppression of phosphate modes on doping. The incorporation of Ni ions induces nanoneedles morphology, effectively engineering the surface area and volume of the material. Notably, specific capacitance significantly improves to 415.35 Fg-1 at 10 mAg−1 for Ni-incorporated PH (0.1 M of Ni) (0.1NH), a remarkable enhancement compared to 166.77 Fg-1 at 10 mAg−1 for pristine (PH). X-ray photoelectron spectroscopy (XPS) confirms the mixed valence state of Ni2+/3+ and the presence of Ni–O bonding on Ni incorporation. Moreover, X-ray absorption spectroscopy (XAS) reveals the formation of Ni–O–Ni clusters, enhanced charge transfer by Ni ions, and optimized coordination of Ni–O bonds. The comprehensive correlation established between the phase, morphology, and electronic and local atomic structures elucidates the excellent electrochemical charge storage performance. This investigation sheds light on the potential of Ni-doped hydroxyapatite as a promising material for electrochemical applications, contributing valuable insights for the design and optimization of advanced energy storage systems.

High-performance asymmetric pseudocapacitor based on NiCo2O4@MnO2 cathode and N-doped graphene anode

The design and fabrication of scalable energy storage systems are crucial for portable applications, demanding advanced materials through large-scale and facile processing. In this report, an asymmetric solid-state pseudocapacitor (ASPC) is assembled using a core–shell NiCo2O4@MnO2 composite and solution-processed nitrogen-doped graphene (N-GP) as the positive and negative electrodes, respectively. With interconnected porous structures, the electrodes based on N-GP and NiCo2O4@MnO2 offer high specific capacitance and rate capability, broad operation voltage, and a binder-free design with no added conductive agent. With these benefits, the ASPC with a PVA-KOH electrolyte exhibits a specific capacitance of 103.3F g−1 at 1 A/g and exceptional energy density of 36.73 Wh kg−1 at a power density of 792 W kg−1. In addition, the specific capacitance retention reaches 90.9 % after 5000 cycles, indicating excellent cycling stability. In the broader context of solid-state supercapacitors based on solution-processed graphene, this ASPC exhibits highly competitive performance, establishing it as a promising candidate for advanced energy storage.

A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics

Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling.

A methodical approach for the design of thermal energy storage systems in buildings: An eight‐step methodology

AbstractRecent research focuses on optimal design of thermal energy storage (TES) systems for various plants and processes, using advanced optimization techniques. There is a wide range of TES technologies for diverse thermal applications, each with unique technical and economic characteristics. Matching an application with the most suitable TES system remains challenging. This study proposes an eight‐step design methodology guiding the process from describing the thermal process to defining the most appropriate TES based on constraints and requirements. The steps include specifying the thermal process, system design parameters, storage characteristics, integration parameters, key performance indicators, optimization method, tools, and design robustness. Seven already‐designed TES systems are evaluated to assess the methodology's effectiveness, where the design procedures have been adapted to the proposed steps. Case studies involve various applications with both sensible and latent TES systems, demonstrating the applicability of the proposed design procedure. A significant diversity exists among the design cases regarding the design objective, input, design, and output parameters. Nevertheless, the design procedure in each case can be deconstructed into the outlined design steps. The last design step has been excluded from all case studies due to insufficient information regarding the robustness of the design process. The paper demonstrates how a methodical approach can be applied to examine the TES design and the integration. The design steps proposed in this study can serve as a foundation for developing a more systematic approach for designing TES systems in future works, resulting in simplifying the design process.

Urban Rail System Modeling and Simulation Based on Dynamic Train Density

To further improve the simulation calculation ability of urban rail traction systems during the peak operation period and provide an accurate and reliable simulation tool for the subsequent train schedule and energy storage system design, a multi-train circuit model with a bilateral power supply was established in this paper, and a power calculation algorithm based on dynamic train density was designed. The circuit topology in the model can be dynamically adjusted according to the number of trains to improve the operation rate. Based on the spatial and electrical data of a real section of the subway, the urban rail circuit model was built on the MATLAB platform, and the actual operation data of the subway was imported for verification. The experimental results show that the multi-train model can accurately reflect the influence of voltage fluctuations on the traction system under different train running conditions, and the results fit the actual operation conditions. By comparing the influence of different train intervals on the RBE (regenerative braking energy) utilization, the results show that the optimal RBE utilization rate can be achieved by adjusting the train interval in the peak period.

Optimal energy storage system design for addressing uncertainty issues in integration of supply and demand-side management approaches

The primary goal of Sustainable Development Goal 7 (SDG 7) is to increase renewable energy use to reduce reliance on fossil fuels and mitigate climate change. Energy-intensive industries can benefit from in-house renewable power generation, reducing their reliance on fossil fuel-based grid power and making processes greener. However, integration among power generation/purchase, energy storage systems (ESS), and power consumption is crucial to overcome the intermittent nature of renewable power sources. ESS plays a vital role in increasing resilience and optimizing material production based on power availability and pricing. The efficacy of ESS needs critical evaluation considering cost, efficiency, and uncertainties related to renewable power generation and material product demand. The paper proposes two scenario-based optimization approaches to assess the impact of uncertainties on the integrated supply and demand side management (ISDM) system, focusing on lithium-ion batteries and cryogenic energy storage (CES). Compared to the conservative approach of the scenario-based robust optimization (SRO) method, the proposed stochastic simulation optimization (SSO) method provides a ‘risk-neutral’ solution, which is 6.45% less than the minimum expected cost solution. The analysis also suggests that lithium-ion batteries are more economically effective for the proposed integrated framework than CES, resulting in almost a 29% reduction in operating costs compared to no battery option. The proposed framework could contribute to sustainable and economically viable energy management practices in energy-intensive industries. Further research and implementation of such frameworks could accelerate the adoption of renewable energy and energy storage technologies in industrial processes.

High energy density biomass-derived activated carbon materials for sustainable energy storage

Biomass-derived activated carbons are promising materials for sustainable energy storage systems such as aqueous supercapacitors and Zn-ion capacitors due to their abundance, low cost, tunable porosity, and heteroatom-rich structures. Herein, we report biomass derived carbon materials fabrication via a two-step activation method. The activated carbons possess well-tuned pore structures and high heteroatom content, resulting in remarkable surface area, ultrahigh micropore volume, and good wettability. The symmetrical supercapacitors and aqueous Zn-ion capacitors were assembled using the produced activated carbons. The 2PA-6-800 supercapacitor delivers an ultrahigh rate capability (up to 10,000 mV s−1) and promising cycle life, retaining 66.3 % of its initial performance after 33,000 galvanostatic charge–discharge cycles. The assembled 2PA-6-800-based ZIC delivers a superior specific capacitance of 785.0 F g−1, a maximum energy density of 352.5 Wh kg−1, and a remarkable power density of 60.3 kW kg−1. The outstanding performance is attributed to the two-step activation method, high heteroatom content, and the structural integrity of the biomass derived AC to the ZIC device architecture. This work contributes to the design and development of high-performance, safe, and sustainable energy storage systems.

Thermodynamic design of the novel energy storage system based on liquid carbon dioxide for a 17 MW concentrated solar thermal power plant

Currently, the majority of electric power generation in Iran is provided through fossil fuel power plants. Recently, the effort to increase the share of renewable energy in the energy supply portfolio is increasing. Naturally, none of the renewable energy plants in this country are currently equipped with an energy storage system. The first concentrated solar thermal power plant of this country is currently under construction with a nominal capacity of 17 MW, but it has not yet been put into operation. Therefore, in this research, the thermodynamic design of the energy storage system based on liquid carbon dioxide is presented to be coupled with it. The results of the research showed that the energy generated per unit of storage volume of the designed system is equal to 17.6 kWh and the thermal, exergy and round-trip efficiencies of the system are equal to 36.14 %, 61.51 % and 40.63 %, respectively. Also, the effect of turbine and pump efficiencies on the output of the designed energy storage system was investigated and it was shown that with the increase in turbine efficiency, the effectiveness of the system enhances; while increasing the pump efficiency does not have much effect on the performance of the system.