Battery Energy Storage System Research Articles

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.

Grid-integrated solutions for sustainable EV charging: a comparative study of renewable energy and battery storage systems

IntroductionThe integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging.MethodsTo address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis.ResultsThe proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (NPC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case.DiscussionSeasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption.

Model-free detection and quantitative assessment of micro short circuits in lithium-ion battery packs based on incremental capacity and unsupervised clustering

Timely diagnosis of micro short circuit (MSC) faults is crucial for ensuring the safe operation of lithium-ion battery energy storage systems. Existing diagnostic methods face limitations such as high dependency on battery models, difficulty in determining accurate diagnostic thresholds, or low computational efficiency. This work presents a model-free approach for the detection and quantitative assessment of MSCs in lithium-ion battery packs, with incremental capacity (IC) and unsupervised clustering. First, the IC is extracted from charging voltage data to effectively characterize MSC faults in lithium-ion batteries. Next, principal component analysis is used to map the high-dimensional feature space onto a two-dimensional plane to facilitate fault detection and result visualization. Then, an unsupervised clustering algorithm is employed for anomaly detection to identify MSC cells within the battery pack. For the detected MSC cells, a method based on the maximum charging voltage difference between adjacent cycles is designed to estimate the MSC resistance, quantitatively assessing the severity and evolution stage of the MSC. Experimental results show that the accuracy of MSC detection is 99.17 % and the minimum relative error of short-circuit resistance estimation is 1.20 %, which demonstrates the effectiveness and feasibility of the proposed method.

Comprehensive case study on the technical feasibility of Green hydrogen production from photovoltaic and battery energy storage systems

AbstractThe growing demand for alternative energy sources to alleviate environmental impacts highlights the need to move from fossil fuels to renewable energy. This study demonstrated the technical feasibility of using a solar photovoltaic (PV) system for the production of green hydrogen. This research examined electrical and power data from a PV plant in Irecê, Bahia, using open data sources to provide insights into the production of green hydrogen from renewable sources. The system mainly depends on the use of a renewable source, PV solar energy, integrated with batteries, electrolyzers, and hydrogen tanks. Electrolyzer, battery, and hydrogen tank sizing analysis for optimal hydrogen production was effectively conducted using HOMER Energy software. The predicted system topology prioritizes a local DC network, optimizing efficiency for electrolyzers that have inherently low efficiency. The electrolyzer simulation involves initial Python‐based sizing and comprehensive sizing with HOMER Energy software, ensuring accuracy within a 10% discrepancy limit. This highlights the importance of analytical calculations and optimization software for sizing more complex systems.

Optimal sitting, sizing and control of battery energy storage to enhance dynamic stability of low‐inertia grids

AbstractAs inverter‐based resources like wind turbines increase, grid inertia and stability decrease. Optimal placement and control of energy storage systems can stablise low‐inertia grids. This paper investigates how optimal battery energy storage systems (BESS) enhance stability in low‐inertia grids after sudden generation loss. The sitting, sizing and control of BESS are determined simultaneously in each genetic algorithm (GA) population, then voltage and frequency stability is evaluated based on the network simulation. This continues until the optimal solution is found. A network based on Kundur's four‐machine system is modelled for the first study and two of the four synchronous generators (SGs) have been replaced with wind farms. Then, the production of the third SG has been decreased by 13%. According to the results, addition of wind farms causes the frequency drop below 49.6 Hz for more than 5 min, indicating instability. It is also demonstrated that with optimal control parameters and placement, a 60 MW BESS can alleviate the voltage and frequency fluctuations, leading to enhanced stability. This method has also been tested on the IEEE 39‐bus network, where the installation of a BESS with a capacity of 9 MVA could restore the frequency stability.

Research on Energy Control Strategy based on Improved Hybrid Energy Storage System

With the rapid development of globalization, problems such as resource depletion have become increasingly prominent, and new energy technologies will become a new way for humans to alleviate the energy crisis, resulting in a large number of new energy devices. The application of a large number of new energy devices has led to unstable power in the power grid. Therefore, this paper proposes an energy control strategy based on an improved hybrid energy storage system to improve power grid stability. By integrating a traditional single battery energy storage system into a supercapacitor energy storage system and combining it into a battery capacitor hybrid energy storage system, the power of the hybrid energy storage system is distributed to compensate for fluctuating power. Simulation results show that the energy management control strategy of the improved hybrid energy storage system can effectively smooth out the occurrence of power in independent microgrid systems. The issue of power fluctuations, this further proves the feasibility and correctness of the control strategy.

Optimal Placement and Sizing of Battery Energy Storage Systems for Improvement of System Frequency Stability

Optimal Placement and Sizing of Battery Energy Storage Systems for Improvement of System Frequency Stability

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

A solar-powered multi-functional portable charging device (SPMFPCD) with internet-of-things (IoT)-based real-time monitoring—An innovative scheme towards energy access and management

In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet- of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution.

Optimised rule-based peak shaving and its impact on annual energy cost reduction

This paper presents an optimised rule-based peak-shaving method which is tested on an educational institute to know its impact on annual energy cost reduction. In recent years, energy management in low-voltage distribution systems has drawn more attention. Peak shaving is an important energy management method which helps consumers and grid operators. Peak shaving reduces voltage drop and voltage-rise issues, ratings of the different components and energy consumption costs and power losses. In this work, some of the most effective peak-shaving techniques to handle the problem of peak shaving and a few real-world implementations are discussed. Furthermore, an optimised rule-based peak-shaving (RBPS) method, which considers grid import and export power limits, is presented. The impact of this peak-shaving method, on a case study of an educational institute with a photovoltaic (PV)-battery energy storage system (BESS) based on its net load data over an annum for annual operating energy cost reduction, is presented. The results indicate that the total cost reduction for a year is 68.48% using PV and BESS with the peak-shaving application.

Aggregator control of battery energy storage in wind power stations to maximize availability of regulation service

Battery energy storage systems can produce very fast bi-directional power flows, which makes them suitable for providing wind power regulation and frequency control services. Though battery systems can provide fast regulation services, their energy storage capacities are quite low in comparison to other generation sources, so regulation responses from them should be optimized to maximize availability of service. This paper proposes an aggregator that optimizes frequency control responses from battery energy storage systems to maximize service availability. The frequency control response from the aggregated system is defined by a single frequency-droop characteristic. This provides the predictability of response required to comply with grid codes and allows the aggregated system to participate in frequency control markets as a single entity. The method of implementation is fail-safe as failure to receive an optimized order from the aggregator does not prevent the battery energy storage systems from responding to frequency events. This mitigates stability concerns relating to communication delays between the aggregator and battery energy storage systems. Battery systems that provide multiple functions, such as frequency control system services and wind power regulation, can participate in the aggregator scheme by assigning a proportion of the battery’s capacity to the aggregator scheme. Simulations performed in DIgSILENT PowerFactory show that the aggregator successfully extends the duration of full regulation service from the battery systems during frequency excursion events.