Performance Of Energy Storage System Research Articles

Multi-timescale rolling optimization dispatch method for integrated energy system with hybrid energy storage system

Integrated energy system is an important approach to promote large-scale utilization of renewable energy. Under the context of energy market reformation and technology advancement, the economic operation of integrated energy system confronts new challenges, in terms of multiple uncertainties, multi-timescale characteristics of heterogeneous energy, and coordinated operation of hybrid energy storage system. To this end, this paper investigates the multi-timescale rolling optimization of integrated energy system with hybrid energy storage system considering the above challenges. Firstly, a basic framework of an integrated energy system with hybrid energy storage system (consisting of battery and hydrogen storage) is proposed, and the typical devices are modeled in detail. Secondly, the parameters and variables are divided into fast/slow timescale according to dispatch needs, and the multi-timescale problem of heterogeneous energy and the coordinated operation of the hybrid energy storage system can be solved simultaneously through two-stage optimization. Thirdly, the dispatch model is incorporated into the framework of model predictive control, the uncertainty of price, renewable energy and load can be effectively handled. Finally, a case study is conducted using an industrial park in the southern coastal region of China, the comparison with the rule-based method shows 60.65% reduction in the operating cost of hybrid energy storage system, the comparison with robust optimization shows 7.01% reduction in the total cost of the system, thus the feasibility and effectiveness of the proposed method are verified.

Optimal battery energy storage planning and control strategy for grid modernization using improved genetic algorithm

The flexible operation of battery energy storage systems (BESS) to support electricity grid modernization requires optimal planning and an efficient control strategy. This paper proposes the optimal allocation of BESS with photovoltaic systems for microgrids to enhance grid reliability and flexibility. An adaptive BESS control strategy is developed to allow the BESS to operate in both redundant and peak shaving modes. An evolutionary programming-based genetic algorithm implemented with an optimal power flow control is proposed to determine the BESS’s optimal location, sizing, and charge/discharge operation. The objective function is to maximize the benefits of energy loss reduction and peak shaving enhancement while minimizing the installation cost. The proposed approach is conducted with MATLAB and DIgSILENT PowerFactory software, which uses an intelligent automatic data exchange process to solve the optimal solution. A practical 22 kV grid-connected microgrid of Thailand is used as a test system to demonstrate the effectiveness of the planning and control strategy for the BESS installation. The simulation test results show that the proposed optimal BESS allocation and control strategy can significantly reduce the microgrid’s energy loss and peak demand and increase energy shaving leading to enhanced grid resiliency and reliability.

Thermal Analysis and Optimization of Energy Storage Battery Box Based on Air Cooling

For energy storage batteries, thermal management plays an important role in effectively intervening in the safety evolution and reducing the risk of thermal runaway. Because of simple structure, low cost, and high reliability, air cooling is the preferred solution for the thermal management. Based on a 50 MW/100 MW energy storage power station, this paper carries out thermal simulation analysis and research on the problems of aggravated cell inconsistency and high energy consumption caused by the current rough air-cooling design and proposes the optimal air-cooling design scheme of the energy storage battery box, which makes the structure of battery module more reasonable and the temperature distribution between the batteries more uniform. It has a certain guiding significance for energy saving, consumption reduction, and stable operation of energy storage systems.

On full-life-cycle SOC estimation for lithium batteries by a variable structure based fractional-order extended state observer

Accurate SOC estimation of lithium batteries are crucial for the efficient operation of new energy storage systems. During the ageing of the battery, structure and parameters of the battery model, especially internal resistance, may change, which has a particularly significant impact on the accuracy of the model. For this reason, this paper proposes a SOC estimation method based on the extended state observer of the variable structure fractional order model. Firstly, an adaptive method for the structure and parameters of fractional order model through distribution of relaxation times (DRT) is proposed on full-cycle-life of lithium battery. The DRT is extracted from the Electrochemical Impedance Spectroscopy (EIS) of the lithium battery. The order and the initial parameters of the fractional order model of the lithium battery is determined by the characteristics of DRT during the ageing process of the lithium battery. Adaptive adjustment of model is realized by parameter identification combining with time domain data. Then, a fractional-order extended state observer is proposed to estimate SOC by treating internal resistance as an extended state, thus realizing online estimation of internal resistance uncertainty. The Lyapunov stability analysis proves that the estimation error of the observer is uniformly ultimately bounded. Finally, the experimental simulation analysis shows that the accuracy of the second-order model is significantly improved compared with the first-order model, and the accuracy improvement of the third-order model is limited compared with the second-order model. The MAE of the proposed algorithm is as low as 0.73%.

An in-Situ Study of the Transformation of Hybrid Layered Hydroxides into Metallic Nanocomposites with Interest in Energy Storage Systems

The high increase of energy demand in recent decade and the necessity of reduce the fossil fuel consumption have triggered the interest of the scientific community in the search of 'green' alternatives. Due to the characteristic intermittence of solar and wind technologies, there is a huge effort on the development energy storage systems. Among them hybrid capacitors (HCs) are promising systems due its high energy and power density values, in comparison with Li-ion batteries, that make them ideal for industrial and automobile applications. Battery-like materials such as nanocomposites (NCs) based on metallic nanoparticles (working as pseudo-capacitor based in redox reactions) with a carbon matrix (working as electric double layer capacitors) are key HCs systems itself. These NCs exhibits a notorious enhancement on their electrochemical performance by applying an external magnetic field. [1] Typically, NCs are obtained by the thermal decomposition of hybrid materials under inert atmosphere, as it has been demonstrated for the case of layered double hydroxides (LDHs), layered hydroxides (α-LHs) and metal organic frameworks (MOFs). [1-3] However, there is a lack of information in terms how the thermal transformation takes places.In this work, we present for the first-time an in-situ study about the transformation of hybrid layered hydroxides into NCs. For that, synchrotron measurements of X-ray absorption spectroscopy (XAS) and powder X-ray diffraction (PXRD) are performed for different hybrid layered hydroxides to follow the transformation in terms of oxidation state and crystallinity. These hybrid layered hydroxides will variate on: (i) the layered hydroxide structure and the anion-layer interactions (LDHs vs α-LHs + electrostatic vs covalent), (ii) the cation nature, and (iii) the identity of the organic linkers, to name a few. For instance, in the case of α-LHs, while the LHs structure defines the thermal decomposition, and the organic molecule length controls the quality and quantity of the final carbon matrix at the NCs.[4] Interestingly, in function of the amount of carbon present on the organic molecule, the oxidation state and the formation of the carbon matrix on the final material are conditioned, which imply that there is a minimum amount of carbon to obtain the NCs. In the other hand, for the previous NCs obtained, the electrochemical characterization is performed in pouch cells, applying an external magnetic field throughout the galvanostatic cycles. This assembly prevents the loss of material along the time and during the process, where it is evidenced how the magnetic field improves the electrochemical performance with an increase in the specific capacity of the NCs. In overall, this work introduces fundamental knowledge on the rational design of hybrid layered hydroxides and the subsequent nanocomposites and its performance in energy storage systems.

Degradation Prediction and Cost Optimization of Second-Life Battery Used for Energy Arbitrage and Peak-Shaving in an Electric Grid

With the development of the electric vehicle industry, the number of batteries that are retired from vehicles is increasing rapidly, which raises critical environmental and waste issues. Second-life batteries recycled from automobiles have eighty percent of the capacity, which is a potential solution for the electricity grid application. To utilize the second-life batteries efficiently, an accurate estimation of their performance becomes a crucial portion of the optimization of cost-effectiveness. Nonetheless, few works focus on the modeling of the applications of second-life batteries. In this work, a general methodology is presented for the performance modeling and degradation prediction of second-life batteries applied in electric grid systems. The proposed method couples an electrochemical model of the battery performance, a state of health estimation method, and a revenue maximization algorithm for the application in the electric grid. The degradation of the battery is predicted under distinct charging and discharging rates. The results show that the degradation of the batteries can be slowed down, which is achieved by connecting numbers of batteries together in parallel to provide the same amount of required power. Many works aim for optimization of the operation of fresh Battery Energy Storage Systems (BESS). However, few works focus on the second-life battery applications. In this work, we present a trade-off between the revenue of the second-life battery and the service life while utilizing the battery for distinct operational strategies, i.e., arbitrage and peak shaving against Michigan’s DTE electricity utility’s Dynamic Peak Pricing (DPP) and Time of Use (TOU) tariffs. Results from case studies show that arbitrage against the TOU tariff in summer is the best choice due to its longer battery service life under the same power requirement. With the number of retired batteries set to increase over the next 10 years, this will give insight to the retired battery owners/procurers on how to increase the profitability, while making a circular economy of EV batteries more sustainable.

A collaborative operation mode of energy storage system and train operation system in power supply network

A collaborative operation mode of energy storage system and train operation system in power supply network

Optimized Power and Capacity Configuration Strategy of a Grid-Side Energy Storage System for Peak Regulation

The optimal configuration of the rated capacity, rated power and daily output power is an important prerequisite for energy storage systems to participate in peak regulation on the grid side. Economic benefits are the main reason driving investment in energy storage systems. In this paper, the relationship between the economic indicators of an energy storage system and its configuration is first analyzed, and the optimization objective function is formulated. Then, according to the objective limitations of the energy storage system configuration and operation, the constraints are formulated. A set of typical parameters is selected, and the CPLEX (IBM ILOG CPLEX Optimization Studio) solver is used in MATLAB to solve the optimal configuration results. Several sets of optimization results are obtained by taking different subjective coefficient β values, and the economic and social benefits of the optimization results are analyzed. When the economic benefits of the energy storage system are more important, the value of β needs to be smaller, such as a value of 1000. Conversely, when the peak-regulation effect is more important, the value of β should be larger. Finally, configuration results of the control groups are given, and the effect of optimizing the calculation for improving economic benefits is verified.

A Super-twisting SMC Design for Bidirectional DC–DC Converters in Electric Vehicle Applications

Many industrial and electrical systems, such as electric vehicles, employ DC/DC converters. Since DC/DC converters are nonlinear and time-varying systems, it is inappropriate to regulate them using linear control techniques. In this paper, a super-twisting sliding mode control (STSMC) is applied for bidirectional DC-DC converter in electric vehicle applications. The traction chain of the electric vehicle consists of a battery-supercapacitor energy storage system (ESS) linked in parallel to the DC bus through two bidirectional DC–DC converters and a synchronous reluctance motor drive (SynRM). The benefit of sharing power in an HPS, which is primarily made up of a supercapacitor and battery, is the ability to use the strengths of both sources, with the former having high specific energy and the latter having high specific power. The on-board energy storage system's performance is enhanced by this hybridization in terms of cost, mass, and charging time. In addition to the proposed control, the hybrid power system (HPS) needs a strategy for routing energy between the two sources and the motor. This strategy is based on frequency decoupling (DFS), which protects the battery from peak current during the acceleration and deceleration phases. The objective of using the control law based on super-twisting sliding mode control (STSMC) is to follow the reference trajectories in finite time with high precision, very good robustness, and a reduction in chattering. Using the MATLAB/Simulink software, a thorough simulation of an electric vehicle was performed in order to confirm the effectiveness of the suggested control approach and assess the effectiveness of the source's energy management strategy. This control method ensures a reduction in the bus voltage stress (Δv=5V) and improves the quality of the power. The overshoot voltage for a load power of 21 kw is reduced to 15V (3.2%) compared to the linear control techniques, improving the system's robustness and reliability..

A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism

This paper proposes a hybrid energy storage system model adapted to industrial enterprises. The operation of the hybrid energy storage system is optimized during the electricity supply in several scenarios. A bipolar second-order RC battery model, which can accurately respond to the end voltage, (State of charge) SOC, ageing mechanism and other characteristics of the battery, is established. The batteries and the supercapacitor consist of a hybrid energy storage system. The system operation cost and the battery cycle life are investigated. This paper realizes energy scheduling through load prediction technology. The proposed energy scheduling strategy plans the operation of the hybrid energy storage system and reduces the frequency of the battery's charging and discharging. The results show that the proposed prediction model keeps the hybrid energy storage model's overall electric load prediction accuracy up to 97.12%–98.89%. Combining the load prediction technique with the optimal scheduling strategy, the decay of lithium battery capacity of 120kwh to 96.16kwh is better than the decay of battery capacity of 120kwh to 87.32kwh under no scheduling strategy set. The total economic cost per quarter is reduced by $20,000-$35,000.

A Novel Adaptive Power Smoothing Approach for PV Power Plant with Hybrid Energy Storage System

Clouds passing over solar photovoltaic (PV) power system causes power fluctuations, which contributes to power quality issues. Power fluctuations are usually compensated by an energy storage system (ESS) integrated with a filtering or smoothing controller. However, there is a great burden to tune and determine the filtering time constant (TF) that leads to better alleviation of PV power fluctuations because the irradiance variability varies from day to day. Lack of cloud information leads to incorrect selection of TF values which leads to poor smoothing performance when setting low values on cloudy days, or to unnecessary ESS operation when setting high values on clear days. This paper introduces a novel power smoothing framework to avoid the arbitrary selection of TF values and reduce the stress on ESS based on predictive and adaptive smoothing mechanisms. The proposed controller contains two layers, the first layer predicts the next day's irradiance profile and uses a clear sky model to identify the cloud class. Next, the controller sets the initial values of the filter time constants for cloudy, moderate, and mild days, and disables smoothing on clear and overcast days. The second layer adaptively adjusts the initial filter time constant based on the current power ramp rate and shares power for a hybrid energy storage system (HESS). The performance of the proposed controller is tested against the forecasting errors and compared to fixed time constant-based smoothing techniques and ramp rate control in a range of scenarios, including smoothing, ramp rate reduction, and battery degradation assessment.

A Bi-Level Optimization and Scheduling Strategy for Charging Stations Considering Battery Degradation

This paper proposes a bi-level optimization scheduling strategy for integrated photovoltaic (PV) and energy storage systems (ESS) to meet electric vehicle (EV) charging demands while reducing charging costs. First, a battery degradation cost model is developed in order to convert the long-term costs into short-term costs for real-time operation. The upper layer of ESS and power grid operation strategies are obtained by minimizing costs associated with battery degradation and distribution grid costs. The lower layer considers the PV uncertainty and the error caused by the upper layer operation strategy, and obtains the lower layer operation strategy by adding a penalty function to minimize fluctuations in power. Second, the author proposes a global optimization algorithm that combines Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) in order to solve the above-mentioned models, effectively combining the global search feature of PSO with the local search capability of SQP. Finally, the bi-level optimization scheduling strategy is obtained by solving the model through the algorithm. Simulation results verify the practicality of the scheduling strategy and the effectiveness of the proposed algorithm.

Optimal energy storage system control using a Markovian degradation model—Reinforcement learning approach

The degradation property of an energy storage system (ESS) has a decisive impact on the economic benefits of ESS operation. However, existing degradation models either do not fully reflect the effects of battery usage or require a full operation history of the battery. This study proposes an accurate Markovian model for battery degradation that reflects battery usage and requires only the current state. We demonstrate the use of the model by establishing a Markov decision process based on the proposed Markovian degradation model and solving it with deep reinforcement learning algorithms. Our intelligent agent, which aims to minimize the sum of electricity cost and degradation cost, generates cost savings of 5%–29% compared to baseline strategies. This framework offers an optimal ESS operation strategy considering battery degradation in a tractable and practical way.

Dynamic and Model Predictive Controllers for Frequency Regulation of an Isolated Micro—Grid with Electrical Vehicles and the ESS Integration

Electric vehicles (EVs) improve the performance of Energy Storage Systems (ESS). The inclusion of electric vehicles in the microgrid system has attracted considerable attention due to the emerging potential of vehicle-to-grid options. The proper coordination among ESS & EVs has a high capacity to tolerate frequency discrepancies and the functioning of the MG (Micro-Grid). In this research, two innovative control techniques-To address the issue of frequency regulation in the standalone MG, Model Predictive Control (MPC) and Dynamical Droop Control (D2C) are linked to affect the structure of ESS and EVs following extensive RES inclusion or a significant change in load demand. The D2C protects EV energy in line with system demands by preserving the least amount of power for anticipated EV demand. Combined MPC and D2C settings are tuned using a complex evolutionary technique to further improve performance. In MATLAB/Simulink, a single MG (micro-Grid) is modeled and evaluated using the controlling strategies indicated above. As well, a range of case papers is taken into account to confirm the integration of the D2C and MPC for a single MG's modulation scheme. Moreover, the MPC outperforms both fuzzy-based proportional-integral (FPI) and proportional-integral (PI) controllers in terms of performance results.

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.