Electrical Energy Storage Research Articles

Advanced Electrode Materials for Low‐Temperature Na Storage

AbstractSodium‐ion batteries have drawn worldwide attention as ideal candidates for the upcoming generation of large‐scale electrical energy storage devices due to the low cost and abundance of sodium. Due to its unique electrochemical and chemical properties, sodium‐ion batteries hold the promise of breaking geographical and environmental constraints, achieving efficient sodium storage under low‐temperature conditions. However, low‐temperature sodium‐ion batteries, especially for their electrode materials, still face numerous challenges, such as the sluggish electrochemical reaction kinetics, poor material stability, significant volume changes leading to the pulverization of materials and the rapid degradation of battery performance. Here, it is focused on the modification methods for electrode materials, the research progress on cathode and anode materials of low‐temperature sodium‐ion batteries is summarized systematically and the other components of the electrodes are discussed briefly, and the shortcomings of the current research and possible future research directions are discussed thoroughly.

Statistical Analysis of Levelized Round Trip Cost of Grid Scale Electrical Energy Storage in Batteries with Different Chemistries

Electrical energy storage is a crucial component of the clean energy transition for integrating high share of renewable electricity generators into the supply mix. In this study, the round-trip costs of grid scale electrochemical energy storage from 2 up to 24 hours for peak power ratings of 1 MW and 10 MW in lithium-ion LFP, lithium-ion NMC, Pb-acid and vanadium redox flow batteries are compared using their currently projected techno-economic characteristics for year 2030. A statistical approach is used for estimating mean costs and quantifying the uncertainties in these estimates, since many of the techno-commercial features are still evolving and are uncertain. Using Monte Carlo simulations to derive the input parameters, the levelized round trip cost of energy storage is found to vary from Rs 10.73±0.77/kWh(e) to Rs 15.96±1.12/kWh(e) for Li-LFP batteries, Rs 14±1/kWh(e) to Rs 20.3±1.46/kWh(e) for Li-NMC batteries, Rs 19.1±1.39/kWh(e) to Rs 58.3±4.28/kWh(e) for Pb-acid batteries and Rs 18.7±1.37/kWh(e) to Rs 44.2±3.19/kWh(e) for Vanadium redox flow batteries, for the range of input values considered in this study. Through sensitivity analysis and calculation of correlation coefficients, the battery installed capital cost and useful lifetime are found to be the two most influential parameters affecting life cycle electrical energy storage cost.

Sensitivity Analysis of a Nuclear Hybrid Energy System With Thermal Energy Storage in Deregulated Electricity Markets Considering Time Series Uncertainty in Electricity Price

ABSTRACTAdding thermal energy storage to nuclear power plants has been proposed as a way to allow nuclear plants to operate more flexibly and potentially be more competitive in deregulated electricity markets. The economics of these systems in deregulated markets are subject to uncertainties in capital costs, operating costs, and revenue. This study quantifies the uncertainty in the net present value of a nuclear power plant with integrated thermal energy storage in three U.S. deregulated electricity markets considering these sources of uncertainty and quantifies, for the first time, the relative contributions each source makes to the overall uncertainty. To accomplish this, a computationally efficient block bootstrap method is introduced to quantify uncertainty contributions from the stochastic time series of electricity prices, achieving a two order of magnitude decrease computational time compared to the model‐based methods used in previous works while also relaxing several strict assumptions made by the model‐based approach. Up to 18.5% of the overall variance in net present value is attributable to variance in the electricity price stochastic process, with this sensitivity varying significantly across markets.

Optimization of emergency frequency control strategy for power systems considering both source and load uncertainties

With the increasing integration of renewable energy sources and the presence of numerous controllable loads such as electric vehicles and energy storage in the modern power system, higher nonlinearities and uncertainty both sources and loads are introduced. These factors pose challenges in achieving fast and accurate emergency frequency control. Therefore, this paper addresses the issue of dual source-load uncertainties in power system and presents an optimization strategy based on the Soft Actor Critic (SAC) algorithm that involves the participation of controllable loads in emergency frequency control. Firstly, the spatio-temporal uncertainties of wind farm power output on power supply side and power demand on the load side are described using Weibull and normal probability distributions, respectively. Secondly, an improved Markov Decision Process (MDP) model for emergency frequency control is established, which considers the characteristics of the dual source-load uncertainties. Finally, an optimization of the SAC algorithm is conducted based on Deep Reinforcement Learning (DRL), aiming to achieve rapid system frequency recovery and minimize the cost of removing controllable loads. The presented approach in the paper enhances the emergency frequency control strategy for uncertain power systems and effectively addresses the issue of source-load uncertainty compounded by fault power shortages.

Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data

This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage.

Optimal dimensioning of renewable energy generation and storage systems

With high energy demand and large available area, agricultural farms offer significant potential for renewable energy investments, like photovoltaic systems and electrical energy storages. However, the profitability of such investments depends strongly on self-consumption, so accurate planning requires computation-intensive simulation and optimization considering local consumption. This study presents a novel methodology for the optimal dimensioning and configuration of photovoltaic systems and electrical energy storages using efficient techniques from continuous non-linear optimization. Combining physical and economic models with measured consumption data, an investment’s net present value over 20 years is maximized. Using gradient-based solver WORHP, the simulation, optimal control, and optimal dimensioning of the local energy system are calculated simultaneously, allowing for efficient computation over an entire year of hourly data to capture both daily and seasonal variations.The approach is demonstrated with simple use cases, including an exemplary day of a dairy farm’s consumption, for which optimal systems with and without storage achieve 77% and 43% of autarky, respectively. Saturation effects of optimal plant size can be observed when sizes are large enough for optimal self-consumption but not expanded further for grid export. With energy storage, this saturation is reached at higher values. Optimizing photovoltaic plants with different orientations to match the specific consumption patterns characteristic of the dairy farm achieves similar autarky as a single plant while reducing investment costs by more than 20%. While thorough validation and comparison against heuristic methods predominantly used in the field is part of ongoing research, the presented use cases demonstrate the flexibility and efficiency of the proposed method and highlight its promise as a planning tool in the agricultural domain and beyond.

Achieving the dendrite-free zinc anode by constructing a Sn-C interfacial layer with zincophilic and conductive network

Under the basic national policy of sustainable green development, high-performance and low-cost energy storage devices are urgently needed for intermittent generation of clean energy and storage of electrical energy. Zn metal with high capacity, abundant reserves and excellent plasticity, but the inevitable side reactions (hydrogen precipitation and corrosion, etc.) of the Zn metal anode itself have severely limited the development of aqueous Zn ion batteries (AZIBs) in the market. In this study, an artificial protective layer of polymer-derived carbon material combined with Sn is prepared to inhibit hydrogen precipitation and corrosion and prolong the lifetime of AZIBs. With these advantages, the Zn@Sn@N doped carbon (Zn@CNS) symmetric cell could achieve a stable cycle life of 1000 h at 2 mA cm-2 and low overpotential. The excellent stripping life of over 400 h is achieved in half-cells assembled with Cu foils. Furthermore, the α-MnO2//Zn@CNS full battery shows superior stability in rate capability and long-term capacity retention compared to the α-MnO2//Zn battery, indicating that the CNS coating provides excellent performance and practical value.

Optimal Configuration for Shared Electric-hydrogen Energy Storage for Multiple Integrated Energy Systems With Mobile Hydrogen Transportation

The flexible operation and storage of hydrogen and electric energy provide an effective path for the development of low-carbon energy and transportation systems. This paper introduces a configuration method for electric-hydrogen shared energy storage supporting the multiple energy and capacity demands of integrated energy systems (IESs). A Stackelberg game-based shared energy framework with gaseous hydrogen transportation by transportation network for shared energy storage operators (SESO) and IESs is established. In this framework, the electric power and electric storage sharing are accomplished by power lines, while the shared hydrogen is achieved by the transportation of mobile trucks. The target of the energy storage operator is to maximize its trading benefits with IESs and lower the life-cycle cost of the whole system, while the IESs aim to meet their energy demand and lower the operation cost via energy storage sharing. To solve this planning model, a two-level sine cosine based grey wolf optimizer (GWO-SCA)-bisectional method is provided to realize the system configuration, mobile hydrogen planning, and economic trading of the shared energy operator. A case study with 3 IESs, real-world geographic roads, and environmental conditions is carried out to verify the effectiveness of the method and the life-cycle configuration and operation economy of the shared energy storage. The comparisons show that the proposed energy storage sharing frame can achieve a higher energy utilization ratio of 92% and the proposed method can solve the two-level problem more efficiently, the calculation time is reduced by 80.2%.

Enhanced electricity generation and energy storage in a microbial fuel cell with a bimetallic-modified capacitive anode

Microbial fuel cells (MFCs) are energy conversion devices that utilize microorganisms attached to the electrode as catalysts for the oxidation of organic waste, thereby generating electricity. In this study, a two-step hydrothermal method was employed to prepare a CF/NiO/Fe3O4 capacitive composite anode by directly growing NiO on a carbon felt substrate as a metal framework to support the in-situ growth of Fe3O4. In this paper, electrochemical tests such as cyclic voltammetry and AC impedance were used to investigate the electrochemical performance of the modified anode. The two electrodes were characterized by SEM, EDS, XRD, FTIR, BET, TEM and SAED test. The MFCs with the CF/NiO/Fe3O4 anode exhibited significant improvements in generation of power and storage of energy performance, reaching a maximum power density of 9.29 W/m3, which has increased by 1.54-fold compared to CF/NiO anode. After charging/discharging for 60 min, the CF/NiO/Fe3O4 anode had a sum charge of 8532.07C /m2, which was a significant increase of 1868.82C/m2 compared to the CF/NiO anode. High-throughput sequencing analysis suggested that the proportion of electricity-generating microorganisms on the CF/NiO/Fe3O4 surface of the anode reached 86.03 %, which was higher than that on CF/NiO anode surface. The protein contents of the CF/NiO/Fe3O4 reached 71.03 mg/cm3. The application of capacitive materials in MFCs would allow the constructed MFCs to generate and store bioelectricity simultaneously.

Expansion force signal based rapid detection of early thermal runaway for pouch batteries

With the extensive application of lithium-ion batteries in electric vehicles and energy storage stations, thermal safety issues have increasingly become the focus of public attention. To alleviate the safety concerns, a qualified battery management system (BMS) must be able to detect and warn of thermal abuse before it develops into a thermal runaway (TR). For the first time, the expansion force characteristics of the pouch battery module during the early heating stage of TR under typical working conditions are analyzed, which proves that the expansion force signal can reflect the internal abnormal heating earlier than the terminal voltage and the surface temperature, and has better noise resistance. Furthermore, the electromechanical coupling model (EmCM) is improved for more accurate open-loop expansion force estimation. On this basis, an expansion force based early TR rapid detection framework is proposed with less 3 min expend in tests, in which the Kalman filter ensures the stability and convergence/divergence of the observer estimation error, and the reliability of fault judgment is ensured by the adaptive threshold designed by the model structure.

Fast and Smart State Characterization of Large-Format Lithium-Ion Batteries via Phased-Array Ultrasonic Sensing Technology.

Lithium-ion batteries (LIBs) are widely used in electric vehicles and energy storage systems, making accurate state transition monitoring a key research topic. This paper presents a characterization method for large-format LIBs based on phased-array ultrasonic technology (PAUT). A finite element model of a large-format aluminum shell lithium-ion battery is developed on the basis of ultrasonic wave propagation in multilayer porous media. Simulations and comparative analyses of phased array ultrasonic imaging are conducted for various operating conditions and abnormal gas generation. A 40 Ah ternary lithium battery (NCMB) is tested at a 0.5C charge-discharge rate, with the state of charge (SOC) and ultrasonic data extracted. The relationship between ultrasonic signals and phased array images is established through simulation and experimental comparisons. To estimate the SOC, a fully connected neural network (FCNN) model is designed and trained, achieving an error of less than 4%. Additionally, phased array imaging, which is conducted every 5 s during overcharging and overdischarging, reveals that gas bubbles form at 0.9 V and increase significantly at 0.2 V. This research provides a new method for battery state characterization.

Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

Preparation, Characterization and Conductivity of SiO2 doped rGO-WO3 Composite for Battery Application

High-performance solid polymer electrolytes are regarded as one of the most promising materials to be used in the development of lithium ion batteries with improved extended life, increased recyclability and increased safety for electric vehicles and energy storage. In this work, a microwave-assisted hydrothermal technique was used to produce SiO2-doped rGO-WO3 composite, which possesses outstanding supercapacitor capabilities. This method is simple, low-cost method without using any other capping and reducing agents. The obtained SiO2 doped rGO-WO3 composite structural, morphology and components have been examined by XRD, FT-IR, FE-SEM, TEM, EDX, UV-visible techniques. It was found that the SiO2 doped rGO-WO3 composite well crystalline and the size range is about 20-30 nm. The SEM morphology results showed that the nanoparticles were stabilized by rGO, having cavity like structure and randomly distributed on the rGO-WO3 matrix. The solid state electrolyte revealed superior lithium ion transference number and cyclic stability. The SiO2 doped rGO-WO3 composite shows improved ionic conductivity of 2.74 × 10-5 S cm-1 at room temperature.

Ball-Milling-Assisted N/O Codoping for Enhanced Sodium Storage Performance of Coconut-Shell-Derived Hard Carbon Anodes in Sodium-Ion Batteries.

Sodium-ion batteries (SIBs) are regarded as cost-effective alternatives or competitors to lithium-ion batteries for large-scale electric energy storage applications. However, their development has been hindered by the high cost of hard carbon (HC) anodes and poor electrochemical performance. To enhance the sodium storage capacity and rate performance of HC, this study accelerated the electrochemical performance of coconut-shell-derived HC anodes for SIBs through N/O codoping using ball milling and pyrolysis. Experimental results demonstrate that the simultaneous introduction of N and O generates a synergistic effect, increasing the surface oxygen-containing functional groups, defects, and interlayer spacing of coconut-shell-derived HC through the codoping of light elements. The excellent strategy has increased the slope capacity and platform capacity of HC, and the synergistic modification of N/O has increased its reversible specific capacity from 272 to 343 mA h g-1 (30 mA g-1), with a retention rate of approximately 92.1% after 100 cycles. In addition, it also exhibits an excellent rate performance, reaching 178 mA h g-1 at 1500 mA g-1. In summary, this study presents an effective strategy for modifying biomass-derived HC.

Optimal management in island microgrids using D-FACTS devices with large-scale two-population algorithm

Amidst the increasing complexity of microgrid optimization, characterized by numerous decision variables and intricate non-linear relationships, there is a pressing need for highly efficient algorithms. This study introduces a tailored Mixed Integer Nonlinear Programming (MINLP) model that optimizes the charging and discharging schedules of electric vehicles (EVs) and energy storage systems (ESS) while incorporating Distributed Flexible AC Transmission System (D-FACTS) devices. To address these challenges, a novel approach based on the Large-Scale Two-Population Algorithm (LSTPA) is proposed. The model's effectiveness was evaluated using a 33-node microgrid, where the proposed method achieved a total purchased energy of 1.2 MWh, a voltage deviation of 0.0357 p.u, and a CPU time of 551 s, outperforming traditional methods like NSGA-II, PSO, and JAYA. Additionally, in a 69-node microgrid, the approach resulted in a total purchased energy of 0.3 MWh and a voltage deviation of 0.0078 p.u. These results demonstrate the superior performance of the proposed method in terms of energy efficiency, voltage stability, and computational time, advancing the efficiency of microgrid management.

Economic evaluation of kinetic energy storage systems as key technology of reliable power grids.

In recent years, energy-storage systems have become increasingly important, particularly in the context of increasing efforts to mitigate the impacts of climate change associated with the use of conventional energy sources. Renewable energy sources are an environmentally friendly source of energy, but by their very nature, they are not able to supply the required amount of energy in a uniform distribution. This study evaluated the economic efficiency of short-term electrical energy storage technology based on the principle of high-speed flywheel mechanism using vacuum with the help of an innovative approach based on life cycle cost analysis (LCC). The innovative potential of high-speed flywheel energy storage systems (FESS) can be seen in increasing the reliability of the electricity transmission system with the possibility of providing control power to compensate for residual loads caused by volatile renewable power sources and power sinks. Based on the research conducted, the LCC method was selected in this study as the most appropriate method to evaluate the economic efficiency of a high-speed FESS used to compensate for short-term fluctuations in an upgraded electric transmission system. As a result, the adjusted LCC per MWh values were compared with the average intra-hour margin realisable in the Intra-Day OTE Market, while the margin calculation also considered the efficiency of the inertial storage. Under the modelled technical and economic conditions, it was found that a high-speed FESS project that can compensate for short-term fluctuations in the electricity transmission system can be economically efficient in the Czech Republic.

Bifunctional Fluorocarbon Electrode Additive Lowers the Salt Dependence of Aqueous Electrolytes.

The solid electrolyte interphase (SEI) plays a crucial role in extending the life of aqueous batteries. The traditional anion-derived SEI formation in aqueous electrolytes highly depends on high-concentrated organic fluorinating salts, resulting in low forming efficiency and long-term consumption. In response, this study proposes a bifunctional fluorocarbon electrode additive (BFEA) that enables electrochemical pre-reduction instead of TFSI anion to form the LiF-rich SEI and in situ produce conductive graphite inside the anode before the lithiation. The BFEA lowers the salt dependence of aqueous electrolytes, enabling the inorganic LiCl electrolyte to work first, but also successfully achieves a high SEI formation efficiency in the relatively low 10m LiTFSI without mass transfer concerns, suppressing the parasitic hydrogen evolution from 11.24 to 4.35nmol min-1. Besides, BFEA strengthens the intrinsic superiority of Li storage reaction by lowering battery polarization resulting from the in situ production of graphite, promoting charge transfer of electrode kinetics. Compared with the control group, the demonstrated Ah-level pouch cell employing BFEA exhibits better cycle stability above 300 cycles with higher capacity retention of 78.2% and the lower decay of the round-trip efficiency (△RTE = 2%), benefiting for maintaining the high efficiency and reducing heat accumulation in large-scale electric energy storage.

An investigation of the electrochemical characteristics of NiO-Co3O4/graphite/PVDF composites for the development of a supercapacitor with ultrahigh stability

Abstract Supercapacitor is a groundbreaking electrical energy storage technology that falls between batteries and dielectric capacitors which has undergone significant progress in recent years. Among the several elements influencing a supercapacitor's capacitance, the choice of electrode materials plays a crucial role. Nanomaterials formed from transition metal oxides with incorporated 3-D graphite are said to possess high capacitance, conductivity, increased area of active site, distinct redox properties, and several valence shells, making them an appropriate material for electrode synthesis. Therefore, in this study, three composites of NiO and Co3O4 are prepared in the ratio 2:8, 3:7 and 4:6 using facile sol-gel method. To the prepared composites, graphite and PVDF are added in equal quantities. The resultant samples are characterized using XRD, SEM, FTIR and UV-Vis spectroscopy. The samples are further integrated on an FTO electrode and subjected to CV, GCD and EIS for electrochemical study. The highest specific capacitance is obtained for NiO and Co3O4 composite in the ratio 3:7 and is equal to 156.66F/g at a sweep rate of 10 mV/s. This material is further used for a two-electrode study to check the feasibility of a symmetric solid-state device, which demonstrated a specific capacitance of 36F/g with 100% capacitive retention.

Enhancing Energy Density in Aqueous Ammonium-Ion Supercapacitors via CuCo2S4@MoS2 Core@Shell Heterostructure Design.

Aqueous ammonium-ion supercapacitors (AASCs) are recognized for their rapid charge-discharge capability, long cycle life, and excellent power density. However, they still confront the challenges of low energy density. To address the above issue, this work proposes a novel strategy involving the establishment of CuCo2S4@MoS2 core@shell heterostructures to enhance the capacity of electrode material. The double electric layer energy storage mechanism of the MoS2 shell facilitates the storage and provision of a substantial ammonium source for NH4 + insertion into CuCo2S4, thereby enhancing the electrochemical performance of AASCs. The density functional theory (DFT) calculations demonstrate that the CuCo2S4@MoS2 core@shell heterostructures exhibit better affinity for NH4 + and improved conductivity. Furthermore, the internal electric field at the heterojunction accelerates NH4 + transfer, thereby enhancing the pseudocapacitive behavior of CuCo2S4. Owing to the abundant active sites and pronounced pseudo-capacitance, the CuCo2S4@MoS2 electrode achieves a specific capacity of 2045 C g-1 at 1 A g-1. With activated carbon (AC) as the negative electrode, the fabricated CuCo2S4@MoS2//AC AASC device attains a specific capacity of 591 C g-1 and an energy density of 83.23Wh kg-1. This work presents a promising new strategy for the next generation of AASCs.

Optimizing Energy Yield and Economic Benefit of Renewable Energy Technologies for Urban Mediterranean Dwellings

Mediterranean European cities are characterized by high population density and limited space for large-scale implementation of renewable energy installations. This paper addresses the optimization of renewable energy installations in Mediterranean dwellings with the scope of increasing their energy contribution and cost-effectiveness. In a case study for Malta, the three technologies studied were solar photovoltaics, solar water heating, and heat pump water heating. Technical and economic analyses were performed on a number of reference configurations using Polysun software (version 2022.8). Sensitivity analyses were also conducted to study the impact that different technical and economic factors have on the performance of the configurations considered. Finally, comparisons were made between the techno-economic results obtained from the reference and sensitivity analyses. Based on data collected, the presence of renewable energy source (RES) technologies in the residential sector of Malta was characterized and correlated with the types of dwellings considered. Among the results obtained, it was found that although a solar RES installation may experience some shading, this does not mean that it is rendered economically unfeasible. Moreover, from the simulations conducted, electrical energy storage technology was considered as too premature unless strongly subsidized, making economic sense only in specific circumstances. On the other hand, although heat pump water heating technology is also relatively modern, it was concluded to be the most beneficial in terms of both energy yield and economic benefit, generally speaking. Furthermore, it was determined that in a higher occupancy dwelling, solar water heating (SWH) and heat pump water heating (HPWH) result in considerably more attractive energy savings.