Energy Storage Demand Research Articles

Li-Sn Alloyed Layer Containing Artificial SEI on Li-Metal Anode for Superior Lithium Metal Batteries: Development and Understanding of Anode/Electrolyte Interfacial Phenomena

Modern civilization and cutting-edge technologies and tools are highly dependent on energy storage devices and demand efficient and long-lasting high-energy-density storage devices. Lithium metal batteries have surpassed their competitors concerning energy density due to their inherent high specific capacity (3860 mAh/g) and the lowest electrochemical potential (-3.04 V vs. standard hydrogen electrode), proving to be one of the most favorable energy storage devices for smart and mobile gadgets, electric automobiles, grid storage, etc. However, safety-threatening and performance-decay challenges associated with lithium metal anodes, such as lithium dendrite formation, uncontrolled parasitic reactions between Li-metal and electrolyte, low coulombic efficiency, dead Li-formation, etc., restrict its employment in practical batteries. The root cause of the dendrite formation is poor Li-ion conductivity of bulk lithium metal, which results in an uneven electric field distribution, leading to non-uniform Li deposition/stripping upon repeated charge/discharge. Herein, a facile chemical reduction process is being opted to fabricate a highly Li-ion diffusive Li-Sn-based artificial SEI layer on the lithium metal surface to prevail over the above-mentioned issues. Therefore, upon careful concentration investigation of reactants, 25 mM precursor concentration (SnCl4 in EC: DMC in 1:1 volume ration) demonstrated the best electrochemical performance, revealing a cumulative capacity of over 700 mAh/cm2 at a current density of 1 mA/cm2 for 1 h in a symmetric cell. Physical characterizations (XRD and SEM-EDS) showed a thin layer containing Li-Sn alloys along with LiCl formed at the lithium metal surface. Possessing high Li-ion diffusivity, Li-Sn alloyed hybrid anode illustrated a gradual decrease in the charge transfer resistance on increasing the concentration, thus, lower overpotential in symmetric cells, in contrast to pristine Li-metal. However, an increase in the electrical resistance has been observed on increasing the precursor concentration due to the presence of the insulating LiCl phase in the protective layer. Moreover, in-situ optical microscopy demonstrated more uniform and on-surface Li-deposition for the hybrid electrode, whereas mossy and dendritic growth on bare Li-metal. Full cells prepared with Li-Sn alloyed anode against NMC532 cathode illustrated stable cycling up to 150 cycles; contrastingly, the cell with pristine Li-metal showed a drastic capacity fade just after 100 cycles, demonstrating the inclination towards safer lithium metal batteries.

Designing a centralized storage hydrogen supply chain network with multi-period and bi-objective optimization

This study introduces a multi-period centralized storage optimization model aimed at designing an efficient hydrogen supply chain system, considering cost and emissions as dual objectives. It integrates multiple energy sources, production and storage methods, transport combinations, demand scenarios, and carbon capture systems, offering a comprehensive decision-making approach for hydrogen network design. Employing the mixed-integer linear programming methodology, the proposed model resolves these complexities. The research applies this model to a case study in France, generating six unique scenarios for 10 and 15 cities, and compares them against two distinct decentralized models. The findings consistently highlight the centralized storage model’s cost benefits across various demand scenarios, including cases of unrestricted emissions as well as cases with limited emission targets. The cost-effectiveness of this proposed model enhances its feasibility within the current context of decarbonization.

Reducing Energy Storage Demand With ES-2: Principles Analysis and Control Design

Reducing Energy Storage Demand With ES-2: Principles Analysis and Control Design

Distributionally robust CVaR optimization for resilient distribution system planning with consideration for long-term and short-term uncertainties

Climate change is exerting excessive strain on the environment, leading to an ever-increasing frequency of extreme weather events that negatively impact distribution systems. To mitigate these impacts, this paper presents a distributionally robust conditional value-at-risk (DR-CVaR) optimization approach for resilience-oriented distribution system planning. Firstly, a resilience-oriented distribution system planning model is developed, which incorporates flexible operational resources, such as mobile energy storage prepositioning and demand response program, in the emergency response phase. Then, the uncertainties associated with extreme weather events are addressed using a DR-CVaR optimization approach, where the short-term uncertainty in the occurrence of outages caused by specific extreme weather events is addressed through a distributionally robust optimization approach, while the long-term uncertainty regarding the risk of potential extreme weather events is addressed by a conditional value-at-risk optimization approach. The DR-CVaR model is eventually transformed into an equivalent three-level model and solved using a customized nested column-and-constraint generation algorithm. Finally, case studies are conducted based on the IEEE 33-bus distribution system under several extreme weather events. The numerical results show that the proposed model can reduce the load shedding cost as well as investment cost, which confirms the effectiveness and superiority of the proposed model.

Are Bergmann's and Jordan's rules valid for a neotropical pitviper?

AbstractMorphological variation along the spatial distribution of species has been extensively investigated in ecological studies, and several ecogeographical rules explore the relationships between morphological traits and the environment. Many morphological traits are correlated, providing an opportunity to evaluate the validity of multiple ecogeographical rules simultaneously. Bergmann's rule predicts that endothermic animals in colder locations are larger than those in warmer locations. Jordan's rule predicts that fish from colder locations have more vertebrae than those from warmer locations. We tested the validity of Bergmann's and Jordan's rules for the neotropical lancehead snake Bothrops jararaca. We evaluated three morphological characters of 342 specimens: number of ventral scales (proxy for vertebrae number), snout–vent length (a linear measure of body size) and stoutness (volumetric body size). We implemented spatial regressions to evaluate the variation of morphological dimensions using climatic predictors: the minimum temperature and evapotranspiration. SVL was poorly related to minimum temperature and evapotranspiration. However, stouter individuals were found in colder places with greater evapotranspiration, following Bergmann's rule and the water conservation hypothesis. Individuals in warmer locations also had a greater number of ventral scales, reversing Jordan's rule. We showed that different selective pressures act on different morphological dimensions. Although stoutness follows Bergmann's rule, its variation would arise from an energy storage demand rather than heat conservation. Also, stoutness variation along evapotranspiration gradients could represent a mechanism to avoid hydric stress in environments with considerable climatic variations. The variation in vertebrae number along temperature gradients could be related to ecological factors and foraging. We highlight that physioecological mechanisms to deal with climatic variation and ecological aspects could be identified in snakes through intraspecific analyses, contrasting with interspecific studies that can hardly detect general trends. Due to different environmental effects on body size, we shed new light on the importance of exploring multiple morphological dimensions in macroecological studies.

Capacity optimum distribution mode of various types of energy storage devices considering the improvement of active-supporting capability

Abstract Under the dual-carbon development goal, the energy storage (ES) demand on the new energy (NE) side has been upgraded from the scene of promoting absorb to the scene of promoting absorb and actively supporting the power grid. Therefore, this paper proposes a capacity optimum distribution mode (CODM) for various types of ES equipment considering the improvement of active-supporting capability. Firstly, a polytypic ES capacity optimum distribution method considering both NE absorb and active-supporting grid capacity improvement is proposed. Then, the solution process of the complex model is optimized by introducing the characteristic curve extraction method of typical working conditions. Finally, founded on the historical data of typical NE power stations, the ES configuration requirements and economy under the scenes of promoting absorb, promoting absorb and active-supporting are calculated. The comparison results show that compared with promoting absorb, the scenes of promoting absorb and active-supporting can better meet the ES requirements of the NE side technically and have a better economy.

Modified free energy generation using permanent Neodymium Magnet based on Bedini with Maxwell and Lorenz gauge conditions

An efficient, reliable, and real-time electricity generator is proposed by keeping in view the constraints, related to the electricity generation cost, resource reservoirs, greenhouse gas emission, energy storage, and power demand for a pre-defined residential area. Paramount importance is given to the rotor's design; coupled with movable neodymium magnets to increase the harvester's life span and system's net revenue. For the amplification in the stator output, a cost-effective approach is adopted that is based on the parallel winding of the bifilar coil. Furthermore, the coefficient of performance factor for the Bedini's smart school girl circuit based electricity harvester is modified by aiding the concepts of radiant energy incorporated with the electromagnetic phenomenon. Moreover, the mathematical model is formulated by the Maxwell equations using the Lorenz gauge condition to capture free vacuum energy from the environment. For the investigation, analysis, and corroboration of the proposed design, results are reported after performing extensive numerical computations and a comprehensive comparison between the original and modified design is stated in detail.

A two-stage risk-based framework for dynamic configuration of a renewable-based distribution system considering demand response programs and hydrogen storage systems

Distribution feeder reconfiguration (DFR) in distribution systems can enhance operating conditions by reducing losses and improving voltage indices. This paper introduces a dual-stage risk-based framework that concurrently tackles day-ahead reconfiguration and microgrid scheduling within the distribution network. To control the negative aspects of uncertainties, the proposed framework integrates energy storage systems (ESS/HSS) and demand response programs (DRPs) at the network level, enhancing adaptability. The initial stage of the proposed model employs the AC power flow model, utilizing loss and voltage deviation functions as objective benchmarks for network reconfiguration. The scheduling is meticulously executed per interval, deriving optimized structures under diverse scenarios with the aid of a case reduction technique (CRT) to streamline solutions. The ultimate solution employs the grey wolf optimization (GWO) algorithm and CPLEX solver in the first and second stages respectively. Outcomes from applying this approach to the adjusted 118-bus network manifest improved operational conditions and voltage quality through reconfiguration. Impressively, the integration of ESS/HSS and DRPs yields a substantial 22.34% reduction in total operating costs, a conclusion substantiated by the numerical findings. Furthermore, by leveraging the CRT, a remarkable 56.17% reduction in problem-solution time is achieved.

Quantifying energy flexibility potential of ground electric vehicles in an airport with real behavior data

Towards sustainable airports, vehicle electrification can not only reduce direct carbon emissions, but also provide great energy flexibility to integrate renewable energy. However, it remains challenging to quantify their potential, due to a lack of understanding of various vehicles’ complicated behavior in an airport. Hereby, a large-scale investigation into the behavior and energy consumption of all types of vehicles was performed in a Chinese hub airport. From the overall perspective of an airport, a behavior data-based simulation was conducted to evaluate their accessible storage (EAS) under different conditions, including charging infrastructure and flight schedule. For the full-potential case with a normal flight schedule, the airside vehicles have the largest share of EAS (60 %), followed by the landside vehicles in the ground transportation center (30 %) and the staff parking lots (10 %). The overall EAS is almost as large as the energy storage demand to realize a fully solar-supplied airport. Nevertheless, the current capacity is limited to only 15 % of the full potential by electrification rate and charging behavior. This study fills the gap of lacking behavior data of the vehicles in airports. The findings shed light on the role of vehicle electrification in airport decarbonization and provide guidelines for future practice.

A two-stage optimal mechanism for managing energy and ancillary services markets in renewable-based transmission and distribution networks by participating electric vehicle and demand response aggregators

The growing uncertainties in power operations due to the integration of renewable generations (RGs) and electric vehicles (EVs) into electricity grids have amplified the significance of ancillary services (AS). These services have become essential to ensure the sustainable functioning of the grid. In light of this, we introduce a two-stage optimization framework to manage competitive energy and AS markets at the interface of the transmission system (TS) and distribution system (DS). Our approach takes into account a comprehensive set of economic, technical, and security factors. This mechanism is structured in two stages: the first stage encompasses the energy market, while the second stage encompasses AS markets. Spinning reserve (SR) is supplied by conventional thermal units (TUs), whereas regulation capacity is provided by energy storage systems (ESSs), fast-response generators, electric vehicles (EVs), and demand response (DR) aggregators. We applied this mechanism to a 30-bus transmission network connected to four 10-node DSs and utilized the GUROBI solver in GAMS for solving. The simulation results demonstrate that the engagement of DSs in the SR market reduces the reliance on costly TUs, thereby decreasing system costs by approximately 10%. Furthermore, involving ESSs, EVs, and DR aggregators in the regulation market enhances technical performance and results in a 6.91% reduction in total system costs. This approach provides a robust solution to the evolving challenges posed by RGs and EVs in modern electricity grids.

Performance analysis of machine learning based prediction models in assessing optimal operation of microgrid under uncertainty

Of late, the exponential rise in the global population is driving higher energy demand. However, the rapid depletion of conventional fossil fuels and growing environmental concerns have prompted the evolution of alternative energy sources. To this end, Microgrid (MG) with Renewable Energy Sources (RES) has emerged as popular means of small-scale localized power grid. However, planning of MG operation poses challenges due to the inherent variability and stochasticity in RES power output and energy demand. On account of this, the present study introduces a Stochastic Energy Management Strategy (SEMS) for a grid-connected MG incorporating Micro-Turbine, Fuel-Cell, RES, Battery Energy Storage, and electrical and heat energy demand. The stochasticity of RES is forecasted through a hybrid prediction model (sARIMA-GRU) and the uncertain demand is estimated via 'Monte Carlo Simulation.' The proposed problem is formulated as a dynamic non-linear stochastic optimization problem. It seeks to minimize the expected value of MG operational cost satisfying the practical constraints. Addressing this, a newly developed ‘Artificial Electric Field Algorithm (AEFA)' is utilized. Several case studies are performed to assess MG operation under varied operating conditions. Moreover, the present study analyses the impact of uncertainty on energy contribution from DER, grid dependency, and MG operation cost. Comparative analysis reveals that sARIMA-GRU outperforms other contemporary prediction models. It is noteworthy that the superior prediction accuracy of sARIMA-GRU leads to lower MG operation costs. Moreover, statistical analysis and convergence confirm the proficiency of applied AEFA over state-of-the-art Grey Wolf Optimization and Firefly Algorithm in solving the proposed problem.

Boosting energy markets in grid with a focus on battery storage, including flexible loads and electric vehicles, via a two-stage optimization framework

Abstract This paper introduces a detailed dual-level structure designed for the efficient organization of energy and supportive service markets throughout transport and delivery systems. The framework delineates the organization of energy markets at the first level and supportive services at the second. Traditional thermal units serve as providers for the spinning reserve market, while rapid response generators, energy storage systems, electric vehicles, and demand response aggregators supply capacity adjustments. Simulations applied to a 30-bus transmission network linked to four 8-bus distribution networks show that integrating distribution network resources in the spinning reserve market reduces reliance on thermal units by 22%, decreasing daily operational costs by approximately 15%. Furthermore, including demand response aggregators, storage systems, and electric vehicles in market regulation improves voltage profiles by 7%. The framework is constructed as a linear optimization model and simulated using the CPLEX solver in GAMS software.

Elucidating Gas Evolution of Prussian White Cathodes for Sodium‐ion Battery Application: The Effect of Electrolyte and Moisture

AbstractAs global energy storage demand increases, sodium‐ion batteries are often considered as an alternative to lithium‐ion batteries. Hexacyanoferrate cathodes, commonly referred to as Prussian blue analogues (PBAs), are of particular interest due their low‐cost synthesis and promising electrochemical response. However, because they consist of ~50 wt% cyanide anions, a possible release of highly toxic cyanide gases poses a significant safety risk. Previously, we observed the evolution of (CN)2 during cycling via differential electrochemical mass spectrometry (DEMS), but were unable to determine a root cause or mechanism. In this work, we present a systematical investigation of the gas evolution of Prussian white (PW) with different water content via DEMS. While H2 is the main gas detected, especially in hydrated PW and during overcharge (4.6 V vs. Na+/Na), the evolution of CO2 and (CN)2 depends on the electrolyte conductive salt. The use of oxidative NaClO4 instead of NaPF6 is the leading cause for the formation of (CN)2. Mass spectrometric evidence of trace amounts of HCN is also found, but to a much lower extent than (CN)2, which is the dominant safety risk when using NaClO4‐containing electrolyte, which despite being a good model salt, is not a viable option for commercial applications.

Research on the Application of Joint Optimization Strategy of Energy Storage System and Demand Side Response Based on Tianqun Algorithm in Renewable Energy Grid

Research on the Application of Joint Optimization Strategy of Energy Storage System and Demand Side Response Based on Tianqun Algorithm in Renewable Energy Grid

A novel framework for photovoltaic energy optimization based on supply–demand constraints

Introduction: Distributed power supply has increasingly taken over as the energy industry’s primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources.Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master–slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster.Results: Simulation results show that the proposed framework has better performance under different operating conditions.Discussion: The energy storage operators and numerous energy storage users can implement master–slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win–win conditions.

Carbon Dioxide Energy Storage --- a Novel Energy Storage Technology

Carbon dioxide energy storage (CES) is a novel energy storage developed from compressed air energy storage (CAES), which is suitable for construction of large-scale long-time energy storage systems and demand for sustainable development. Its high energy storage density, low economic cost, and negative carbon emissions make this system could be widely employed. The working principle of the typical CES systems is introduced in this paper. It also discusses the principle of three types of CES systems: Thermo-electrical CES (TE-CES), trans critical and supercritical CES technology, and liquid CES technology. Additionally, two calculation indicators are introduced to depict the relationship between the energy release and storage processes, and show how much energy can be stored in one unit of storage space by the energy storage system, respectively. Finally, it discusses the applications and prospects of the CES system. According to the research, the CES system could be utilized in many fields to provide great operation efficiency and be combined with other technologies to increase reliability and availability.

A distributionally robust optimization approach for optimal load dispatch of energy hub considering multiple energy storage units and demand response programs

The coupling relationship among various energy sources has been consistently reinforced by the advancement of the energy Internet. In the context of modeling multi-energy systems, the energy hub (EH) represents a significant and valuable approach. To attain optimal operation of the EH, this study proposes an optimal load dispatch model for a system that incorporates a combined heat and power (CHP) unit, gas boiler (GB), electric chiller (EC), absorption chiller (AC), heat energy storage (HES) unit, and electrical energy storage (EES) unit. A two-stage distributionally robust optimization (DRO) method that is driven by data is employed to address the uncertainties of electricity prices. Additionally, the proposed two-stage DRO model is effectively solved through the utilization of the column and constraint generation (C&CG) algorithm. Three cases are discussed in the paper with various energy storage units and demand response (DR) settings. The simulation results show that by deploying energy storage units and participating in DR projects, the EH system can reduce the total costs by 2.56 % and 10 %, respectively. Meanwhile, simulation results reveal that the proposed data-driven DRO model can achieve a compromise between the economy and robustness of the scheduling model compared with stochastic optimization (SO) and robust optimization (RO) methods. The simulation results illustrate the effectiveness of the proposed model for ensuring the economical and efficient operation of the system in the face of electricity price uncertainties.

Multi-objective optimal sizing for grid-connected LVDC system with consideration of demand response of electric vehicles

Grid-connected low-voltage direct current (LVDC) systems offer a promising solution for achieving low-carbon buildings. However, a new challenge arises in sizing grid-connected LVDC systems, which is to fully utilize distributed renewable energy, energy storage, and load demand response to minimize both the economy and carbon emissions. To address this gap, this paper studies the sizing of grid-connected LVDC systems, considering the grid-connected converter, photovoltaic (PV), battery, and demand response of electric vehicles (EVs). Firstly, two objective functions are proposed, including the economy and carbon emissions. Secondly, constraints for sizing and operation are presented, with particular consideration given to the discharging of EVs and the corresponding cost of cycle-life loss. Finally, a minimum fuzzy carbon emission method (MFCEM) is proposed to solve the model, which can adaptively and objectively transform the multi-objective into a single objective. According to the case study, the proposed model comprehensively considers the cost and carbon emissions of grid-connected LVDC systems, and its feasibility and validity are verified. Compared with the linear weighting method, MFCEM is a useful method for finding a point on the Pareto front that balances cost and carbon emissions.

Metal oxide- and metal-loaded mesoporous carbon for practical high-performance Li-ion battery anodes

The rapidly expanding Li-ion battery market needs new materials that can satisfy the increasing energy storage demand. Metal oxides and some metals such as tin are viable alternatives to graphite as Li-ion battery anodes, but their low conductivity and large volume change during cycling impose severe challenges that need to be overcome. Confinement of metal oxide and metal nanomaterials within mesoporous carbon (MC) is an effective strategy in this regard, but complex synthesis and nanoparticle aggregation have hindered its application. Herein, we report a facile, scalable and generalized methodology for the room-temperature synthesis of metal oxide and metal nanoparticles within a MC host. The approach has been successfully applied to achieve uniform distribution and prevent aggregation of a large variety of metal oxide and metal nanomaterials in MC support. These nanocomposites were screened as Li-ion battery anodes, and the optimal candidates were shown to be superior to previously reported systems. Our synthesis method was scaled up using commercial MC. The nanocomposites were validated in a high-loading electrode (4 mg/cm2) with a practical voltage range (< 2 V vs. Li+/Li), showing impressive initial Coulombic efficiency (> 100%), and excellent stability (∼ 500 mAh/g after 250 cycles at 0.2 A/g), which was 1.5–2x better than commercial graphite at the same testing conditions. The facile nature, universality and versatility of our approach make it possible to load various metal oxide and metal nanomaterials within different types of MC. These nanocomposites would be of significant interest to different fields, such as energy storage and conversion, sensing, and catalysis.

CoSe2@ZnS microsphere arrays with remarkable electrochemical performance for hybrid asymmetric supercapacitor

This study presents a simple hydrothermal synthesis method for preparing CoSe2, ZnS, and their nanohybrid combinations (ZnS-70 % CoSe2 and ZnS-50 % CoSe2, referred to as ZC-1 and ZC-2) for the first time. These materials are intended for application in a supercapacitor (SC) and are synthesized through an ex-situ wet-chemical approach. The research thoroughly examines the electrochemical properties of as-prepared electrodes and how they relate to their structure and morphology to understand their charge storage mechanism better. The detailed characterization reveals that a one-step hydrothermal reaction obtains the cubic crystal structure of the ZnS with microsphere morphology, and the CoSe2 presents a snow crystals-like morphology with a highly smooth and transparent surface appearance. The systematic electrochemical examination declares the typical pseudocapacitive charge storage response owing to fast Faradaic redox reactions with good reversibility and rate capability. Notably, a high capacitance of CoSe2-ZnS nanohybrid (denoted as ZC-2) is realized by the synergistic effect between ZnS and CoSe2 supported by the impedance, cyclic voltammetry, and charge/discharge studies. High specific energy with a power of 46.71 Wh/kg (899.21 W/kg) and 9 Wh/kg (5400 W/kg) is realized by adding an optimal voltage and capacitance simultaneously by fabricating a hybrid asymmetric HASCs (ZC-2||AC HASC) in aqueous solution. This study believes that metal sulfide and selenide-based nanohybrids can be effective candidates for competing in the growing electrochemical energy storage demand.