Design Of Energy Storage System Research Articles

An improved system design method for cell-based energy storage systems: A combination of a constraint satisfaction problem with an extended Ragone plot

Over the past three decades, advancements in electrical energy storage technologies have substantially enhanced performance and reduced costs, rendering them viable for diverse applications. Cell-based energy storage systems, such as batteries and supercapacitors, are particularly notable for their adaptability and scalable interconnection capabilities. However, the design process for such systems lacks uniform standards, presenting a gap in research for the conceptual system design. This paper introduces an improved system design method (SDM), addressing critical limitations of existing approaches: (a) Shifting from current-based to power-based requirements; (b) Enabling flexible adaptation of operational design points beyond fixed datasheet specifications; (c) Ensuring harmonization of energy storage design with other system components, notably power electronics. Central to our SDM is the novel “extended Ragone plot” (ERP), which maps cell-specific energy–power relations and allows for flexible adjustment of operational limits. Since it can be determined experimentally, the subsequent design method is model-free and can efficiently be executed for different cell types. In the SDM, this ERP is coupled with a constraint satisfaction problem (CSP) to compute a comprehensive design solution space that accommodates all application-specific requirements and component interrelations. It is implemented in a holistic, interactive tool, facilitating mathematical optimization and enabling visual analysis of energy storage system designs. As a proof-of-concept, two case studies on the design of lithium-ion battery systems for stationary grid supply demonstrate the method’s superiority over conventional approaches. While in the first case, an optimal design can be determined immediately, in the second case, there are no valid design solutions based on the cell’s initial operating point. Utilizing the ERP, a practical design solution is achieved by reducing the maximum cell power by approximately 9%, thereby modifying the cell’s available voltage and current limits. This approach provides a possible solution for scenarios where a system design based on data sheets alone is not feasible. A comparative analysis and graphical synthesis of both case studies validate this hypothesis, illustrating the novel system design method’s ability to precalculate and evaluate diverse design scenarios in advance. The source code and data used in this study are openly accessible, offering the SDM as an interactive tool for analyzing and designing energy storage systems.

The pitfall in designing future electrical power systems without considering energy return on investment in planning

Measuring the limitations of transitioning to 100% renewable energy systems partly equates with the capability of quantifying associated net-energy production. To foresee future possibilities, an advanced systemwide energy-return-on-investment (EROI) model was constructed. Driven by the thorough consideration of the existing weaknesses of EROI, the model integrates the concept of energy statistics, life cycle assessment techniques and energy system modelling in order to overcome the underlying primary energy quality issues, boundary condition problems and energy system design related uncertainties that compromises comparability of EROI during the energy transition. This model was applied to the Israeli power system that presents a unique opportunity to examine the vulnerability of depending on limited resources and the connection of EROI to system design. This study demonstrates the dependency of EROI on the energy transition path, system design requirements expressed via interactive linkages of curtailment, variable renewable energy penetration and storage system design, and resource diversity. Achieving a very high variable renewable energy penetration by 2050 with solar photovoltaics carries a risk of falling below an EROI value of 10, such vulnerability can be largely minimised by well-conducted manoeuvring of the system design and resource diversification. Despite the large uncertainty of this study, the need for a multicriteria designing technique is out of the question.

Variable time constant filtering control for MMC-HESS considering wind power fluctuations

Wind power exhibits frequent fluctuations, which can impact the stability of the power grid. The impact of wind power on grid stability can be mitigated by using energy storage system whose battery lifespan can be extended by control strategies. This study focuses on the design of a modular multilevel hybrid energy storage system that incorporates both battery packs and supercapacitors. This system aims to effectively smooth the fluctuation of wind power and prolong the lifespan of battery packs. The power distribution between the battery packs and the supercapacitors is achieved through a first-order low-pass filter. The low-frequency power variations are smoothed using the battery packs, while the time constant of the filter is adjusted based on changes in the state of charge of battery packs. This approach prevents overcharging and overdischarging of the battery packs, thereby increasing their lifespan. The remaining high-frequency power variations are handled using the supercapacitors. Simulation results and a comparison with fixed time constant low-pass filtering demonstrate that the grid-connected power meets national standards following the implementation of the energy storage configuration. The proposed control strategy effectively allocates high- and low-frequency energy storage power, resulting in an 18.34% increase in battery pack lifespan.

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.

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.

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.

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.