Battery Energy Storage System Research Articles

Differential Power Processing Based Control Framework for Multiple Battery Energy Storage Systems in DC Microgrids

Differential Power Processing Based Control Framework for Multiple Battery Energy Storage Systems in DC Microgrids

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

The Microgrid (MG) concept is being developed to better integrate renewable energy sources and automate distribution networks. Microgrids combine distributed generating units (DGs) and energy storage systems to achieve this. This research paper aims to simultaneously minimize the daily operational cost and net environmental pollution of a small MG system, factoring in the charging demand from Plug-in-Hybrid Electric Vehicles (PHEVs) and consumer load demands. The proposed energy management process not only minimizes operational costs and emissions, but also determines the optimal battery size for the energy storage system. The analysis also explores the importance of two critical variables - the operation and maintenance costs of the DGs, and the total daily cost of the battery energy storage system. The demand for PHEV charging is managed using an intelligent charging approach. Given the complexity of the optimization, a recently developed metaheuristic algorithm, Slime Mould Algorithm (SMA), is applied. The performance of SMA is compared against the Grasshopper Optimization Algorithm and Sine Cosine Algorithm. To solve the multi-objective problem, a weighted sum method maintaining non-dominance and a fuzzy decision-maker technique are employed alongside the suggested algorithms. Three different scenarios verify the proposed method's effectiveness.

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Study on the parameter identification of lithium-ion battery model under high-rate pulse discharge conditions

With the rapid development of the new energy sector and lithium-ion (Li-ion) battery technologies, using Li-ion batteries with high energy density and high discharge rate to form the primary energy subsystem can further improve the compactness and miniaturization level of pulsed power systems. In traditional electric power systems, Li-ion batteries normally operate under the discharge condition of low current and long time. However, as the main power source of pulsed power systems, it is required that Li-ion batteries should have the capability for high-rate pulse discharge. To ensure the safe and reliable operation of the battery energy storage system, it is necessary to conduct the parameter identification of the Li-ion battery model to establish an accurate pulse discharge model under such working conditions. This paper focuses on the discharge characteristics of a cylindrical high-rate single Li-ion battery with a capacity of 3 Ah. At a 20 C rate, the discharge data were obtained when the pulse repetition frequency was 10 Hz and the duty cycle was 90%. By using the mathematical expressions of the Thevenin equivalent circuit model, the recursive relationship of the circuit parameters was obtained, and the parameter identification of the battery model was conducted based on the experimental data by using the genetic algorithm. The results show that the fitting accuracy of the parameter identification reaches 0.9995, indicating the high precision of the model for the Li-ion battery. This work provides a significant reference for the modeling and parameter identification of Li-ion batteries under high-rate pulse discharge conditions.

Semi-Peer-to-Peer Safety Coordination Control for Distributed Battery Energy Storage System in DC Microgrids via Saturated Limitation

Semi-Peer-to-Peer Safety Coordination Control for Distributed Battery Energy Storage System in DC Microgrids via Saturated Limitation

Improving grid reliability with grid-scale Battery Energy Storage Systems (BESS)

The modern electric power system is stable because generation and demand are balanced in real-time. To provide grid managers the leeway to maintain this balance, grid-scale energy storage devices are seeing increased deployment. Another existing technique to achieve a stable and reliable power system today is integrating renewable energies with a battery energy storage system (BESS). Integrating grid-scale BESS to improve grid dependability is crucial since renewable energy sources, which may be somewhat unpredictable, are increasingly being integrated into existing power networks. With its massive electrical energy storage and distribution capabilities, BESS contributes to the grid's ability to balance supply and demand. The BESS helps maintain grid stability by storing energy that is not used during peak hours. This energy comes mostly from renewable sources like solar and wind and is then sent back to the system when the demand is highest. Primary function of BESS includes energy storage and time-shifting, regulation of frequency, voltage support, and enhancement of grid reliability. Development in battery technologies and controls has made them cheaper and inevitable for future power networks. The functions and elements of BESS, the types of electrochemical batteries, the implications of their degradation, and their applications for grid optimization and reliability are discussed in this research. In conclusion, BESS is a significant enabler of grid modernization, resilience, and the evolution of the energy system.

Optimal Battery Energy Storage System Placement Strategy in Central Java Electrical System for Voltage and Losses Improvement

Optimal Battery Energy Storage System Placement Strategy in Central Java Electrical System for Voltage and Losses Improvement

Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

Reliability and economic evaluation of energy storage as backup and load regulation power supply in data centers

AbstractThe battery energy storage system (BESS) combines backup and load regulation functions, making it a potential alternative to the diesel generator (DG) as the backup power source for data centers. Some studies have been conducted on the reliability and cost–benefit of equipping data centers with BESS, but the impact of the reliability of external utility power and the expected outage losses have not been considered. Therefore, this study established a power supply reliability model that included the external utility power reliability and the electrical equipment reliability, and a cost–benefit model that took into account the BESS construction and replacement cost, the outage loss reduction, and the load regulation benefits. A case study was conducted on a data center in Beijing. The results indicate that: (a) the reliability and cost–benefit of BESS significantly vary with the reliability of the external utility power; (b) based on the 2022 utility power reliability data from various regions in China, in over 48% of the regions, the objective function value of BESS, the annual net income considering power outage losses, and the investment payback period are all superior to those of DG; and in over 27% of regions, the BESS–DG parallel system can serve as a suboptimal alternative to DG; (c) in regions with reliable utility power, BESS exhibits the potential as a feasible substitute for DG in providing backup and load regulation power to data centers.

Degradation-Aware Derating of Lithium-Ion Battery Energy Storage Systems in the UK Power Market

Degradation-Aware Derating of Lithium-Ion Battery Energy Storage Systems in the UK Power Market

Bi-Layer Model Predictive Control strategy for techno-economic operation of grid-connected microgrids

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy’s effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy’s effectiveness in optimizing BESS operations under real-world conditions.

Solar PV focused LFC studies utilizing an SFS-optimized PID with fractional derivative (PIDDμ), and incorporating BESS and FESS applications

This study investigates the application of a PID controller with a fractional derivative component (PIDDμ), optimized using the stochastic fractional search (SFS) technique, for load frequency control (LFC) in power systems. Additionally, solar PV and battery energy storage systems (BESS) and flywheel energy storage systems (FESS) are integrated as backup sources to enhance the LFC performance. To assess the effectiveness of the proposed approach, three error metrics—Integral Time Absolute Error (ITAE), Integral Time Multiplied Square Error (ITSE), and Integral of Absolute Error (IAE)—are evaluated. The results are compared with recent optimization-based LFC techniques across various scenarios. It is found that the SFS-PIDDμ achieves IAE=0.006786, ITSE=1.07e-05, and ITAE=0.01853, which are significantly lower than the error values produced by other LFC techniques. Furthermore, integrating solar PV, along with BESS and FESS, further reduces these error values. The graphical LFC responses of the SFS-PIDDμ outperform other optimization-based LFC techniques. The robustness of the approach is also validated against random load changes of varying magnitudes and for broader parametric variations.

Impact analysis of uncoordinated electric ferry charging on distribution network

The maritime sector significantly contributes to greenhouse gas emissions, and implementing electric marine vessels can mitigate this emission. Replacing conventional ferries with EFs (Electric Ferries) necessitates dockyard charging stations, making it crucial to analyze the EF's charging impacts on the shore-side distribution network. This research examines the potential effects of uncoordinated EF charging on a regional Australian distribution network. Using OpenDSS (Open Distribution System Simulator) software, power flow impact analysis is conducted on a selected simulated distribution network, integrating BESSs (Battery Energy Storage Systems) as representation of proposed charging stations. The load demand is increased to 50 % and 80 %, given the actual demand is 15 % of the transformers’ capacities. The power flow results without BESSs serve as the base case. In uncoordinated mode, bus voltages decrease by 1 %-3 %, and load currents increase by 2 %-3.5 % compared to the base case. Additionally, lines and transformers near BESS-connected buses see a 1 % rise in power flow with increased total power consumption and loss. THD (Total Harmonic Distortion) and TDD (Total Demand Distortion) are over 5 % and 18 % in uncoordinated charging, which exceeds harmonics limits according to IEEE 519–2022 standards. These results may provide valuable insights for installing EF charging stations in distribution networks.

Coordinated control algorithm of hydrogen production-battery based hybrid energy storage system for suppressing fluctuation of PV power

Photovoltaic (PV) power generation has issues of volatility and intermittency. Currently, PV plants are generally equipped with 10% rated capacity lithium-ion (Li) battery energy storage systems in China, who often fail to suppress fluctuation in the output power of PV plants effectively and meet the grid-connected standard. The hybrid energy storage system (HESS) combining with hydrogen production and Li battery system can produce hydrogen by water electrolysis during the peak period of PV power generation, effectively improving PV utilization efficiency, while smoothing PV power fluctuation and improving grid connection electricity quality. Firstly, models of the solid oxide electrolysis cell (SOEC) and alkaline electrolysis cell (AEC) systems for hydrogen production, and Li battery energy storage system are established, and the transient response characteristics of each system are analyzed. Secondly, an adaptive wavelet packet decomposition (AWPD) method for PV power signal decomposition is proposed based on the wavelet packet decomposition (WPD) method. Thirdly, a capacity configuration method for HESS are proposed based on the APWD method. Fourthly, a coordinated control strategy for HESS is proposed with the transient response characteristics of different energy storage systems and the state of charge for Li battery system. Finally, the proposed method is validated through simulation experiments based on the actual power data of the PV plant. The results show that the developed methods can effectively utilize partial PV power generation to produce hydrogen, improve PV utilization, and the combined output power of PV plant and HESS can fulfill the grid-connected standard.

Implementation of a multistage predictive energy management strategy considering electric vehicles using a novel hybrid optimization technique

In this paper, a novel artificial intelligence (AI)-based energy management strategy across three levels is designed for isolated microgrids. During the initial phase, AI is not employed. A precise and rapid-response microcontroller, namely the FPGA, is utilized. The performance of the FPGA is experimentally investigated under various operation conditions. In the second phase, AI plays a crucial role in enhancing both the reliability and economic effectiveness of the system. The multi-objective snake optimization (SO) algorithm is employed to attain high technical and economic performance within predefined constraints. Three objective functions are involved in this phase, focusing on the minimization of operational costs, loss of power supply probability (LPSP), and electrical energy losses in the dummy load. In the third level, a novel control strategy-based coordinated model predictive control is presented as part of the proposed AI-embedded energy management strategy. A hybrid optimization algorithm combining the multilayer feed-forward neural networks (MFFNN) and SO algorithms is proposed to predict the output power of backup sources. The microgrid under consideration is supported by a hybrid backup system comprising battery energy storage systems (BESS), electric vehicle (EV) batteries, and fuel cells (FCs). The effectiveness of this hybrid algorithm is evaluated through comparison with many other algorithms, aiming to assess its performance in accurately predicting the output power of backup sources. The results show the feasibility of using AI to achieve efficient operation and energy management in microgrids. The proposed MFFNN-SO algorithm achieves the best performance, as the normalized root mean square error (NRMSE) for FCs, BESS, and EV is about 0.1296 %, 0.0094 %, and 0.1304 %, respectively. The SO algorithm achieves a notable 6.3361% reduction in operating costs, resulting in a final operational cost of 166.4811 $.

The impacts of DC/AC ratio, battery dispatch, and degradation on financial evaluation of bifacial PV+BESS systems

As the price of solar modules has decreased, oversizing PV system becomes a general practice. Without proper energy management, the oversized systems could lead to over-generation waste which cause a loss in revenue. Battery energy storage system (BESS) can be integrated to the PV system for utilizing the over-consumption energy and increasing the system’s financial benefits. This paper highlights the influence of technical and financial factors on the photovoltaic (PV) and PV with BESS design. To analyze the impacts of the factors on PV systems, thirty-six system configurations with a variety of geographic locations, tariff structures, billing methods, and PV system technology are simulated using System Advisor Model (SAM) as a tool with result validation from measurements on a real oversized residential grid-tied bifacial PV+BESS system. To study the financial criteria that affect the optimal system design, we conducted the sensitivity analysis of the DC/AC ratio, battery replacement threshold, and battery size to observe the optimal system design based on three financial outputs: levelized cost of energy (LCOE), net present value (NPV), and payback period. The findings show that the optimal DC/AC ratio for residential-scale PV systems lies between 1.1 - 1.3 and can go up to 1.6 with BESS. PV+BESS systems can reduce the payback period by 20% and boost NPV by 80% compared to the PV-only systems. The sensitivity study of the battery replacement shows that the battery can be degraded until 50% of its maximum capacity for the lowest LCOE. To achieve the optimal grid-tied PV system design, each economic parameter should be evaluated together.

The state of charge predication of lithium-ion battery energy storage system using contrastive learning

The state of charge (SOC) is a critical state quantity that must be determined in real-time for a battery energy storage system (BESS). It is a prerequisite for the operation of a BESS. However, obtaining the precise value of SOC is challenging due to it being a hidden state quantity. Existing neural network models commonly employ an end-to-end prediction paradigm for SOC estimation, which fails to fully exploit the rich information present in the time-series battery data. Unlike most studies available in the literature, we propose a novel SOC prediction method named CLDMM. This method is the first to apply contrastive learning techniques from the image field to the SOC prediction of lithium batteries. The method utilizes data augmentation, a multi-scale encoder, and multi-layer perceptrons to learn latent representations and mix these with raw data proportionally for downstream predictive tasks. The performance of the proposed method is evaluated using the Panasonic NCR18650PF dataset, and ablation study were conducted. Experimental results show that CLDMM outperforms baseline methods, achieving an average mean absolute error (MAE) of 0.64% and an average maximum error (MAX) of 2.66%.

Iterative sizing methodology for photovoltaic plants coupled with battery energy storage systems to ensure smooth power output and power availability

Photovoltaic (PV) solar energy is a fundamental technology that will help transition from a fossil fuel–based energy mix to a future with high shares of renewable energy. To do so, PV plants coupled with energy storage systems can accumulate excess power and dispatch it when PV generation changes, performing PV smoothing. While coupling PV plants with battery energy storage systems (BESS) offers a solution, current methodologies often need to thoroughly describe the interplay between BESS energy capacity, power rating, and the long–term impacts of battery degradation. This paper addresses this gap by proposing a four–step methodology that optimizes BESS sizing for PV plants, accounting for both cycling and calendar aging effects on system performance and the economic implications of battery replacements. We use a model that considers the degradation of PV modules and two Li–ion battery types (LiFePO4, LFP, and LiNiMnCoO2, NMC). A 16.3 MW PV plant is simulated along with the BESS to test the methodology. The results indicate that an LFP–based BESS of 2.5 MWh with a rated power of 1.25 MW ensures a stable output power with variation below 10% of the rated PV plant 98 % of the time. In contrast, an NMC–based BESS, while effective in reducing non–compliance, incurs higher costs due to more frequent battery replacements, making it less economically viable. The methodology and results presented in this paper provide valuable insights for designing cost–effective and reliable energy storage solutions in PV plants, ensuring compliance with set power availability.

A Coordinated Control Strategy for Efficiency Improvement of Multistack Fuel Cell Systems in Electric–Hydrogen Hybrid Energy Storage System

A two-layer coordinated control strategy is proposed to solve the power allocation problem faced by electric–hydrogen hybrid energy storage systems (HESSs) when compensating for the fluctuating power of the DC microgrid. The upper-layer control strategy is the system-level control. Considering the energy storage margin of each energy storage system, fuzzy logic control (FLC) is used to make the initial power allocation between the battery energy storage system (BESS) and the multistack fuel cell system (MFCS). The lower-layer control strategy is the device-level control. Considering the individual differences and energy-storage margin differences of the single-stack fuel cell (FC) in an MFCS, FLC is used to make the initial power allocation of the FC. To improve the hydrogen-to-electricity conversion efficiency of the MFCS, a strategy for optimization by perturbation (OP) is used to adjust the power allocation of the FC. The output difference of the MFCS before and after the adjustment is compensated for by the BESS. The simulation and experiment results show that the mentioned coordinated control strategy can enable the HESS to achieve adaptive power allocation based on the energy storage margin and obtain an improvement in the hydrogen-to-electricity conversion efficiency of the MFCS.

Application of Artificial Intelligent Techniques for Power Quality Improvement in Microgrid System

This paper focuses on modeling, control and power quality improvement of a microgrid connected system. This last was designed as a multi-converter system with Wind Turbine driven Permanent Magnet Synchronous Generator, and lithium ion Battery Storage Energy System. These sources are connected by a continuous bus to a nonlinear load through a DC /AC converter and three-phases multi-functional voltage source inverter. Where, wind systems are considered as primary power resource, and the grid is used for the effect of consumption of the overage power available from sources, when the battery has been fully charged. Multi Functional Voltage Source Inverter is used to ameliorate the performance of the proposed system, guaranteeing both reactive power and harmonic compensation. Moreover an intelligent control by fuzzy logic control algorithm are managed In order to extract the maximum power from wind turbine, to guarantee an effective storage management, this about the DC side. For the AC side a direct power control strategy with fuzzy logic algorithm have been used. The simulation of the system solution is carried out in Matlab/Simulink. The obtained simulation results show that the technique of fuzzy logic furnish the best solution in term of robustness, optimization performance, low THD and fast dynamic response.