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Energy storage is one of the key technologies for energy conservation and therefore is of great practical importance. One of its main advantages is that it is best suited for solar thermal applications. This study deals with a comprehensive discussion of the evaluation and the selection of sensible and latent heat storage technologies, systems and applications in the field of solar energy. Several issues relating to energy storage are examined from the current perspective. In addition, some criteria, techniques, recommendations, checklists on the selection, implementation and operation of energy storage systems are provided for the use of energy engineers, scientists and policy makers. Copyright © 1999 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system's feasibility. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system's modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery's efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation. [ABSTRACT FROM AUTHOR]

Summary: At present, pumped hydro energy storage plays the dominant role in electrical energy storage. However, its development is clearly restricted by the topography and adverse impacts on local residents. Underground pumped hydro energy storage (UPHES) using abandoned mine pits not only can effectively remedy these drawbacks but is also constructive to the management of abandoned mine pits. In this paper, we firstly conduct a comprehensive analysis of conventional pumped hydro energy storage (CPHES) and UPHES, using life cycle sustainability assessment (LCSA). Sustainability indicators in this paper include economic indicators, environmental indicators, and social indicators. Among all the indicators, blue water footprint (BWF) and ecological footprint (EF) are included for the first time to assess the social performance of CPHES and UPHES. Then, this paper employs multi‐attribute value theory (MAVT) and scenario analysis to evaluate the overall performance of energy storages. The results show that CPHES has better performance in economy and environment than UPHES because of the economies of scale, while the UPHES has better performance in social sustainability impact because of the absence of stages of excavation and backfilling. When using MAVT methodology, only when the weight for social indicator is three times higher than that of economy and environment; ie, the weight for social dimension is 0.6, and the weights for environmental and social dimension are 0.2; the score of UPHES is higher than CPHES. [ABSTRACT FROM AUTHOR]

Sustainable development of renewable energy sources is one of the most important themes that humanity faces in this century. Wide use of renewable energy sources will require a drastically increased ability to store electrical energy. Electrochemical energy storage devices are expected to play a key role. With the increased demand in flexible energy resource for wearable electronic devices, great efforts have been devoted to developing high-quality flexible electrodes for advanced energy storage and conversion systems. Because of its high specific surface area, good chemical stability, high mechanical flexibility, and outstanding electrical properties, graphene, a special allotrope of carbon with two-dimensional mono-layered network of sp2 hybridized carbon, have been showing great potential in next-generation energy conversion and storage devices. This review presents the latest advances on the flexible graphene-based materials for the most vigorous electrochemical energy storage devices, that is, supercapacitors and lithium-ion batteries. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

As a result of market liberalisation, a large number of electricity retailers have emerged in the electricity market. Acting as the intermediaries between the electricity producers and the customers, the electricity retailers aim to balance the supply and demand and shoulder substantial risks generated from both sides. Due to the randomness of the electricity load, it is difficult for electricity retailers to make an accurate electricity purchasing plan in advance to meet customer demand. This deviation leads to a proportion of spot electricity purchases that require a higher purchase cost. As a result, one of the most serious concerns facing electricity retailers is how to improve their balancing abilities and reduce power purchase deviation. In contrast to previous research, which has generally recommended that electricity retailers invest in energy storage systems or develop optimised purchasing strategies, this paper proposes a new strategy for the electricity retailers, which is renting external flexible resources to solve the market uncertainty of the electricity retailers, thereby lowering purchase costs and increasing profits. The proposed business model makes use of the cloud energy storage to solve the supply-demand imbalance issue of electricity retailers. The cost calculation model and decision optimisation model have been established in the process of renting cloud energy storage. Charging and discharging cloud energy storage have been separately rented to deal with different positive and negative load deviations, which can simplify the optimisation model. As an experimental paper, the proposed model has been tested in the PJM power market in the United States and the New South Wales power market in Australia. The findings confirm that renting the cloud energy storage capacities can significantly reduce costs and maximise profits for the electricity retailers when compared to the situation without the cloud energy storage. The biggest saving can reach 24.5% in the PJM market. With the rapid fall of battery prices, the advantage of the proposed strategy will be more obvious. [ABSTRACT FROM AUTHOR]

Summary: In the modern world, only conventional energy resources cannot fulfil the growing energy demand. Electricity is a fundamental building block of a technological revolution. Today, most of the electricity demand is met by the burning of fossil fuels but at the cost of adverse environmental impact. In order to bridge the gap between electricity demand and supply, nonconventional and eco‐friendly means of energy generation are considered. Renewable energy systems (RESs) offer an adequate solution to mitigate the challenges originated due to greenhouse gasses (GHG). However, they have an unpredictable power generation with specific site requirements. Grid integration of RESs may lead to new challenges related to power quality, reliability, power system stability, harmonics, subsynchronous oscillations (SSOs), power quality, and reactive power compensation. The integration with energy storage systems (ESSs) can reduce these complexities that arise due to the intermittent nature of RESs. In this paper, a comprehensive review of renewable energy sources has been presented. Application of ESSs in RESs and their development phase has been discussed. Role of ESSs in increasing lifetime, efficiency, and energy density of power system having RESs has been reviewed. Moreover, different techniques to solve the critical issues like low efficiency, harmonics, and inertia reduction in photovoltaic (PV) systems have been presented. Unlike most of the available review papers, this article also investigates the impact of FACTS technology in RESs‐based power system using multitype flexible AC transmission system (FACTS) controllers. Three simulation models have been developed in MATLAB/Simulink. The results show that FACTS devices help to maintain the stability of RESs integrated power system. This review paper is believed to be of potential benefit for researchers from both the industry and academia to develop better understanding of challenges and solution techniques for REs‐based power systems and future research dimensions in this area. [ABSTRACT FROM AUTHOR]

Summary: Energy storage systems have been in the spotlight for the past decade as they offer tangible solutions to the ever‐growing pollution problem faced by the planet. These storage systems, primarily lithium‐ion based, power most of the mobile devices and electric vehicles (EVs). Substantial efforts are being made to electrify every mode of transportation to combat climate change. Accurate state‐of‐charge (SOC) and state‐of‐health (SOH) assessment of lithium‐ion batteries play an important role for determining the available range in EVs. Research in this area of capacity estimation demands extensive curated battery parameter data such as voltage, current, and temperature. Performing repeated experiments to collect these data is expensive and tedious. Also, lack of diversity in open access datasets has limited research in this area. This paper introduces a generative adversarial network (GAN)‐based approach for data augmentation. This technique enables expansion of sparse datasets in‐turn enhancing the learning capability of Neural Networks used for SOC/SOH estimation. A time series GAN is used in the present work to produce synthetic data. This technique was evaluated on two publicly available battery parameter datasets to test its effectiveness. A Kullback‐Leibler Divergence value of 0.2317 and 1.0572 was obtained for the battery dataset obtained from NASA prognostics repository and Oxford battery degradation dataset, respectively. The contributions of the paper include: (a) synthetic time‐series data augmentation of battery parameters, (b) high‐fidelity diverse data generation of battery profile data such as voltage, current, temperature, and SOC. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an optimal parameter identification strategy of the lithium‐ion (Li‐ion) battery model applying a recent metaheuristic artificial ecosystem‐based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root‐mean‐square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show. [ABSTRACT FROM AUTHOR]

Summary: The collection of high quality battery datasets is critical for applications such as State‐of‐Charge or State‐of‐Health estimations. Missing features within the dataset is a challenging problem. The issue is further exacerbated in on‐field electric vehicle applications. One method to resolve this challenge is to impute missing battery parameter values with best possible estimates. In this paper, we explore a self‐attention technique for imputing missing battery parameters. A masked self‐attention block enables accurate estimates of missing battery data. The introduced technique helps provide potential solutions to resolve the issue of missingness in battery parameters. We provide two primary contributions, (a) we illustrate the effectiveness of self‐attention mechanism to provide best estimates of missing battery parameters, (b) we highlight the efficacy of the approach over available state‐of‐the‐art imputation models. We release the code used in this scenario in the results section of our paper. This will allow researchers to effectively reproduce the results presented here.Self‐attention based Imputation Technique [ABSTRACT FROM AUTHOR]

Summary: Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium‐ion battery (LIB) and a supercapacitor (SC)‐based HESS (LIB‐SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal‐sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system‐level optimizations, include component sizing (CS) and power‐energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB‐SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB‐SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB‐SC HESS are provided to inspire researchers for further studies. Highlights: Lithium‐ion battery (LIB) and supercapacitor (SC)‐based hybrid energy storage system (LIB‐SC HESS) suitable for EV applications is analyzed comprehensively.LIB‐SC HESS configurations and suitable power electronics converter topologies with their comparison are provided.System‐level optimization of LIB‐SC HESS and generalized steps involved in implementing the optimization algorithm for component sizing (CS) and power‐energy management strategy (PEMS) are discussed.A rigorous study on CS and PEMS is presented to develop efficient LIB‐SC HESS.Current challenges and future recommendations for the development of LIB‐SC HESS for EV applications are provided. [ABSTRACT FROM AUTHOR]

Summary: This paper aims at specifying the optimal allocation of vanadium redox flow battery (VRB) energy storage systems (ESS) for active distribution networks (ADNs). Correspondingly, the appropriate operation strategy and the rated capacity and rated power of VRB ESS allocation are obtained. The dynamic characteristics of the external working performance of VRB are analyzed in this paper to accurately describe the operation condition of VRB. Then, A variety of operation strategies considering multiple scenarios are proposed to constrain the working state of VRB ESS. In addition, the comprehensive operation benefits are described quantitatively, to determine the optimal allocation of ESS by comparing the operation benefits. Finally, the proposed optimal allocation method was validated and tested by experimental field data of ADNs, and the optimal allocations under different operation strategies are compared, selecting the appropriate operation strategy can improve the benefits by 95%, also the consumption and the operation benefits will increase by 230% and 190% when double the capacity that can be learned from influence analysis. [ABSTRACT FROM AUTHOR]

Summary: The installation of an energy storage system (ESS) is vital for the Micrgorid (MG) islanded operation to ensure the maintenance of demand–supply balance. Lithium‐ion batteries (LIBs) are among the most commonly used ESS technologies for grid‐based applications, which is used in this paper to constantly maintain the demand–supply balance in a residential MG. For this purpose, a novel frequency‐based energy management scheme is proposed. It uses an LIB ESS to handle the primary frequency control and energy management during peak‐load period, while the dispatchable distributed generators like microturbine and fuel cell supply the base load. The air‐conditioning TCLs consume a significant part of the residential load profile, especially during mid‐summer. Unlike TCLs that instantaneously use their generated cooling thermal energy to control indoor temperatures, the thermal energy storage systems (TESSs) can also store the thermal energy and use it later. In this paper, the TESSs are used instead of TCLs to reduce the power consumption of a residential Microgrid (MG) in islanded mode. By doing this, the required LIBESSs capacity is decreased 70% considerably than the case the TCLs are used for controlling the indoor temperatures. Also, replacing the ACs and EHPs with TESSs results in a 63.7% reduction in the total cost of electrical and thermal storage. [ABSTRACT FROM AUTHOR]

This paper proposes and analyses several configurations for hybridising concentrating solar power (CSP) plants with combined cycle gas turbines (CCGT). The objective is to increase the solar contribution to a large extent, much higher than those obtained in integrated solar combined cycles but maintaining synergies, which are usually lost when increasing the solar share. For that, two thermal energy management systems are introduced at different temperature levels. First, a configuration with only the low-temperature system is proposed. Then, an enhanced configuration with the low- and high-temperature systems is conceived. These configurations are compared to reference CSP and CCGT state-of-the-art plants. The analyses include different strategies of operation and two sizes for the thermal energy storage system. The results show that the first proposed configuration introduces some synergies but cannot improve the performance of the reference CSP and CCGT working separately, due to an issue with the solar dumping on days with high solar irradiation. The enhanced configuration overcomes this problem and maintains the synergies, leading to an improvement from both the thermodynamic and economic points of view, increasing the solar contribution and decreasing the levelized cost of energy over the reference plants. [ABSTRACT FROM AUTHOR]

Summary: Fossil fuel depletion and its adverse impact on global warming is a major driving force for a recent upsurge in the development of hybrid electric vehicles technologies. This paper is a conglomeration of the recent literature in the usages of an energy storage system and power conversion topologies in electric vehicles (EVs). An EV requires sources that have high power and energy density to decrease the charging time. Commonly used energy storage devices in EVs are fuel cells, batteries, ultracapacitors, flywheel, and photovoltaic arrays. The power output from energy storage sources is conditioned to match load characteristics with the source for maximum power delivery. A DC‐DC converter topology performs this task by way of transforming voltage under the condition of power invariance. In addition, power electronics is also required to power DC/AC motors efficiently with precise control as these motors provide tractive efforts and acts as prime movers. This paper therefore brings out a critical review of the literature on EV's power conversion topologies and energy storage systems with challenges, opportunities and future directions by systematic classification of EVs and energy storage. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, power consumptions are increasing due to the modern behaviour of technology. To accomplish the power demand more power generation is needed. The usage of fossil fuel causes many health problems and environmental pollutions so the researchers notice the renewable energy resources (RER) to generate electricity. RER consume large land areas to produce a low amount of power. In this paper, floating photovoltaic (FPV) is used to produce the maximum amount of power to compensate the load demand. Normally, a battery is used for a storage device in the solar PV system. In this paper, the energy storage device is used for compressed air energy storage system which stores electricity in the form of compressed high‐pressure air. Power quality issues like swell, sag, harmonics, and interruption, as well as disturbance are arisen at the time of linking non‐linear loads in the power system. A controller is placed in the system to mitigate these power quality issues. Here, unified power quality conditioner (UPQC) is used to reduce the above power quality issues. UPQC injects the real power into the transmission line, so the unbalance voltage and current are compensated. Such that the inject voltage and current are controlled with the help of the series part and shunt part, respectively. The mitigation of UPQC is enhancing with the support of a proportional‐integral (PI) controller. The PI controller produces two conditions of the signal according to the need for power; one is a proportional signal, the other one is proportional to the integral of the signal. The PI controller optimal gain value is appropriately selected based on the black widow Optimization algorithm. The proposed algorithm carries off the load variation and avoid disturbance in the floating PV system. The projected method is designed and executed in the MATLAB/Simulink software. The proposed FPV with ACESS design is verified with the load conditions of 180 e3 V, 50 Hz. The proposed method efficiency is validated and contrast with the prevailing methods such as Cuckoo Search Optimization Algorithm, Whale Optimization Algorithm, Particle Swarm Algorithm, and Gravitational Search Algorithm that validate the innovative method is more efficiently reducing the power quality issues. [ABSTRACT FROM AUTHOR]

Summary: Series‐connected lithium battery packs are widely adopted in industries such as electrical vehicles and large‐scale energy storage systems. It is necessary to configure an equalization system for them to reduce the inconsistency of single cells, to ensure the battery pack cycle capacity. Although many novel active converters have been proposed for equalization, there still lacks systematic analyses on variables, topologies, and modular methods. Therefore, this paper provides a systematic review on the above aspects. Initially, an overall summary is present on current variables for equalization control. Second, the dominant structures are divided into cell‐to‐cell, cell‐to‐pack, and pack‐to‐pack topologies, and their characteristics are pointed out for comparison. And then, the modular methods, including single‐layer‐based, multi‐layer‐based, and hybrid integrations are evaluated via graph theory. Some key issues and potential research directions are given, which are expected to offer assistance on the selection of appropriate variables and topologies for equalization systems in the future. [ABSTRACT FROM AUTHOR]

Summary: With an increasing need to install variable renewable energy sources to the grid, energy storage (ES) has become a key component for electrical grids to maintain stability. Various methods have been developed to optimize siting of ES systems to benefit a certain actor in the power grid, such as consumers, system operators, or power generators, or to optimize a particular variable, such as frequency, voltage, investment profits, or operator costs. In this paper, a new method has been developed to maximize the grid's efficiency by determining optimal ES siting and evaluating the impact of grid structure on ES performance. The method can decouple the grid structure's impact on grid performance from other factors to show which grid structure is best for ES performance. Inspired by a centrality concept known as a net ability that determines the importance of transmission lines, the net ability equation was modified to include ES within the grid. This paper introduces a weighted topological method called ES net ability that considers capacity, impedance, power transfer distribution factor (PTDF), and node type. ES net ability was also compared to another centrality concept known as betweenness. Using a linear programming method based on direct current (DC) power flow, a MATLAB program was written to determine ES optimal siting for bus test systems IEEE 30, IEEE 118, and IEEE 300. The ES net ability and betweenness of each bus were calculated and compared to determine which bus has the greatest impact on ES performance and efficiency. The buses' rankings could be used to compare buses within a network to determine which node provides the greatest benefit to ES performance and the grid. The average ES net ability of each IEEE test system was calculated to determine which grid structure is best suited for ES grid structure was further broken down into different factors, such as line impedance, line capacity, number of lines per node, number of connections per node, and node location. [ABSTRACT FROM AUTHOR]

Summary: In recent years, commercial building technology integrating renewable energy power generation has developed rapidly. However, the stochastic, intermittent renewable energy power generation and the error of load forecasting of a commercial building (CB) are easy to cause frequent fluctuation of tie‐line power, which brings challenges to the economic and safe operation of CB. In order to reduce the impact of renewable energy and load uncertainty on CB, and improve the power stabilization ability of CB, this paper proposes a multiple time‐scale (MTS) economic dispatch strategy for CB with building virtual energy storage system (VESS) and vehicle to building (V2B) under demand response mechanism. Firstly, taking CB as terminal load carriers, a new heat balance model based on the R‐C network is proposed, and the charging and discharging process of building VESS is analyzed. Secondly, by analyzing the travel trip law and charging and discharging behavior of EVs, an interactive control strategy between EVs and CB is proposed, that is, the control strategy of V2B. Then, Day‐ahead optimal economic dispatch mode with 1 hour as the time interval is established with the objective of minimizing the daily operation cost and considering the user temperature comfort factor of CB. Based on the Day‐ahead dispatching plan, the coordinated scheduling of VESS and V2B on the long time scale (15 minutes) and short time scale (1 minute) is used to establish a MTS rolling optimization model in Intra‐hour adjustment stage. This model is used to modify the Day‐ahead plan and stabilize the tie‐line power fluctuation. Finally, an example shows that the proposed Day‐ahead dispatching model can reduce the daily operating cost, and the Intra‐hour adjustment model can stabilize the tie‐line power fluctuation on MTS. [ABSTRACT FROM AUTHOR]

Summary: Global burgeoning pollution levels have led to massive efforts being made to electrify all modes of transportation in the coming decade. Most of the Electric Vehicles (EVs) in the automotive domain are powered by Lithium‐ion batteries (LIBs). Steady increase in number of EVs has led to generation of enormous amounts of data. Most of this data is related to the usage of LIBs and its parameters such as voltage, current, and temperature values. Recent data‐driven techniques possess the ability to extract meaningful insights from this data such as predicting the range of an Electric Vehicle or estimating State‐of‐Health of the batteries. Improvements in these models have been severely restricted due to limited access to proprietary experimental data and privacy concerns. This paper aims to introduce a Generative Adversarial Network based approach to produce high‐fidelity synthetic data to overcome the limitation of limited data. The synthetic data produced can be used for training neural networks to improve the accuracy of battery parameter predictions. The objectives of this research work are threefold; first we try to produce high‐fidelity synthetic time series data, secondly, heterogeneity in generated data is maintained, and thirdly, we test whether generated synthetic data enables improvements in accurate State‐of‐Charge estimation. [ABSTRACT FROM AUTHOR]

Summary: This paper deals with optimal multi‐stage power management and control coordinated with load shedding in interconnected microgrid (MG) with various renewable energy sources (RESs), namely: wind turbine, photovoltaic generator and concentrated solar power generator. Hybrid energy storage systems including electrical vehicles, fuel cells, redox flow batteries and superconducting magnetic energy storage have been introduced to improve the overall MG frequency dynamic performances in case of renewable energy integration and load disturbances. To improve the interconnected MG tie‐lines power control, flexible alternating current transmission systems are added. For an optimal and robust solution, an artificial intelligence strategy is proposed, combining a load frequency control‐based cascade Fuzzy‐proportional‐integral‐derivative controller with filter (PIDN). In doing so, a recently meta‐heuristic algorithm that mimics the social behavior of elephants in nature, named Elephant Herding Optimization, has been employed to optimize the Fuzzy‐PIDN controller gains. To show the effectiveness and superiority of the proposed control strategy, various scenarios are carried out. A comparative study in view of a settling time and peak over/undershoot has been performed. The obtained results demonstrate that the proposed control strategy gives good performances for MG power management and control under RESs integration and load disturbances scenarios. [ABSTRACT FROM AUTHOR]

Contemporary lithium-ion batteries (LIBs) are one of the main components of energy storage systems that need effective management to extend service life and increase reliability and safety. Their characteristics depend highly on internal and external conditions (ageing, temperature, and chemistry). Currently, the state of batteries is determined using two parameters: the state of charge (SOC) and the state of health (SOH). Applying these two parameters makes it possible to calculate the expected battery life and a battery's performance. There are many methods for estimating the SOH of batteries, including experimental, model-based, and machine learning methods. By comparing model-based estimations with experimental techniques, it can be concluded that the use of experimental methods is not applicable for commercial cases. The electrochemical model-based SOH estimation method clearly explains processes in the battery with the help of multidifferential equations. The machine learning method is based on creating a program trained to predict the battery's state of health with the help of past ageing data. In this review paper, we analyze the research available in the literature in this direction. It is found that all methods used to assess the SOH of an LIB play an essential role, and each method has its pros and cons. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs), as a new generation of intrinsically porous extended crystalline materials formed by coordination bonding between the organic ligands and metal ions or clusters, have attracted considerable interest in many applications owing to their high porosity, diverse structures, and controllable chemical structure. Recently, 2D transition-metal- (TM-) based MOFs have become a hot topic in this field because of their high aspect ratio derived from their large lateral size and small thickness, as well as the advantages of MOFs. Moreover, 2D TM-based MOFs can act as good precursors to construct heterostructures with high electrical conductivity and abundant active sites for a range of applications. This review comprehensively introduces the widely adopted synthesis strategies of 2D TM-based MOFs and their composites/derivatives. In addition, this paper summarizes and highlights the recent advances in energy conversion and storage, including the hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, CO2 reduction reaction, urea oxidation reaction, batteries, and supercapacitors. Finally, the challenges in developing these intriguing 2D layered materials and their composites/derivatives are examined, and the possible proposals for future directions to enhance the energy conversion and storage performance are reviewed. [ABSTRACT FROM AUTHOR]

Summary: As an energy storage material, microencapsulated phase change materials (MPCMs) have become a research hotspot in recent years due to their unique thermophysical properties. However, this material usually has limitations in terms of its performance, such as low encapsulation efficiency, leakage during phase change, poor cycle stability, and high degree of supercooling. To solve these problems, nanoparticle as‐modified material to improve the performance of MPCMs has attracted the attention of both domestic and overseas scholars. This paper aims to review the research progress of MPCMs modified by nanoparticle from three aspects: preparation, performance, and application. In this paper, the preparation method and principle of nanoparticles used for the modification of MPCMs are first introduced. Then, based on different properties (mechanical properties, thermal conductivity, thermal stability, and supercooling), the research works of nanoparticle‐modified MPCMs in recent years are reviewed. Finally, the practical applications of nanocomposite MPCM in the field of slurry, building, asphalt pavement, and battery thermal management are reported. [ABSTRACT FROM AUTHOR]

Summary: The rapid development of renewable energy power generation brings the strong demand of load regulation capacity for the coal‐fired thermal power unit. This paper presents a novel approach to improving its operational flexibility with a feedwater pump turbine driven by a thermal energy storage (TES) system. The purpose of this study is to investigate the feasibility of the proposed system and evaluate its system performance. During energy charging process, some steam is extracted from hot reheat pipe to heat the TES system and reduce the power generation. During energy discharging process, the feedwater pump turbine is driven by the steam generated from the stored heat instead of extracting from the main turbine. Performance evaluation was conducted based on a model of a 660 MW supercritical thermal power unit. The results show that a low TES outlet steam temperature during the charging process should be employed and extracting water from the pipe between condenser and No. 8 heater during the discharging process should be adopted. The peak regulation range can be expanded from (198‐660 MW) to (188.41‐685.15 MW) without charging the boiler load rate. Hence, the proposed novel system is demonstrated to be an effective way for flexibility improvement of thermal power unit. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new energy management strategy (EMS) for electric vehicles (EVs) with battery/supercapacitor hybrid energy storage systems (HESS). Firstly, the battery/supercapacitor HESS configuration and obtaining the load current from the driving cycle are comprehensively explained. Secondly, fixed and adaptive frequency‐based (AFB) EMS are discussed in detail. In the proposed method, the current demanded by the load is separated into low and high‐frequency components with the help of an adaptive low‐pass filter (A‐LPF). In addition, adaptive battery current (ABC) is generated according to the supercapacitor (SC) state of charge (SoC), and a double adaptive power allocation strategy is performed. The proposed method is compared with the AFB‐EMS for load currents, load powers, DC link voltages, filter cut‐off frequencies, battery, and SC SoCs on the UDDS driving cycle. When the obtained results were evaluated together, it was observed that the proposed method completed the driving cycle with a higher battery SoC and realized the adaptive cut‐off frequency in a narrower band. [ABSTRACT FROM AUTHOR]

Summary: The automotive sector around the world is undergoing a massive transition towards using cleaner and sustainable forms of energy. Lithium‐ion batteries are the key driver for this ongoing electrification transformation of all modes of transportation. Range computation of all such electric vehicles hinges on precise State‐of‐Charge (SOC) estimation of battery packs. Although significant research endeavours have focused on developing a series of SOC estimation techniques, accessibility to high‐quality battery parameter data (Voltage, Current and Temperature) still remains a challenge. Moreover, there are very few diverse open access datasets available at the moment. This paper directs its efforts in generating manifold battery datasets which result in improved performance of Deep learning architectures. The key contributions of this work are 2fold: (a) we highlight the effectiveness of the pyramidal self‐attention approach for producing reliable synthetic data, (b) we illustrate the generalization capability of this technique by evaluating this approach over different battery chemistries. The attention mechanism resulted in a mean absolute error of 0.3 and 0.2 for the pouch and cylindrical cell respectively. We provide implementation details in our results section which will empower researchers to reproduce our work effectively. [ABSTRACT FROM AUTHOR]

Summary: This paper characterizes an integrated hybrid energy storage unit required to support the new generator excitation system developed for doubly‐fed induction generators in the split‐shaft wind energy conversion units. The goal is to improve the power quality while significantly reducing the generator excitation power rating and component counts. The rotor excitation circuit is modified to directly include the storage to its DC link. The output power fluctuation can be attenuated solely by utilizing the rotor‐side converter making it self‐sufficient from the grid connection. The storage characteristics are identified based on several system design parameters, including the system inertia, inverter capacity, and energy storage capacity. The obtained power generation characteristics suggest an energy storage mix of fast‐acting types with a high energy capacity and moderate acting time. A feedback controller is designed to maintain the charge in the storage within the required limits. Adaptive model‐predictive control is also developed to reduce power generation fluctuations. The proposed system is investigated and simulated in MATLAB Simulink at different wind speeds to validate the results and demonstrate the system's dynamic performance. It is demonstrated that the system's inertia is critical to damping the high‐frequency oscillations of the wind power fluctuations. It is also shown that the bandwidth of the control system is determined by the system inertia and the size of the storage and inverter power rating. [ABSTRACT FROM AUTHOR]

Summary: Molten salts as thermal energy storage (TES) materials are gaining the attention of researchers worldwide due to their attributes like low vapor pressure, non‐toxic nature, low cost and flexibility, high thermal stability, wide range of applications etc. This review presents potential applications of molten salts in solar and nuclear TES and the factors influencing their performance. Ternary salts (Hitec salt, Hitec XL) are found to be best suited for concentrated solar plants due to their lower melting point and higher efficiency. Two‐tank direct energy storage system is found to be more economical due to the inexpensive salts (KCl‐MgCl2), while thermoclines are found to be more thermally efficient due to the power cycles involved and the high volumetric heat capacity of the salts involved (LiF‐NaF‐KF). Heat storage density has been given special focus in this review and methods to increase the same in terms of salt composition changes are discussed in the paper. Methods of concatenating energy storage systems with nuclear power plants are also discussed with different types of nuclear reactors like MHTGR, PAHTR, VHTR, etc. Nanomodifications of molten salts are done to improve heat transfer properties and efficiency of the transfer. The best dopants for such modifications were found to be TiO2, SiO2, MWCNTs, etc. Future challenges for large scale deployment of molten salts viz., high volume expansion ratio, low thermal conductivity, incongruent melting, corrosion, etc., are listed and discussed. Corrosion of molten salts and its mitigation has been discussed in detail for developing next‐generation storage systems and its remedies are also discussed. [ABSTRACT FROM AUTHOR]

Summary: The term "future material" is referred for activated carbons (AC) due to their easy/scalable preparation, compatible property, and numerous applications. The applications of such materials for gas adsorption and storage play a vital role in the large‐scale feasibility of adsorbed gas storage. This could provide a better alternative to compression‐based storage for vehicular applications due to lower energy and materials input. Currently, compression‐based storage has been thoroughly studied and widely used; however, there are limited reports on LCA. In this paper, a lifecycle‐based approach has been deployed for chemically activated carbon (CAC) production using KOH and compared with inventory‐based physically activated carbon (PAC). The PACs have a far lesser impact than the CACs mainly owing to the chemicals used. Potassium hydroxide and electricity account for nearly 70 to 90% of different ecotoxicity indicators. It has also been estimated that if by‐products generated using chemicals are recovered, the midpoint and endpoint impacts can be brought down substantially up to 60%. In particular, Terrestrial ecotoxicity is reduced by 80%, Human toxicity up to 60%, and Water toxicity up to 60%. However, the CACs/CACRs still have higher impact than the PACs, mainly on four indicators. Their application for methane‐rich gas storage derived from a life cycle study encourages adsorption‐based storage adoption. The metal requirement for cylinder manufacturing is the highest which can be reduced if adsorption‐based storage is adopted. Therefore, the sustainability of the proposed novel process is increased with incorporation of biomass‐based activated carbons, reduction in thickness of cylinders, and lower pressure requirement for storage. It has been observed that Global warming potential due to the cylinder for gas storage is drastically reduced from 73% to 15.6%. The process of activation and application of ACs for gas storage has been investigated using life cycle as an assessment tool. [ABSTRACT FROM AUTHOR]

Summary: The paper presents a novel methodology based on a BARON solver in general algebraic modeling system (GAMS) computational environment to solve the stochastic profit‐based unit commitment (SPBUC) problem in the deregulated energy market. The profit is considered as the difference between the total revenue collected by selling energy in the competitive energy market and the production cost of energy. The concepts of the deregulated energy market like market clearing price and market reserve price are employed for the determination of the expected revenue. Energy storage systems and electric vehicles have the benefit to act as load as well as generating sources during the uncertain and intermittent nature of renewable energy sources (RES). Thus, the RES in combination with battery energy storage systems and plug‐in hybrid electric vehicles (PHEVs) are modelled and included in the present study. The Monte Carlo Simulation technique is employed to deal with the uncertain nature of wind speed, solar irradiance and PHEVs. First, the proposed solution methodology is tested on a 10‐unit thermal system with and without valve point loading wherein it has provided an enhanced profit of 1.28% when compared with existing results reported in published literature. Secondly, the comparative results obtained by solving the SPBUC problem integrated with RES with different solvers available in GAMS computational environment demonstrate that the BARON solver outperformed the other solvers in terms of computation time and expected profit attained. [ABSTRACT FROM AUTHOR]

Summary: The efficient implementation of solar systems in buildings depends on the storage of energy yielded, as it can both increase the solar system's autonomy and make it a feasible solution for zero energy buildings, and make storage vessels more compact, reducing precious space requirements. This is of particular important in places with reduced time of sunshine, where solar systems are less effective, because of the deviation between solar radiation and the demand. The traditional storage options use water, which is practical, safe and low‐cost, especially when the storage requirements are small. However, when larger storage is needed, limits concerning the use of water exist, mainly due to the need for larger installation space and the increased thermal losses. The use of phase change materials (PCM) for thermal energy storage seems an upcoming technology. The main idea is the substitution of water with PCM, which feature larger specific energy storage capacity compared to other conventional materials. In the context of the specific paper, a combined solar thermal system used for the preparation of domestic hot water (DHW) and space heating (Solar Combi System) with two different types of storage is studied, for two Greek cities. The aim is to find out which is the most efficient way of storing energy with respect to the autonomy of the system, for a solar combi system. This is being achieved by determining the comparative autonomy of PCM and water storage system for various climates. It was proven that using PCM is advantageous, as it can extend the autonomy duration of the solar system for 2 to 8 hours, depending on the season and the climatic conditions. However, it was also seen that in solar combi systems used throughout the whole year, PCM are inefficient during summer period. [ABSTRACT FROM AUTHOR]

Summary: The lithium‐ion battery, as an electrochemical energy storage technology, has expanded its application in recent years, owing primarily to electric vehicles (EVs). The battery management system (BMS) is housed within the battery pack and is in charge of calculating one of the most important variables, the state of charge (SOC). The electrical equivalent circuit model (EECM) of the battery has been developed and different model parameters are identified by solving the equations with the help of Levenberg‐Marquardt (LM) method. To calculate the SOC for various load conditions, a precise relationship between the SOC and open‐circuit voltage is required. In this paper, the extended Kalman filter (EKF) and dual extended Kalman filter (DEKF) algorithms are utilised in order to get a fairly good estimate of SOC based on the EECM. The impact of voltage and current sensor bias on SOC is investigated. Three driving cycles, namely the Urban Dynamometer Driving Schedule, New York City Cycle, and Braunschweig City Driving Cycle, are used as a simulated variable load to create a real‐life EV environment at different temperatures to validate the effectiveness of these algorithms. The proposed algorithms give a fairly early indication of the SOC threshold levels from 0.5 to 0.1. [ABSTRACT FROM AUTHOR]

Summary: Energy storage systems (ESS) provide buildings with the ability to store electricity generated from renewable energy sources (eg, solar photovoltaics), increasing building operational flexibility and reliability. Previous studies on ESS in buildings often make assumptions about key input parameters (eg, installation factor, roundtrip efficiency, etc.) being fixed, overlooking potential performance risks due to uncertainty or variations in these parameters. The goal of this paper is to quantify the impact of uncertainty in technical and financial parameters on the economic performance of Li‐ion batteries (LIB), proton‐exchange membranes reversible fuel cells (PEM RFC), and reversible solid oxide cells (RSOC). The analysis is conducted on three medium‐sized office buildings located in three different climate zones of the United States: 2B (Phoenix, AZ), 3B‐Coast (Los Angeles, CA), and 4C (Seattle, WA). Two metrics are used to evaluate the economic viability of the selected technologies, namely the levelized cost of electricity (LCOE) and the levelized cost of energy storage (LCOS). While the results of the LCOE and LCOS differed in value between those cities, the cost breakdown for LCOS in all locations shows that capital cost is the biggest cost contributor, followed by electricity cost. A Monte‐Carlo simulation was also conducted to assess the impact of uncertainty in input parameters on the LCOS model. The simulation results allowed us to generate probable ranges and probability tables for the LCOS. [ABSTRACT FROM AUTHOR]

Summary: The large‐scale penetration of renewable energy leads to some imperative issues to the power grid. Energy storage technology is regarded as an effective method to solve these problems. In this paper, a hybrid cogeneration energy system based on compressed air energy storage system with high temperature thermal energy storage and supercritical CO2 Brayton cycle is proposed. A thermodynamic model of the system is established. Energy and exergy analysis are carried out based on a case study. It was found that under the design condition, the round trip efficiency of 58.66%, the energy storage density of 5.45 kWh/m3, and the overall exergy efficiency of 62.00% can be achieved. At the same time, the hot water about 88°C can be supplied. The influences of some key parameters on the performance are analyzed. It was found that the round trip efficiency is most sensitive to the outlet temperature of high temperature thermal energy storage system, isentropic efficiency of compressors and turbines, followed by the intercoolers effectiveness. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, microgrids are emerging as an invaluable framework for the integration of renewable energy sources and demand response programs. In such systems, energy storage facilities are also frequently deployed to properly manage surplus energy from renewable sources on pursuing more efficient management of the system. Hybrid storage systems in which various storage facilities are combined may result in a more effective solution than only considering one storage technology. This way, the good features of the different technologies may be jointly exploited while their drawbacks are minimized. Due to the large‐scale integration of renewable energies in this kind of grid, coping with uncertainties becomes a critical issue. Moreover, the operation of microgrids frequently deals with other kinds of uncertainties related to energy pricing from the upscale grid (in the case of grid‐connected mode) or local demand. This way, proper modeling of uncertainties is essential for adequately operating these systems. This paper contributes to this pool by developing a novel interval‐based formulation, for optimal scheduling of microgrids considering battery and pumped‐hydro storage systems. To achieve this goal, the optimal scheduling of a microgrid with pumped‐hydro and battery energy storage considering demand response is modeled, firstly. Then, the new interval‐based formulation is used to cope with the uncertainties. Finally, the suggested model is verified using simulations in various cases, and the results confirm the effectiveness of the novel interval‐based formulation for the optimal scheduling of microgrid with pumped‐hydro and battery energy storage under uncertainty. [ABSTRACT FROM AUTHOR]

This paper gives a broad overview of a plethora of energy storage technologies available on the large-scale complimented with their capabilities conducted by a thorough literature survey. According to the capability graphs generated, thermal energy storage, flow batteries, lithium ion, sodium sulphur, compressed air energy storage, and pumped hydro storage are suitable for large-scale storage in the order of 10's to 100's of MWh; metal air batteries have a high theoretical energy density equivalent to that of gasoline along with being cost efficient; compressed air energy storage has the lowest capital energy cost in comparison to other energy storage technologies; flywheels, super conducting magnetic storage, super capacitors, capacitors, and pumped hydro storage have very low energy density; compressed air energy storage, cryogenic energy storage, thermal energy storage, and batteries have relatively high energy density; high efficiencyin tandem with high energy density results in a cost efficient storage system; and power density pitted against energy density provides a clear demarcation between power and energy applications. This paper also provides a mathematical model for thermal energy storage as a battery. Furthermore, a comprehensive techno-economic evaluation of the various energy storage technologies would assist in the development of an energy storage technology roadmap. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The inconsistency in the mass production of lithium‐ion battery (LIB) packs stem from the inconsistency in the capacity, voltage and internal resistance of single batteries that compose packs. The inconsistency issue of these battery packs can greatly reduce the output performance of a large power pack. This paper proposed the machine learning approach based on self‐organization mapping (SOM) neural networks for establishing the consistency of LIBs. This method comprehensively compares and analyzes the real‐LIB parameters (internal resistance, capacity and voltage) data obtained during charging and discharging to form the clusters of similar performing LIBs. Experimental result validated the clustering analysis and it indicates that the performance of clustered battery pack typically precedes than that of original. The capacity of clustered battery pack increased 1.9% compared with brand‐new pack. The temperature distribution of the battery pack assembled after screening is consistent. The peak temperature is 4°‐5° lower than the ordinary battery, and the temperature fluctuation is reduced by 2.6°. In addition, the application of cluster analysis is expanded and some key research directions are pointed out. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicle virtual energy storage technology can effectively improve the utilization of renewable energy. Aiming at the impact of the uncertainty of electric vehicle on the power grid, an optimized dispatching method of hybrid energy storage systems based on multiobjective optimization in the scenario of tracking plan output is proposed in this paper. The predicted value of the photovoltaic power obtained by the particle swarm optimization (PSO)‐back propagation (BP) neural network is used to formulate the planned output of photovoltaic power generation, and the principle component analysis algorithm is used to extract the main features affecting photovoltaic power generation to further improve the prediction accuracy of photovoltaic output power. From the perspective of the service life of electric vehicles, a two‐stage optimal control method of hybrid energy storage systems based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to achieve energy distributions between electric vehicles and supercapacitors. Fully consider the benefits of electric vehicle users and the capacity of tracking plans, a multiobjective optimization model of hybrid energy storage systems to track planned output is established, and the nondominated sorted genetic algorithm‐III is adopted to solve the model. The validity of the model is verified by a simulation test of actual operating data of a business park in China. The simulation results show that after the optimized control, the average absolute error of the deviation power reduces from 1.092 to 0.0528 MW, power fluctuating times of electric vehicles decreases from 151 to 80, and the daily income benefit increases from $404.468 to $483.116 in the cloudy day. The method proposed in this paper can effectively improve the controllability of renewable energy, and provide a theoretical basis for the application of electric vehicle virtual energy storage technology. [ABSTRACT FROM AUTHOR]

Summary: Climate change is one of the grand global challenges facing the world community. With the global warming threat and the attendant public health risks caused by poor air quality and harmful fine particles, the two different energy sources—nuclear and renewables—may have found a common ground and thread to form an alliance to meet these grand global challenges. The latest advent of technologies enables load‐follow flexibility of nuclear power, which has mainly provided baseload power, making nuclear energy an ideal partner for renewable energy, leveling the uncertainty and accommodating the unpredictability associated with renewables. The progress in small modular reactor (SMR) technology, which can be deployed to remote areas, can further facilitate the linking of the two energy resources. Some investigators have recently pioneered the ideas to inosculate these two energy resources with the aid of energy storage devices. This paper presents a series of case studies on meeting demand forecasts by coupling nuclear and renewable energy resources for various practical scenarios. The paper also proposes a paradigm shift in thinking in order to eliminate (or minimize to a certain extend) the need for energy storage devices in the combined energy production system by replacing the energy storage devices with some industrial facilities to directly utilize any excess power supplied by renewable energy. As an example, a desalination facility appended to the nuclear‐renewable integrated system is shown to absorb surplus power from renewable energy to produce water, thereby satisfying the demand for both power and water in a typical small remote village. Case studies are also performed to extend the concept to include the effect of weather condition and seasonal variation in solar energy conditions. The promising role of disruptive emerging technologies in the nuclear‐renewable combined system is also addressed. [ABSTRACT FROM AUTHOR]

Summary: Because of the unstable and intermittent nature of solar energy availability, a thermal energy storage system is required to integrate with the collectors to store thermal energy and retrieve it whenever it is required. Thermal energy storage not only eliminates the discrepancy between energy supply and demand but also increases the performance and reliability of energy systems and plays a crucial role in energy conservation. Under this paper, different thermal energy storage methods, heat transfer enhancement techniques, storage materials, heat transfer fluids, and geometrical configurations are discussed. A comparative assessment of various thermal energy storage methods is also presented. Sensible heat storage involves storing thermal energy within the storage medium by increasing temperature without undergoing any phase transformation, whereas latent heat storage involves storing thermal energy within the material during the transition phase. Combined thermal energy storage is the novel approach to store thermal energy by combining both sensible and latent storage. Based on the literature review, it was found that most of the researchers carried out their work on sensible and latent storage systems with the different storage media and heat transfer fluids. Limited work on a combined sensible‐latent heat thermal energy storage system with different storage materials and heat transfer fluids was carried out so far. Further, combined sensible and latent heat storage systems are reported to have a promising approach, as it reduces the cost and increases the energy storage with a stabilized outflow of temperature from the system. The studies discussed and presented in this paper may be helpful to carry out further research in this area. [ABSTRACT FROM AUTHOR]

Summary: Compressed air energy storage (CAES) is promising to enable large‐scale penetration of renewable energies (REs). However, conventional diabatic CAES (D‐CAES) depends largely on fossil fuels, while adiabatic CAES (A‐CAES) is limited in output power. To conquer these disadvantages, concept of combined heat and CAES (CH‐CAES) is proposed in this paper. The proposed system couples an electric heater with conventional A‐CAES. During charging, electricity storage transforms from pure compression to partly relying on Joule heating. The stored heat in an electric heater will be used to boost turbine inlet temperature during discharging. Consequently, system charge/discharge capacity can be improved without enlarging cavern size, raising cavern pressure, and producing greenhouse gases. This paper discusses three types of CH‐CAES systems with different electric heater installation positions. Off‐design performance analysis for each system is conducted on the basis of turbomachinery (compressors, turbines, and the pump) characteristic maps and heat exchangers off‐design models. Performance comparison is conducted between these three CH‐CAES systems (called Mode II, III, and IV for simplification) and the conventional A‐CAES system (Mode I). Control strategies are also given in this paper. Results show that the EVR (energy generated per unit volume of storage) increases with participation of an electric heater, while the RTE (system roundtrip efficiency) slightly decreases. Mode I has the highest RTE. The largest EVR appears in Mode III where the electrical heater is in series with the intercooler and after cooler. Mode II is a compromise solution to achieve both relatively high RET and EVR when the electrical heater is installed in series only with the intercooler. Mode IV with a paralleling electrical heater has great flexibility to adapt different user demands. The integration of the ORC has a positive effect on system RTE and EVR. [ABSTRACT FROM AUTHOR]

Summary: The all‐vanadium redox flow battery (VRFB) is emerging as a promising technology for large‐scale energy storage systems due to its scalability and flexibility, high round‐trip efficiency, long durability, and little environmental impact. As the degradation rate of the VRFB components is relatively low, less attention has been paid in terms of VRFB durability in comparison with studies on performance improvement and cost reduction. This paper reviews publications on performance degradation mechanisms and mitigation strategies for VRFBs in an attempt to achieve a systematic understanding of VRFB durability. Durability studies of individual VRFB components, including electrolyte, membrane, electrode, and bipolar plate, are introduced. Various degradation mechanisms at both cell and component levels are examined. Following these, applicable strategies for mitigating degradation of each component are compiled. In addition, this paper summarizes various diagnostic tools to evaluate component degradation, followed by accelerated stress tests and models for aging prediction that can help reduce the duration and cost associated with real lifetime tests. Finally, future research areas on the degradation and accelerated lifetime testing for VRFBs are proposed. [ABSTRACT FROM AUTHOR]

Summary: The demand of electric energy is increasing globally, and the fact remains that the major share of this energy is still being produced from the traditional generation technologies. However, the recent trends, for obvious reasons of environmental concerns, are indicating a paradigm shift towards distributed generation (DG) incorporating renewable energy resources (RERs). But there are associated challenges with high penetration of RERs as these resources are unpredictable and stochastic in nature, and as a result, it becomes difficult to provide immediate response to demand variations. This is where energy storage systems (ESSs) come to the rescue, and they not only can compensate the stochastic nature and sudden deficiencies of RERs but can also enhance the grid stability, reliability, and efficiency by providing services in power quality, bridging power, and energy management. This paper provides an extensive review of different ESSs, which have been in use and also the ones that are currently in developing stage, describing their working principles and giving a comparative analysis of important features and technical as well as economic characteristics. The wide range of storage technologies, with each ESS being different in terms of the scale of power, response time, energy/power density, discharge duration, and cost coupled with the complex characteristics matrices, makes it difficult to select a particular ESS for a specific application. The comparative analysis presented in this paper helps in this regard and provides a clear picture of the suitability of ESSs for different power system applications, categorized appropriately. The paper also brings out the associated challenges and suggests the future research directions. [ABSTRACT FROM AUTHOR]

Summary: Potassium ion batteries (KIBs) have attracted considerable attention as a next‐generation large‐scale energy storage system. Due to the large diameter of K+ ions, developing advanced electrode materials to achieve high‐performance KIBs is challenging. In this paper, one‐dimensional (1D) hybrid nanostructures comprising a MoSe2 core and a CoSe2/N‐doped carbon shell (denoted as MC‐Se@NC) are successfully synthesized by thermal treatment of Co‐based metal‐organic framework (ZIF‐67)‐coated MoO3 nanorods. The unique 1D morphology and synergistic effect between the different elements provide sufficient electrochemical reaction sites and facilitate the reaction kinetics. Further, the N‐doped carbon coating alleviates the volume fluctuation and pulverization of the active materials during cycling and improves the electrode conductivity. Accordingly, when applied to KIBs, the MC‐Se@NC anode delivers a high reversible capacity of 327 mA h g−1 at 0.5 A g−1 after 150 cycles, and remarkable rate capability of 190 mA h g−1 at 2.0 A g−1. Furthermore, to explore the practical application of MC‐Se@NC anode, a K‐ion full cell consisting of MC‐Se@NC anode and Prussian blue cathode is fabricated and characterized. As a demonstration, a light‐emitting diode (LED) is successfully powered by the K‐ion full cell. These excellent electrochemical properties of MC‐Se@NC present possibilities for the commercialization of KIB‐based large‐scale energy storage systems. [ABSTRACT FROM AUTHOR]

Summary: The energy storage sector has witnessed meteoric growth in the last decade. Lithium‐ion based batteries, at the centre of this unprecedented rise in storage systems, have been driving the electrification revolution in the automotive domain. These lithium‐ion batteries are largely used in consumer electronic devices and wearables. The energy storage research activities have been primarily focused on devising accurate algorithms to compute state‐of‐charge of these energy storage systems. State‐of‐charge helps to calculate the remaining usage time of devices and determines the range of an electric vehicle. All state‐of‐charge algorithms developed till date have used curated labelled datasets available in abundance. However, in reality, researchers do not have access to battery datasets from manufacturers due to privacy concerns. Recording battery measurements is also an expensive and herculean task. This paper introduces an auto‐correlation mechanism to compose synthetic battery datasets. This procedure is very beneficial during data scarce scenarios wherein researchers have access to finite amount of data. The leading contributions of this work include (a) synthetic battery dataset creation using auto‐correlation mechanism and (b) diverse heterogeneous data generation, which is suitable for battery capacity forecasting purposes. [ABSTRACT FROM AUTHOR]

Summary: Residential building consumes a significant amount of energy. To address the issue, these structures have been supplied with renewable energy sources (RES), an energy storage system (ESS), and an electric vehicle (EV). In a home, a home energy management system (HEMS) has been implemented to schedule and regulate domestic appliances. Many studies in HEMS have been conducted in order to reduce the cost of power and the peak to average ratio (PAR). However, there is insufficient use of RES, ESS, EV, and excess domestic energy. As a result, this research presents a HEMS architecture that is integrated with RES and ESS and includes home‐to‐grid (H2G) power flow functionality. The RES electricity is initially utilized to power domestic appliances and charge ESS before being transferred back into the main grid to reap economic rewards. During the low‐price period, the ESS and EV are charged and discharged during the high‐price period. The paper built a multi‐objective optimization problem using the provided approach that integrates electricity cost and system PAR. The grey wolf optimization algorithm is used to tackle the multi‐objective optimization problem. The results clearly show that the proposed technique decreases expenditures by 45.80% and PAR by 28.44%, compared to the baseline timetable in 24 h. The H2G power flow feature allows the home to send back excess energy to the grid, cutting electricity expenditures by 70% and achieving additional economic benefits. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel control scheme on AC/DC ring bus islanded microgrid under unbalanced and non‐linear loads. The DC bus is integrated with the fuel cell, one wind turbine, and two batteries. The DC bus can be divided into two sub‐sections, where the first zone is supported by the fuel cell and battery 1, and the second zone is installed with the wind turbine and battery 2. The AC bus is also able to be sectionalized into two sub‐sections where the diesel generator and the unbalanced load are located on the first section and the non‐linear load with one wind turbine are placed on the other section. The DC bus is connected to the AC bus through two parallel three‐phase lines each one configured by three single‐phase inverters. The proposed control scheme in the ringed and separated modes presents several contributions including balancing the unbalanced loads, compensating the harmonics of non‐linear loads, improving the voltage stability and sag/swells, enabling partial operation under outages, and enhancing resilience. In the ring mode, wind turbine 1 and diesel generator inject balanced and linear P‐Q powers to the system as 39 kW/6 kVAr and 7 kW/9 kVAr, respectively. In the separated mode, the P‐Q powers of wind turbine 1 are 36 kW/9 kVAr and they are 15 kW/9 kVAr for diesel generators. In both modes, the unbalanced and non‐linear powers are supplied by the DC bus. The results verify that the developed control strategy efficiently deals with harmonic components, unbalanced currents and voltage issues. [ABSTRACT FROM AUTHOR]

Summary: This paper suggests a new sizing optimization method of an off‐grid renewable energy system. To perform an accurate analysis of the distribution of the exchanged energy with all storage elements, the discrete Fourier transform tool has been used. Besides, different frequency strains have been achieved in accordance with the dynamic of each storage device. Thus, the problem formulation targets the minimization of the Total Cost of Electricity (TCE) for the purposes of improving the reliability, and increasing the performance and the exploitation of the renewable energies for electrification fulfillment. The Loss of Power Supply Probability (LPSP) is also considered to improve the reliability rate of the selected configuration. Considering all storage dynamics to achieve the energy management strategy, a modified Moth‐Flame Optimization (MFO) algorithm is proposed to address this problem. Based on economic evaluation and analysis, an optimal configuration of the system has been established. Referring to a high level of economic feasibility, the obtained results affirm that this architecture is able to deal successfully with the current sizing problem through its flexibility to achieve the lowest cost which is decreased by around 0.7 e04$ compared with the start of the search for the desired solution. [ABSTRACT FROM AUTHOR]