Performance Of Energy Storage System Research Articles

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.

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.

Benchmarking core temperature forecasting for lithium-ion battery using typical recurrent neural networks

One of the factors hindering the scaled application of lithium-ion batteries is their thermal safety issues. Real-time monitoring of core temperature holds promise in alleviating this concern. However, calibrating individual models for thousands of battery cells poses challenges and leads to big data issues. The maturing and rapidly advancing artificial intelligence technologies hold promise in addressing the task of core temperature prediction. In this study, we conduct a benchmark test using typical recurrent neural networks for core temperature prediction. The test encompasses three main aspects: data acquisition, data processing, and data analysis. Firstly, we conduct dynamic battery tests across a wide temperature range on a custom-built experimental platform. Subsequently, we employ big data techniques for data pre-processing, including filtering, normalization, and sliding window methods. Next, hyperparameter optimization is then performed using the Bayesian optimization with Tree-structured Parzen Estimator, followed by K-fold cross validation. Consequently, we obtain three optimal performing models based on typical recurrent neural networks. Finally, we extensively compare these models in terms of performance and complexity. According to the results, all data-driven methods achieve satisfactory model performance, with RMSE within 0.17 °C and MAE within 0.15 °C. Among the models with similar predictive performance conditions, gated recurrent unit exhibits the lowest model complexity and faster training speed. Meanwhile, we also validate the portability and generalizability of the forecasting models. This study lays the foundation for future research in this area and can provide guidance for engineering practical applications to ensure the safe operation of battery energy storage systems.

Lithium‐Free Redox Flow Batteries: Challenges and Future Prospective for Safe and Efficient Energy Storage

AbstractConsidering the costs, waste, and impact on the environment of current energy consumption, accurate, cost‐effective, and safely deployed energy storage systems are required. Lithium (Li)‐free redox flow batteries (RFBs) are a feasible solution. RFBs can store enormous amounts of energy effectively and are increasingly used for large‐scale applications. The use of RFBs has significantly enhanced the performance of energy storage systems and effectively reduced the costs and wastage of energy storage operations. Vanadium‐based RFBs are an emerging energy‐storage technology being explored for large‐scale deployment owing to their numerous benefits, including zero cross‐contamination, scalability, flexibility, extended life cycle, and nontoxic working state. This study describes the fundamental operating principles of redox flow battery‐based systems as well as the design considerations and constraints placed on each component. It discusses recent progress in the design and deployment of RFBs for energy‐related applications and the remaining obstacles and prospects. Finally, this study highlights the enormous potential of RFBs and suggests some solutions to scale up the use of RFBs in the near future.

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

Breakeven analysis of Thermal Energy Storage System under uncertain operating conditions

Since thermal energy storage (TES) systems gained momentum in the global energy market, there is a greater demand to enhance their energy efficiency and, more significantly, lower their costs. The purpose of the study is to assist decision-making under uncertainty and risk associated with stochastic increases in cost connected with Thermal Energy Storage System operation. The Thermal Energy Storage System (TSS) at the Gas District Cooling Plant in Perak, Malaysia is the purpose of this research. When fluctuating cooling demands develop, the Thermal Energy Storage System (TSS) is used to transfer energy use from peak to off-peak hours. For TSS and Gas District Cooling (GDC) systems, electric chillers and thermal storage tanks are required. Thermal energy systems (TES) play a role in the ongoing process of increasing integration across diverse energy systems to achieve a cleaner, more flexible, and long-term use of energy resources. Break-even analysis was used in the study, which is a basic tool for analyzing the feasibility of real-world initiatives with uncertainty. The findings reveal that a single-shift operation is not feasible, and the project will require at least two shifts to be viable.

Research on interval optimization of power system considering shared energy storage and demand response

Considering the low utilization rate of energy storage system under uncertainty of source-load and the coarse demand response mechanism, an interval optimization model of power systems based on shared energy storage and refined demand response is proposed. The dual-side uncertainty of source-load is expressed by interval numbers, and the refined demand response mechanism and shared energy storage optimization model for different building load are analyzed. Then, the source-grid-load-storage interval optimization model with shared energy storage is solved and analyzed. In order to improve the accuracy of the optimization results, sensitivity analysis is performed on the linearized segmentation number of the objective function under dual-side uncertainty of source-load. Finally, by comparing the simulation with the traditional centralized energy storage system, the results show that the dual-side uncertainty of source-load can be effectively handled using interval optimization. Considering shared energy storage and demand response, it can effectively improve the energy storage utilization rate and system operation economy, and realize the source-grid-load-storage synergistic interaction.

Application of machine learning in ultrasonic diagnostics for prismatic lithium-ion battery degradation evaluation

Lithium-ion batteries are essential for electrochemical energy storage, yet they undergo progressive aging during operational lifespan. Consequently, precise estimation of their state of health (SOH) is crucial for effective and safe operation of energy storage systems. This paper investigates the viability of ultrasound-based methods for assessing the SOH of prismatic lithium-ion batteries. In the experimental framework, a designated prismatic lithium-ion battery was subjected to numerous charging and discharging cycles using a battery cycling system. Subsequently, ultrasonic detection experiments were conducted to record the waveforms of the transmitted and received signals. These signals were then processed through wavelet transforms to extract signal amplitude and time-of-flight data. To analyse these data, we applied four algorithms: linear regression, support vector machines, Gaussian process regression, and neural networks. The predictive performance of each algorithm was evaluated through extensive experimentation and analysis. The combination of ultrasonic signals with computational models has emerged as a robust technique for precise battery degradation assessment, suggesting its potential as a standard in battery health evaluation methods.

Heat activated VO2 (R) cathodes for high-performance aqueous zinc ion batteries

We introduce a novel approach for design and production of vanadium dioxide (VO2) cathodes activated by heat, specifically tailored for use in aqueous zinc-ion batteries (ZIBs). Our cathodes exhibit distinctive metal-insulator transition (MIT) characterized by the shift between monoclinic (M) and tetragonal rutile (R) phases. The employment of an aqueous electrolyte in ZIBs enables successful operation of VO2 cathodes, showcasing phase transition and enhanced electrochemical performance above a readily attainable temperature of approximately 68 °C. The MIT feature enhances the charge transfer properties at electrode-electrolyte interfaces, resulting in notable improvements in specific capacity and energy densities. Fabricated VO2(M) cathodes underwent heat-triggered transformation to VO2(R), resulting in a remarkable increase (400 %) in capacity. At 0.1 A g−1, VO2(R) cathodes show specific capacity of 354.6 mAh g−1, yielding an impressive energy density of 461.0 Wh kg−1 for aqueous ZIBs. This exceptional performance can be attributed to abrupt change in electrical conductivity upon thermal activation of MIT. This work underscores pivotal role of thermally activated MIT in finely tuning electrochemical performance of energy storage systems, prospecting a promising avenue for further advancements.

A battery internal short circuit fault diagnosis method based on incremental capacity curves

The safe operation of battery energy storage systems (BESSs) has become one of the research priorities in this industry. And it is usually threated by various faults caused by design flaws, environmental conditions, and operating conditions et al. Among these faults, the internal short circuit (ISC) faults pose a significant threat to the safety of BESSs. Relevant studies focus on ISC fault diagnosis itself and ignore the impact of battery aging within the pack on fault diagnosis. To solve this problem, this paper proposes an ISC fault diagnosis method based on incremental capacity (IC) curves. And a qualitative differentiation between ISC batteries and aging ones is first achieved by leveraging the characteristic variations of IC curves. Then, an equivalent circuit model is constructed for ISC batteries. Further, a joint estimation of ISC resistance and SOC of the faulty battery is performed by combining Extended Kalman Filtering (EKF) and Forgetting Factor Recursive Least Squares (FFRLS). Finally, an experimental platform is established to verify the proposed method. Results show the proposed method can effectively differentiate between ISC batteries and aging batteries. Moreover, the estimation errors of SOC are less than 0.26% and the estimation accuracy of ISC resistance is more than 99.42%.

Optimal Operation of Residential Battery Energy Storage Systems under COVID-19 Load Changes

Over the past few years as COVID-19 was declared a worldwide pandemic that resulted in load changes and an increase in residential loads, utilities have faced increasing challenges in maintaining load balance. Because out-of-home activities were limited, daily residential electricity consumption increased by about 12–30% with variable peak hours. In addition, battery energy storage systems (BESSs) became more affordable, and thus higher storage system adoption rates were witnessed. This variation created uncertainties for electric grid operators. The objective of this research is to study the optimal operation of residential battery storage systems to maximize utility benefits. This is accomplished by formulating an objective function to minimize distribution and generation losses, generation fuel prices, market fuel prices, generation at peak time, and battery operation cost and to maximize battery capacity. A mixed-integer linear programming (MILP) method has been developed and implemented for these purposes. A residential utility circuit has been selected for a case study. The circuit includes 315 buses and 100 battery energy storage systems without the connection of other distributed energy resources (DERs), e.g., photovoltaic and wind. Assuming that the batteries are charging overnight, the results show that energy costs can be reduced by 10% and losses can decrease by 17% by optimally operating batteries to support increased load demand.

Finite control set model predictive control of three-port converter for interfacing a PV–battery energy storage system to a three-phase stand-alone AC system

Abstract This paper proposes a multiport bidirectional non-isolated converter topology that provides advantages in terms of simultaneous multiple operations, single-stage conversion, high power density and reduced power losses due to the lower number of switches. The proposed multiport converter uses a centralized non-linear controller known as a finite control set model predictive controller to manage the flow of power between different ports. It deals with the parallel operation of photovoltaic and battery energy storage systems for stand-alone alternating current (AC) systems. The converter connects the lower voltage battery to the photovoltaic port using a bidirectional buck/boost converter and the photovoltaic port is linked to the stand-alone AC load through a three-phase full-bridge inverter. Each leg of the three-phase converter will act as a bidirectional direct current (DC)/DC converter as well as an inverter simultaneously. Only six switches manage the power transfer between all the connected ports of photovoltaic–battery energy storage system linked to the stand-alone AC load. The proposed multiport converter is mathematically modelled and controlled by a finite control set model predictive controller. The system is validated in simulation (1-kW rating) and experimental environment (200-W rating). The hardware prototype is developed in the laboratory and the controller is implemented on the field-programmable gate array board. Two independent case studies are carried out to validate the efficacy of the system. The first scenario is for a change in solar irradiance, while the second scenario is for a change in the output load.

Adiabatic Compressed Air Energy Storage system performance with application-oriented designed axial-flow compressor

Medium and long-duration energy storage systems are expected to play a critical role in the transition towards electrical grids powered by renewable energy sources. ACAES is a promising solution, capable of handling power and energy ratings over hundreds of MW and MWh, respectively. One challenge with ACAES is achieving the required highly efficient operation in the compressor over the range of conditions encountered in the system as the pressure in the air store changes. In this paper, an application-oriented axial-flow compressor is designed, aiming towards efficient operation throughout the operation range, whilst also associating the performance prediction to a practical compressor geometry. A two-step design methodology based on inviscid, axisymmetric flow conditions has been implemented, leading to the flowtrack, blade-row geometries and the compressor performance map. The compressor model is integrated into an ACAES model, including two compression spools, two expansion stages with preheat, a constant volume high pressure storage operating between 5.5 and 7.7 MPa and two separate Thermal Energy Storage units. While the existing ACAES literature either ignores the transient off-design operation or uses generic numerical correlations (which are not associated to a particular geometry), the key novelty of this paper is the application of a detailed design method for turbomachinery to ACAES. The results indicate that the designed compressor requires 33 stages over the two spools, and is able to operate efficiently over the storage pressure range, showing that if the application-oriented design procedure is applied to the compressor, it does not stop ACAES reaching 70% round-trip efficiency, outputting 35MW for approximately 15 h. Importantly, the specific ACAES requirement of conserving heat at higher temperatures has been fulfilled by decreasing the number of intercoolers. Finally, it is recommended that a similar level of scrutiny is applied to the other components (i.e. expanders, heat exchangers and TES units), keeping in mind the unique set of operational requirements of ACAES. This work is an important step towards removing the common misconception that off-the-shelf components can be easily be used in typical ACAES designs.

Battery Energy Storage System Performance in Providing Various Electricity Market Services

The Battery Energy Storage System (BESS) is one of the possible solutions to overcoming the non-programmability associated with these energy sources. The capabilities of BESSs to store a consistent amount of energy and to behave as a load by releasing it ensures an essential source of flexibility to the power system. Nevertheless, BESSs have some drawbacks that pose limitations to their utilization. Indeed, effectively managing the stored and released energy is crucial, considering the degradation of performance associated with these systems over time. The substantial capital expenditure (CAPEX) required to install these systems represents a current constraint, impeding their broader adoption. This work evaluates a techno-economic analysis of a 2MW/2MWh BESS providing multiple services, namely participating in capacity and balance markets. The analysis is based on a BESS model implemented in SIMULINK, adopting online data gathered from a Lithium Iron Phosphate (LFP) battery facility. The model evaluates the auxiliary power consumption, state-of-charge (SoC), state of health (SoH), and the round-trip efficiency (RTE) of the overall system. The analysis is based on three price profiles: 2019 (Business-As-Usual), 2020 (COVID-19), and 2022 (Gas Crisis). Furthermore, this work conducts a case study to analyze the behavior of the BESS. It entails a sensitivity analysis, specifically evaluating the influence of CAPEX and upward bid price on the economic viability of the project. The results show a strong relation between the CAPEX variation and the Internal Rate of Return (IRR) of the project.

Multipurpose Optimization Method for Energy Storage System Specification Using Measurement Data of DC Traction Substations

The peak demand for railway power occurs when trains operate at full capacity, which calls for the need of facilities that can handle such peaks. These expansive railway power facilities, which cover vast areas, result in increased maintenance and management costs while affecting the power supply to traction substations (TSs). Herein, we investigated the load leveling of TSs using energy storage systems (ESSs). Most of the studies that have been conducted on the use of ESSs in electric railways have focused on high‐speed railways and urban lines. The application of ESSs to local lines is expected to be highly effective in reducing the capacity of power facilities using the load leveling of TSs. However, the introduction of ESSs to such local lines has not yet been discussed in detail. Hence, we focused on the relationship between ESS specifications (battery capacity, kWh) and load peak reduction (kW) and proposed a method to determine the ESS specifications by simultaneous optimization using linear programming. Numerical calculations were performed based on measured data from a real line, and the proposed method was verified in terms of battery capacity, load peak reduction, and ESS operation ratio. The numerical results indicate that the maximum load of the TS can be reduced by 98.7% and 91.1% using 380 kWh and 65.42 kWh ESS, respectively, considering its cooling time. This highlights the potential of Energy Storage Systems (ESSs) to balance TS (Time‐of‐Use) loads and consequently reduce power facility requirements. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Recent Progress in the Application of Ion Beam Technology in the Modification and Fabrication of Nanostructured Energy Materials.

The development of green, renewable energy conversion and storage systems is an urgent task to address the energy crisis and environmental issues in the future. To achieve high performance, stable, and safe operation of energy conversion and storage systems, energy materials need to be modified and fabricated through rationalization. Among various modification and fabrication strategies, ion beam technology has been widely used to introduce various defects/dopants into energy materials and fabricate various nanostructures, where the structure, composition, and property of prefabricated materials can be further accurately tailored to achieve better performance. In this paper, we review the recent progress in the application of ion beam technology in material modification and fabrication, focusing on nanostructured energy materials for energy conversion and storage including photo- (electro-) water splitting, batteries (solar cells, fuel cells, and metal-ion batteries), supercapacitors, thermoelectrics, and hydrogen storage. This review first provides a brief basic overview of ion beam technology and describes the classification and technological advantages of ion beam technology in the modification and fabrication of materials. Then, modification of energy materials by ion beams is reviewed mainly concerning doping and defect introduction. Fabrication of energy materials is also discussed mainly in terms of heterojunctions, nanoparticles, nanocavities, and other nanostructures. In particular, we emphasize the advantages of ion beam technology in improving the performance of energy materials. Finally, we point out our understanding of challenges and future perspectives in applying ion beam technology for the modification and fabrication of energy materials.

Energy storage system coordinated with phase-shifting transformer and dynamic rating equipment for optimal operation of wind-rich smart power networks

Emergence of flexibility devices into smart power systems can assist the power system operators in making effective and economical decisions for the power system scheduling. These devices include energy storage system (ESS), phase-shifting transformer (PST), dynamic transformer rating (DTR), and dynamic line rating (DLR). In this paper, an approach is proposed for optimal day-ahead scheduling of power system using coordinated operation of ESS, PST, DTR, and DLR units under high wind power penetration situation. The aim is to minimize operational, load shedding, and wind power curtailment costs by regarding all the problem constraints. All of the model's relations as well as AC power flow equations have been convexified in order to construct a mixed-integer quadratically-constrained programming (MIQCP) model whose solution will obtain global optimum results. The developed model is implemented in GAMS and applied to the IEEE 24-bus system under various case studies, and the obtained results are investigated thoroughly. By coordinated operation of the employed flexibility devices, the loading of transformers and lines reach 159 % and 246 % compared to their nominal rating without violation of temperature limitations. Through releasing the capacity of lines and transformers, the total scheduling cost is reduced by about 52 % compared to traditional operation. Also, the whole energy of wind farms is utilized without any curtailment or constraints violation.

Novel hierarchical core-shell NiCo2S4@NiWO4 nanowire arrays for high performance pseudocapacitor

In this paper, a novel NiCo2S4@NiWO4 honeycomb core-shell nanorod array significantly improved the electrochemical storage properties of supercapacitors. The characteristic architecture of the NiCo2S4@NiWO4 electrode showed a remarkable specific capacity of 1273.5 C g−1 at a current density of 1 A g−1. The asymmetric supercapacitor (ACS) had a high specific capacitance of 1502.4 C g−1 at a current density of 1 A g−1 as well as excellent cycling stability at a current density of 10 A g−1 (101.25 % cycling retention rate after 10,000 cycles), and an energy density of 334 Wh kg−1 with a power density of 2.38 kW kg−1. The research emphasized that a strong electronic coupling effect was observed at the interface of NiCo2S4 and NiWO4 and electron transfer from Ni-W-O to Ni-Co-S occurred around the interface, which further improved the electron transfer properties of electrode materials and hit the mark extends the life of the electrode and significantly improves the performance of energy storage systems.

Development of a simulation model for controlling energy storage systems on electric trains

The article discusses the experience of using electrical energy storage devices on multiple unit rolling stock, both in Russia and abroad. It is noted that the use of energy storage systems in railway transport in Russia lags somewhat behind its foreign colleagues. The main assumptions taken when performing simulation modeling of the operation of energy storage systems on an electric train are presented. Simulations of the operation of energy storage systems were carried out using the MatLab software package. Simulation models of an electric train with an energy storage device, a model of a heater for heating an electric train car, a model of a hybrid energy storage system, a model of a supercapacitor unit, and batteries are presented. The algorithm of operation of the active topology of a hybrid drive is considered. The developed simulation models have been tested. Graphs of the voltage at the pantograph and the current consumed by the electric train were obtained, as well as graphs of the operation of the battery and supercapacitor in charge/discharge modes.

Market Operation of Energy Storage System in Smart Grid: A Review

Market Operation of Energy Storage System in Smart Grid: A Review