Performance Of Energy Storage System Research Articles

Construction of electrochemical model for high C-rate conditions in lithium-ion battery based on experimental analogy method

Lithium-ion batteries (LIBs) modeling is critical for the safe and efficient operation of electric vehicles (EVs) and energy storage systems (BESSs). Most electrochemical models are mainly suitable for normal temperature or low C-rate conditions (≤2 C). Meanwhile, the electrochemical model parameters are usually obtained by the half-cell testing method. This method has difficulty in disassembling batteries and obtaining model parameters quickly. To solve this problem, the internal electrochemical behavior of LIBs under high-C rate conditions is first studied in this paper. Through the above theoretical analysis, the relationship between the solid phase diffusion coefficient, the reaction rate constant and temperature/concentration must be considered under high C-rate conditions. Then a fast calibration method for electrochemical model parameters is proposed, and a variable parameter high C-rate model is established. Finally, the variable parameter high C-rate model is validated at different rates of 5 °C, 25 °C and 55 °C. The results show that the mean absolute errors (MAE) of the variable parameter high C-rate model are within 13 mV under low C-rate conditions. Under the high C-rate condition (≤6 C), the MAEs of the model are within 50 mV.

Room-temperature synthesis of 2D-Ti3C2Tx nano-sheets by organic base treatment.

The growing demand for improved electrochemical performance in energy storage systems has stimulated research into advanced two-dimensional (2D) materials for electrodes. In this work, we obtain a layered MXene compound by exfoliating a titanium aluminum carbide precursor using tetramethylammonium hydroxide (TMAOH) ions in a full room temperature process followed by manual shaking. The hexagonal crystal structure and composition of the layered materials are characterized using different techniques. X-Ray diffraction shows the formation of 2D nano-sheets before and after the TMAOH treatment via its characteristic (002) diffraction peak, bringing to light an increase in the interlayer spacing after treatment. Scanning electron microscopy images confirm the layered morphology, whose composition is determined by energy dispersive x-ray analysis for the bulk material and by x-ray photoelectron spectroscopy for the surface of the obtained compounds. This study demonstrates a promising route to enhance delamination of this MXene 2D material in a low-cost room-temperature approach.

A Multi-objective Control Strategy of the Energy Storage System in Distribution Networks with a High Proportion of Renewable Energy

With the development of battery technology and power electronic technology, battery-based energy storage has been widely used in Peak Shaving and Valley Filling frequency and voltage regulation for the power system with a high proportion of renewable energy integration. However, the existing control strategies of the energy storage system (ESS) mostly take peak shaving and valley filling frequency and voltage regulation as a single control objective, causing the rest capacity of the energy storage in the single control working period to be wasted, which significantly reduces the utilization rate and economic benefits of energy storage. How to improve the utilization rate and economic benefits of the ESS while enhancing the support performance of ESS has not received much attention. This paper proposes a multi-objective control strategy of ESS to maximize energy storage’s benefit and ensure the distribution network’s safe and stable operation. First of all, this paper combines the auxiliary compensation benefits of the price system of distributed energy storage power stations and proposes a comprehensive index that comprehensively considers the economy of the ESS and the stable operation of the distribution network to support the evaluation needs of multi-objective control of energy storage. On this basis, this paper predetermines the multi-layer judgment logic for ESS operation control under different operating conditions, divides the ESS into different operating conditions according to the control objectives, and proposes a multi-objective control strategy based on variable droop coefficients. It effectively utilizes the idle capacity of the ESS and achieves an optimized control of energy storage that takes into account both technology and economy. The simulation verification based on the energy storage model built in Matlab/Simulink is conducted to validate the effectiveness of the proposed ESS multi-objective control strategy.

A control method combining load prediction and operation optimization for phase change thermal energy storage system

Due to the high latent heat and load-shifting capacity, phase-change thermal energy storage technology is an effective way to reduce energy costs under time-of-use electricity pricing. A favorable operation strategy is essential to exploit the advantage of the phase change thermal energy storage system. Previous studies on the operation strategy lack consideration of load prediction, which could reduce the matching degree of heat supply and demand. This study proposed a control method combing load prediction and operation optimization based on an electric boiler-phase change thermal energy storage heating system. A deep learning-based heating load prediction model was built; on this basis, an operation optimization method using dynamic programming was formulated subsequently. The proposed method was applied to a case building located in Beijing, China. By applying the proposed control method, the energy costs of the system were saved by 10% compared to the original operation strategy in the whole heating season, and the matching degree of heat supply and demand could be close to 100%, demonstrating that the method can provide users with cheaper and the best thermally comfortable heating. The proposed method is capable of achieving optimal operation of a phase change thermal energy storage system in practical applications.

An energy storage allocation method for renewable energy stations based on standardized supply curve

The goal of carbon emission peak and carbon neutrality requires China to vigorously develop renewable energy. However, renewable energy has obvious randomness and volatility. Therefore, it is necessary to configure energy storage systems for renewable energy stations to ensure the safe and stable operation of power systems. Given the problem of energy storage system configuration in renewable energy stations, it is necessary to consider the system load characteristics and design appropriate principles to formulate planned output curves for renewable energy stations, so that the joint output curve of renewable energy and energy storage system can match the system load curve as much as possible, which will reduce the system peak shaving pressure and promote the utilization of renewable energy. This paper first considers the impact of renewable energy stations with the different installed scales on the power system and designs the standardized supply curves differentially, and defines the supply curve deviation index to characterize the difference between the renewable energy–energy storage system joint output curve and the standardized supply curve. Then, to minimize energy storage system investment costs and supply deviation costs, an optimization model for energy storage system configuration in renewable energy stations is established, and output deviation control constraints are set to ensure that the operation of energy storage systems conforms to actual conditions. Finally, case studies analyze the energy storage system configuration results and the typical scenario operation results of a single renewable energy station and a renewable energy power generation base, which verify the rationality and effectiveness of the method proposed in this paper.

Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates

Efficient operation of battery energy storage systems requires that battery temperature remains within a specific range. Current techno-economic models neglect the parasitic loads heating and cooling operations have on these devices, assuming they operate at constant temperature. In this work, these effects are investigated considering the optimal sizing of battery energy storage systems when deployed in cold environments. A peak shaving application is presented as a linear programming problem which is then formulated in the PYOMO optimization programming language. The building energy simulation software EnergyPlus is used to model the heating, ventilation, and air conditioning load of the battery energy storage system enclosure. Case studies are conducted for eight locations in the United States considering a nickel manganese cobalt oxide lithium ion battery type and whether the power conversion system is inside or outside the enclosure. The results show an increase of 42% to 300% in energy capacity size, 43% to 217% in power rating, and 43% to 296% increase in capital cost dependent on location. This analysis shows that the heating, ventilation, and air conditioning load can have a large impact on the optimal sizes and cost of a battery energy storage system and merit consideration in techno-economic studies.

Two-timescale coordinated operation of wind-advanced adiabatic compressed air energy storage system: A bilevel stochastic dynamic programming method

Renewable energy is a promising solution to address the energy crisis and environmental issues, but it comes with challenges due to its inherent volatility and limited dispatchability. Advanced adiabatic compressed air energy storage (AA-CAES) is a favorable partner for centralized renewable integration, due to its numerous benefits, such as large capacity, long lifetime, fast response capability, and zero carbon emissions. For the economic management of a wind-AACAES system, a battery-like AA-CAES dispatch model is proposed, where the charging/discharging efficiencies and capacities are dependent on the operating power and state-of-charge (SoC) to capture the part-load characteristics. A hybrid control strategy is proposed to enhance the flexibility and efficiency of the compression side under off-design conditions caused by simultaneous changes in back pressure and load. To provide dispatch and control strategies of AA-CAES, a multi-timescale optimization problem is established. The slow timescale determines the baseline output scheduling on an hourly basis to maximize total revenue, while the fast timescale updates the real-time adjustment every five minutes to minimize the penalty of tracking error. A bi-level stochastic dynamic programming (SDP) framework is formulated to incorporate the wind uncertainties and multi timescale coordination, where terminal SoCs and value functions are essential to integrate decisions across slow and fast timescales. A non-iterative parametric programming-based method is proposed to solve the slow-timescale SDP, and a simulation-based rollout algorithm is applied to extract the fast-timescale actions, which yields evident improvement over any heuristic base policy that is sequentially consistent. The numerical results demonstrate that the proposed method has a performance gap of approximately 7.1%, which outperforms the rule-based method by 32.6%. The installation of AA-CAES improves the total revenue by 11.9%. Additionally, the hybrid control strategy enhances the charging flexibility and efficiency by 30.7% and 4.3%, respectively, resulting in a 3.9% reduction in tracking deviation penalty.

A Review of Mathematical Models of Energy Storage Systems for Electric Power Systems Simulation. Part II

Currently the energy storage system (ESS) has become the development focus in the electric power systems (EPS) with the renewable energy power generation. At the same time, high penetration levels of ESS leads to a change the dynamic properties of the EPS. Accordingly, the analysis of the specifics of ESS operation becomes necessary for effective solution the problems of designing and operating EPS with ESS. Since mathematical simulation level is the main way to obtain the indicated information, the task of the adequacy of approaches and methods for modeling a processes in the ESS as part of the EPS becomes relevant. In the first part of the article, detailed mathematical models of the main elements of the ESS were considered. An analysis of mathematical models of ESS with different detailization level, depending on the type of energy storage device and a number of other factors, are presented within the framework of the second part of the article. The article also provides an overview of the approaches used to simplify the ESS models and their mathematical description. The areas of application of these models are considered. In addition, an analysis of the limitations and disadvantages associated with the simplification of models are presented. The article is an overview and can help in choosing an appropriate mathematical model of the ESS for solving a required designing and operating tasks.

Influence of the transient operation of a large-scale thermal energy storage system on the flexibility provided by CHP plants

Among many technical options to improve the flexibility in combined heat and power (CHP) plants, thermal energy storage (TES) has attracted the most attention with its high applicability and benefit. Previous studies normally adopted a simplified approach for modelling a TES system, which assumes the optimized charged or discharged rate of heat can always be realized within the rated capacity. However, this may yield unfeasible results as the charging and discharging rates are dependent on the dynamic status of a TES, such as the state of charge (SOC) and water temperature, and the water flowrate for charging and discharging. In order to consider the transient operation of a TES, a 1D dynamic model was developed and validated against measured data from a real CHP plant. To analyze the dynamic performance of a TES, two key performance indicators (KPI), the maximum charging/discharging rate (C/D-ratemax) and the constant maximum charging/discharging rate that can be maintained constantly for one hour (CC/CD-ratemax) were employed. By doing simulations, it has been found that the CC/CD-ratemax was lower than the C/D-ratemax for most given SOCs of the studied TES. The developed model was also used to examine the optimized operation of a TES for providing flexibility. Some unfeasible results have been identified, as the optimized hourly charging/discharging rates were constrained by the CC/CD-ratemax. Therefore, it is of great importance to integrate a detailed dynamic model when optimizing the dispatch of electricity and heat for a CHP plant.

A hybrid approach to predict battery health combined with attention-based transformer and online correction

The lithium-ion battery is an important part of green energy systems, and battery aging will lead to the degraded performance of energy storage systems (ESSs). Therefore, accurate battery health prediction is crucial to guarantee the safe and efficient operation of ESSs. This paper proposes a hybrid battery health prediction method that fuses Transformer and online correction. First, the attention-based Transformer is taken as a global model to establish the nonlinear relationship between measured data and battery capacity decline. Second, a local model based on unscented particle filter is developed for the online correction of Transformer outputs. To characterize battery degradation behavior as much as possible, multi-scale health features are considered, including time-series and statistical features extracted from partial charging curves and operating data distributions, respectively. Then, feature dimension reduction is performed based on the maximum information coefficient method. To handle both types of features, a special filter layer is carefully designed in Transformer. Compared with the state-of-the-art algorithms, the proposed method achieves optimal health prediction performance with minimal computational resources for batteries with different aging conditions. When only 20% of the cell data is used for training, the predicted root mean square error can still be guaranteed to be within 0.72%.

Evaluation of the limiting conditions for operation of a large electrochemical energy storage system

In the last decade, a considerable role has been given to the development of renewable energy sources (Gielen et al., 2019) [1]. However, the unsteadiness of the wind load and insolation is a significant constraint for the transition to non-conventional renewable energy sources (NCRES) - wind turbines and solar panels (Barra et al., 2021) [2]. The use of energy storage systems (ESS) is a necessary factor in the energy transition (Ademulegun et al., 2021) [7]. However, the electrical energy transfer from typical electrochemical energy storage devices to the consumer is accompanied with the dissipation of part of this energy as heat (Henke et al., 2020) [26]. For this reason, it is advisable to assess the overheating of such storage devices, which may lead to a significant decrease in capacitive and resource characteristics (Collath et al., 2022) [43], and in some cases even to ignition and explosions (Murarka et al., 2022) [44]. The article defines the limiting conditions for the operation of electrochemical energy storage devices in a typical autonomous local energy system.

Economic Optimal Coordinated Dispatch of Power for Community Users Considering Shared Energy Storage and Demand Response under Blockchain

In recent years, user-side energy storage has begun to develop. At the same time, independent energy storage stations are gradually being commercialized. The user side puts shared energy storage under coordinated operation, which becomes a new energy utilization scheme. To solve the many challenges that arise from this scenario, this paper proposes a community power coordinated dispatching model based on blockchain technology that considers shared energy storage and demand response. First of all, this paper analyzes the operating architecture of a community coordinated dispatching system under blockchain. Combined with the electricity consumption mode of communities using a shared energy storage station service, the interactive operation mechanism and system framework of block chain for coordinated dispatching are designed. Secondly, with the goal of minimizing the total cost of coordinated operation of the community alliance, an optimal dispatching model is established according to the relevant constraints, such as the community demand response, shared energy storage system operation and so on. Thirdly, the blockchain application scheme of community coordinated dispatching is designed, including the incentive mechanism based on the improved Shapley value allocation coordination cost, and the consensus algorithm based on the change rate of users’ electricity utilization utility function. Finally, the simulation results show that the proposed community coordinated dispatching strategy in this paper can effectively reduce the economic cost, reduce the pressure on the power grid, and promote the consumption of clean energy. The combination of the designed cost allocation and other methods with blockchain technology solves the trust problem and promotes the innovation of the power dispatching mode. This study can provide some references for the application of blockchain technology in user-side energy storage and shared energy storage.

State of Health Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy

Energy storage is an important technical means to increase the consumption of renewable energy and reduce greenhouse gas emissions. Electrochemical energy storage, represented by lithium-ion batteries, has a promising developmental prospect. The performance of lithium-ion batteries continues to decline in the process of application, and the differences between batteries are increasing. Therefore, accurate estimation of the state of health (SOH) of batteries is the key to the safe and efficient operation of energy storage systems. In this paper, the electrochemical impedance spectroscopy (EIS) characteristics of Li-ion batteries under different states of charge and health were studied. Three groups of Li-ion battery impedance module values under different frequencies were selected as characteristic parameters, and the SOH estimation model of Li-ion batteries was built by using the support vector regression (SVR) algorithm. The results show that: the model with the second group of frequency-point combinations as characteristic parameters takes into account both accuracy and efficiency; the cumulative time of the characteristic frequency test and SOH evaluation of lithium-ion batteries is less than 10 s; and this technology has good engineering application value.

THESEUS: A techno-economic design, integration and downselection framework for energy storage

Optimal selection of energy storage technologies is critical to ensure reliable integration of intermittent and often uncertain renewable energy in electricity grids. The consideration of a diverse set of energy storage technologies is required for a more sustainable deployment of energy storage. We present THESEUS (TecHno-Economic framework for Systematic Energy storage Utilization and downSelection), which is a comprehensive framework for the optimal selection, design and operation of energy storage systems. THESEUS includes rigorous models of major energy storage technologies at different maturity levels, such as thermal storage using phase-change materials or molten salt, cryogenic storage, mechanical storage in the form of compressed air and pumped hydro storage, chemical storage using hydrogen, and electrochemical storage in the form of lithium-ion, sodium sulfur and vanadium flow batteries. An illustrative case study on state-wide prospective energy storage shows that storage integration with fossil power plants could reduce the cost of meeting the grid energy demand by 20%, with mechanical storage as the best suited technology. Although high-temperature thermal storage has low storage efficiency, it is optimal for integration with renewable energy plants. In addition, Li-ion batteries are optimal for high ramping but low storage duration requirements. Such insights can enable the deployment of both existing and emerging energy storage technologies to facilitate a smooth transition to a clean energy future.

Design and Modelling of Heat-Coupled Storage System with High- and Low-Pressure Bypass: Electrothermal Characteristics and Peak Regulating Performance

To achieve a balance between supply and demand during cogeneration system operation, it is necessary to improve the peak regulation capacity and regulatory flexibility of the unit. Considering the excellent performance of energy storage systems, a heat-coupled storage system with high- and low-pressure bypass is proposed to increase peak regulation capacity. Employing a 300 MW heating unit as the research object, thermal system models of a traditional-pumping steam-heating system, a high- and low-pressure bypass heating system, and a coupled system were built using Aspen Plus software. The electric heating characteristics of the three systems, as well as the peak regulation capacity and peak regulation depth of the coupled system, were analysed under different storage and heat release loads. Results indicate that the high- and low-voltage bypass system and the coupled system both improve the peak capacity and control flexibility of the unit. Moreover, the coupled system has a greater influence on the maximum thermoelectric ratio and minimum charge rate than the high- and low-voltage bypass heating system, thereby extending the range of safe operation. The peak capacity and depth of heat storage are 65.55 MW and 21.85%, respectively, while the peak capacity and the depth of the heat-release process are 39.32 MW and 13.10%.

Hierarchical Intelligent Operation of Energy Storage Systems in Power Distribution Grids

High penetration of distributed energy storage systems (ESS) offers an unparalleled opportunity to reinforce the distribution grid at the local level against upstream disruptions; however, their mass operation under uncertainty of load and renewable generation is computationally expensive. While deep reinforcement learning (DRL) has been suggested to train operator agents capable of handling uncertainty and high dimensionality of the problem, it falls short when safety and feasibility assurances are required in critical operations. This paper proposes a model for hierarchical coupling of DRL and mathematical optimization for operation of ESS in distribution grids, in order to take advantage of DRL fast response while keeping network constraints in check. In the proposed method, strategic scheduling of distributed ESS units are performed locally by fast DRL-trained agents, while critical grid-wide operations such as fault management and voltage control are performed by an optimization-based central controller. The local controller is trained by Twin Delayed Deep Deterministic Policy Gradient (TD3), whose response time is three orders of magnitude faster than stochastic optimization, while the optimality of solutions are similar in both cases.

Analyzing performance of energy storage system during discharging with loading nanoparticle for enhancing the treatment of water

Current article is about the solidification process of water through the complex container. The geometry has extended cold surfaces with complex shapes. The base PCM was mixed with nano-powders to enhance the thermal feature of water. The final model for presentation of freezing contains two equations for predicting the values of temperature and solid fraction. The forms of equations are unsteady and Galerkin method applied for solving them. As volume fraction of alumina enhances, the freezing rate enhances around 41.2 %. With reducing the concentration from 0.04 to 0.02, the freezing time augments about 24.09 % and this percentage for the middle size of particles is 5.3 times greater than that of the biggest powders. The increase of diameter up to 40 nm makes the period to drop by 19.97 %.

Online fusion estimation method for state of charge and state of health in lithium battery storage systems

To ensure the safe and reliable operation of Li-ion battery energy storage systems, it is important to diagnose the operational status and aging degree of the batteries. In this study, an online fusion estimation method based on back propagation neural network and genetic algorithm (BP-GA) is used for estimating the state of charge (SoC) and state of health (SoH) of Li-ion batteries. First, the effective features of SoC and SoH of Li-ion batteries during charging and discharging are analyzed, and the relevant features are extracted. Subsequently, a conventional back propagation neural network (BPNN) for SoC and SoH estimation is described. The extracted feature quantities are then used as inputs to the neural network for the estimation of the SoC and SoH of Li-ion batteries. A comparison of the SoC and SoH estimation results using, respectively, a single BPNN and a fusion algorithm using a genetic algorithm (GA) to optimize the BPNN shows that the accuracy and efficiency of the estimation results using BP-GA are significantly higher than that of the BPNN. The experimental component of this paper is a comparison experiment using the Panasonic 18650PF and the real-world data from NASA. The results show that the feature extraction operation is relatively easy to perform and combined with the neural network method used in this paper can have good accuracy, reduce the complexity of the algorithm, and improve the detection efficiency.

Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators

Even though generating electricity from Renewable Energy (RE) and electrification of transportation with Electric Vehicles (EVs) can reduce climate change impacts, uncertainties of the RE and charged demand of EVs are significant challenges for energy management in power systems. To deal with this problem, this paper proposes an optimal energy management method using the Virtual Power Plant (VPP) concept for the power system considering solar PhotoVoltaics (PVs) and Electric Vehicle Charging Stations (EVCS). The Differential Evolution (DE) algorithm is applied to manage energy in the power system to minimize the operation cost of generators and degradation costs in Energy Storage Systems (ESS) and generators. The effectiveness of the proposed approach is examined and tested on the IEEE 24 bus Reliability Test System (RTS 24) using the MATPOWER tool on the MATLAB program for calculating Optimal Power Flow (OPF). In this study, two situations before and after applying the proposed method are considered. The simulation results demonstrate that a branch constraint violation occurs before using optimal energy management using the VPP concept. In order to solve this issue, the DE algorithm for optimal energy management using the VPP concept is applied by dividing the studied case into two subcases as follows. For the first subcase, two objectives consisting of the minimization of the generator operation cost and the minimization of the battery degradation cost in ESS are considered. In the second case, three objectives comprising the two mentioned objectives with the minimization of generator degradation cost are considered. The results demonstrate that branch constraint violations can be avoided by applying optimal energy management using the VPP concept. This study also suggests considering the generator degradation cost in the objective function, which can minimize the total costs by 7.06% per day compared with the total cost of the first case.

FEATURES OF ORGANIZATION AND USE OF ENERGY STORAGE SYSTEMS IN DISTRIBUTION NETWORKS

The article analyzes the features and formulates recommendations for the most effective use of the energy storage systems in electrical distribution systems and microgrids in the context of expanding the use of local renewable energy sources. Information about various energy storage technologies is summarized and recommendations are formulated regarding their best applying for various applications in the electric power industry. The tasks that can and should be solved by energy storage systems in distribution networks are considered, as a result of which the expediency of forming hybrid storage systems is substantiated. Based on the analysis of the bibliography and consideration of international experience, the main problems have been identified that require further research for a reasonable choice of the structure, parameters, locations and modes of operation of hybrid energy storage systems, taking into account the specifics of the structure and operation of domestic electric distribution systems with local energy sources. Under the implementation of hybrid energy storage systems, a fundamentally new problem of determining the optimal parameters of individual components of such systems arises, which was practically not covered in published works on this issue. Additionally, difficulty in solving this task is the need for its complex solution, since, firstly, the parameters of the individual components of the hybrid energy storage system are most likely interdependent, and secondly, it is necessary to take into account the involvement of the storage systems in the solution of a number of optimization problems inherent in the electrical distribution systems. In order to facilitate the use of energy storage devices, it is advisable to create a comprehensive standard that allows you to evaluate and compare the quality and performance of different technologies, helps energy storage users justify the type and parameters, as well as optimal placement for maximum benefit.