Search Results (35)

This paper proposes a comprehensive mathematical optimization framework for techno-economic demand side management (DSM) in hybrid energy systems (HESs), with a focus on standalone configurations. The framework incorporates load growth projections and the probabilistic uncertainties of renewable energy sources to enhance planning robustness. To identify high-quality near-optimal solutions, several advanced metaheuristic algorithms were employed, including the Exponential Distribution Optimizer (EDO), Teaching-Learning-Based Optimization (TLBO), Circle Search Algorithm (CSA), and Wild Horse Optimizer (WHO). The results highlight substantial economic and environmental improvements, with battery integration yielding a 69.7% reduction in total system cost and an 84.3% decrease in emissions. Additionally, this study evaluated the influence of future load growth on fuel expenditure, offering realistic insights into the techno-economic viability of HES deployment. Full article

Optimizing the power allocation and capacity configuration of hybrid energy storage systems (HESS) is crucial for enhancing grid stability, power quality and renewable energy utilization in wind–solar complementary microgrids. However, the conventional configuration methods exhibit inaccuracy and low reliability. To achieve the optimal capacity configuration of HESS in wind–solar complementary microgrids, a power allocation strategy and a capacity optimization configuration model for HESS consisting of vanadium redox flow batteries (VRBs) and supercapacitors (SCs) were proposed based on parameter-optimized variational mode decomposition (VMD). Firstly, the number of mode decomposition (K) and the penalty factor (α) of VMD were optimized using the random walk grey wolf optimizer (RW-GWO) algorithm, and the HESS power signal was decomposed by RW-GWO-VMD. Secondly, an optimal capacity configuration model was formulated, taking into account the whole life cycle cost of HESS, and particle swarm optimization (PSO) algorithm was applied to optimize HESS capacity while satisfying operational constraints on charge/discharge power, state of charge (SOC) range, and permissible rates of load deficit and energy loss. Thirdly, the optimal capacity allocation was obtained by minimizing the whole life cycle cost of HESS, with the frequency division threshold N serving as the optimization parameter. Finally, comprehensive comparison and analysis of proposed methods were conducted through simulation experiments. The results demonstrated that the whole life cycle cost of RW-GWO-VMD was 7.44% lower than that of EMD, 1.00% lower than that of PSO-VMD, 0.72% lower than that of AOA-VMD, and 0.27% lower than that of GWO-VMD. Full article

Addressing the issue of efficient, economical, and reliable operation of a single lead-acid battery (LAB) black start system in complex scenarios, a hybrid energy storage system (HESS) black start scheme based on super capacitors (SCs) is proposed. The proposed solution mainly includes two aspects: an integrated structure and a control strategy. A topology structure with a direct parallel output on the AC side is adopted, and the SC is directly connected to the AC side of the LAB in the current source mode. Compared with traditional DC side access schemes, it can cope with large surge currents by a small capacity, and the economy of the HESS black start system has been effectively improved. In order to improve the dynamic characteristics of the black start control system, a self-adaptive control strategy based on the virtual synchronous generator (VSG) and model predictive control (MPC) is proposed. Based on the small signal disturbance model, the influence of the system parameters on stability was analyzed, and the control parameters are adjusted according to the angular velocity and frequency deviation. A generator recognition model at the ms level was constructed, and the set reference current according to the power level is brought into the MPC to track the reference current. Compared with existing methods, it can effectively suppress the disturbance of the black start system, and the fast responsiveness and stability of the control system is improved. Finally, real operational data is compared and analyzed. The results indicate that the proposed control strategy can accurately identify different black start scenarios, with lower configuration costs and good dynamic performance. Full article

This paper proposes a hybrid synchronization control modular multilevel converter-based hybrid energy storage system (HSC-MMC-HESS) that innovatively integrates battery units within MMC submodules (SMs) while connecting a supercapacitor (SC) to the DC bus. The configuration synergistically combines the high energy density of batteries with the high power density of SCs through distinct energy/power pathways. The operational principles and control architecture are systematically analyzed, incorporating a hybrid synchronization control (HSC) strategy to deliver system inertia, primary frequency regulation, fault-tolerant mode transition capabilities, and isolation control. A hierarchical control framework implements power distribution through filtering mechanisms and state-of-charge (SOC) balancing control for battery management. Hardware-in-the-loop experimental validation confirms the topology’s effectiveness in providing inertial support, enabling flexible operational mode switching and optimizing hybrid energy storage utilization. The demonstrated capabilities indicate strong application potential for medium-voltage distribution networks requiring dynamic grid support. Full article

The increasing integration of renewable energy sources has posed significant challenges to grid frequency stability. To maximize the advantages of energy storage in primary frequency regulation, this paper proposes a comprehensive control strategy for a hybrid energy storage system (HESS) based on supercapacitor battery. Firstly, considering the characteristics of the HESS and different control strategies, the battery responds to virtual droop control to reduce frequency deviation, while the supercapacitor responds to inertia control to suppress frequency drops and facilitate frequency recovery. Simultaneously, a reasonable dynamic dead zone is configured to prevent frequent actions of the battery and thermal unit while allowing flexible adjustments according to the load condition. Thirdly, an algebraic S-curve-based adaptive droop coefficient incorporating SOC is proposed, while the inertia coefficient additionally considers load type, enhancing adaptability. Furthermore, to better maintain the battery’s SOC, an improved adaptive recovery strategy within the battery dead zone is proposed, considering both SOC recovery requirements and system frequency deviation constraints. Finally, a simulation validation was conducted in MATLAB/Simulink. Compared to the conventional strategy, the proposed control strategy reduces the frequency drop rate by 17.43% under step disturbance. Under compound disturbances, the RMS of frequency deviation decreases by 13.34%, and the RMS of battery SOC decreases by 68.61%. The economic benefit of this strategy is 3.212 times that of the single energy storage scheme. The results indicate that the proposed strategy effectively alleviates sudden frequency disturbances, suppresses frequency fluctuations, and reduces battery output while maintaining the SOC of both the supercapacitor and the battery, thereby extending the battery lifespan and improving economic performance. Full article

Regenerative braking systems (RBS enhance energy efficiency and range in electric vehicles (EVs) by recovering kinetic energy during braking for storage in batteries or alternative systems. This literature review examines RBS advancements from 2005 to 2024, focusing on system design, control strategies, energy storage technologies, and the impact of external and kinematic factors on recovery efficiency. Based on a systematic analysis of 89 peer-reviewed articles from Scopus, it highlights a shift from basic PID controllers to advanced predictive algorithms like Model Predictive Control (MPC) and machine learning approaches. Technologies such as brake-by-wire and in-wheel motors improve safety and stability, with the latter excelling in all-wheel-drive setups over single-axle configurations. Hybrid Energy Storage Systems (HESS), combining batteries with supercapacitors or kinetic accumulators, address power peak demands, though cost and complexity limit scalability. Challenges include high computational requirements, component reliability in harsh conditions, and lack of standardized testing. Research gaps involve long-term degradation, autonomous vehicle integration, and driver behavior effects. Future work should explore cost-effective HESS, robust predictive controls for autonomous EVs, and standardized frameworks to enhance RBS performance and support sustainable transportation. Full article

To address the critical challenge of high energy consumption in single-source electric vehicles, this study proposes a hybrid energy storage system (HESS)-integrated energy management strategy (EMS). Firstly, the car-following and HESS models are constructed. Secondly, a multi-objective optimization framework balancing adaptive cruise control (ACC) optimal tracking quality and energy economy is developed, where the fast, non-dominated sorting genetic algorithm (NSGA-II) resolves dynamic power demands. Thirdly, the third-order Haar wavelet enables online rolling decomposition of power profiles. The high-frequency transient power is matched by a supercapacitor, while the low-frequency steady-state power is utilized as an input variable to the optimization controller. Then, a fuzzy logic controller dynamically optimizes HESS’s energy distribution based on state-of-charge (SOC) and load conditions. Finally, the cruise simulation model has been constructed utilizing the MATLAB/Simulink platform. Comparative analysis under the Urban Dynamometer Driving Schedule (UDDS) demonstrates a 3.71% reduction in the total power demand of the ego vehicle compared to the front vehicle. Compared to single-source configurations, the HESS ensures smoother SOC dynamics in lithium-ion batteries. After employing the third-order Haar wavelet for online rolling decomposition of the demand power, the high-frequency transient power matched by the lithium-ion battery is substantially reduced. Comparative analysis of three control strategies demonstrates that the wavelet-fuzzy logic approach exhibits superior comprehensive performance. Consequently, the proposed strategy effectively mitigates high-frequency transient peak charge/discharge currents in the lithium-ion battery and the energy consumption of the entire vehicle. This study provides a novel solution for energy storage systems in hybrid energy storage electric vehicles (HESEV) under ACC scenarios. Full article

Callicarpa giraldii Hesse ex Rehd. is an endemic plant in China and has long been used as a traditional medicine in several provinces. Although the plant has been reported to contain flavonoids, triterpenes, and alkaloids, this study represents the first report of the isolation of phyllocladane-type diterpenoids, a relatively rare class of compounds. In this study, 18 new phyllocladane-type diterpenoids (7–24) were isolated and structurally elucidated, including eight uncommon 3,4-seco phyllocladane-type diterpenoids (15–22) and two unusual phyllocladane-type diterpene dimers (23–24), along with six known analogues (1–6). Their structures were elucidated by a comprehensive analysis of 1D and 2D NMR, IR, and HRESIMS data. The absolute configurations were determined by single crystal X-ray diffraction experiments, DFT NMR calculations, and TDDFT ECD calculations. Based on the obtained and reported spectroscopic data, we refined a rule to distinguish phyllocladane-type diterpenoids from their diastereomeric ent-kaurane-type compounds. Additionally, the isolated compounds were evaluated for their in vitro anti-neuroinflammatory activity against lipopolysaccharide (LPS)-induced inflammation in BV-2 microglial cells. Compounds 5, 10, 13, 18, 19, and 20 showed moderate inhibitory activity at the concentration of 20 μM, with compounds 5 and 13 markedly reducing the mRNA levels of the pro-inflammatory cytokines IL-1β, IL-6, and TNF-α at this concentration. Full article

The large-scale integration of wind, solar, and battery energy storage is a key feature of the new power system based on renewable energy sources. The optimization results of wind turbine (WT)–photovoltaic (PV)–battery energy storage (BES) hybrid energy systems (HESs) can influence the economic performance and stability of the electric power system (EPS). However, most existing studies have overlooked the effect of power electronic converter (PEC) efficiency on capacity configuration optimization, leading to a significant difference between theoretical optimal and actual results. This paper introduces an accurate efficiency model applicable to different types of PECs, and establishes an enhanced mathematical model along with constraint conditions for WT–PV–BES–grid–load systems, based on precise converter efficiency models. In two typical application scenarios, the capacity configurations of WT–PV–BES are optimized with optimal cost as the objective function. The different configuration results among ignoring PEC loss, using fixed PEC efficiency models, and using accurate PEC efficiency models are compared. The results show that in the DC system, the total efficiency of the system with the precise converter efficiency model is approximately 96.63%, and the cost increases by CNY 49,420, about 8.56%, compared to the system with 100% efficiency. In the AC system, the total efficiency with the precise converter efficiency model is approximately 97.64%, and the cost increases by CNY 4517, about 2.02%, compared to the system with 100% efficiency. The analysis clearly reveals that the lack of an accurate efficiency model for PECs will greatly affect the precision and effectiveness of configuration optimization. Full article

Supplying power to large, pulsed power loads in shipboard medium voltage direct current integrated power systems is challenging due to the limited dynamic power responsiveness of the gas turbine. The two main solutions to this problem are improving the gas turbine’s dynamic performance and using energy storage devices for transient power compensation. In this paper, these two approaches are combined to achieve optimal coordination between the gas turbine’s dynamic response and the system’s transient power sharing strategy, and a mechanical–electrical cooperative operation strategy and a hierarchical optimization method of the system are proposed. The hierarchical optimization model is designed with energy storage configuration and dynamic performance as the lower and upper objectives, and an efficient parallel neural network-based genetic algorithm is employed to solve this optimization. The proposed method is applied to determine the system optimal energy storage configuration and dynamic performance across multiple scenarios, including different propulsion conditions with various types of large, pulsed power loads. The results demonstrate that the proposed method effectively reduces energy storage requirements: fuel system optimization, IGV adjustment strategy, and bleeding strategy, respectively, lower the energy storage configuration optimization objective values by 10.6%, 20.1%, 2.4%, and 6.2%, 6.5%, 5.3%. The SVSDP scheme achieves reductions of 19.5%, 7.6%, and 49.6%, 39.7% compared to VRCD and PSO-FS. Furthermore, the method also enhances the system’s dynamic response: under the specified HESS configuration, fuel system optimization, IGV adjustment strategy, and bleeding strategy reduce the dynamic performance optimization objective values by 6.8%, 23.3%, 8.6%, and 9.2%, 21.5%, 6.8%. The SVSDP scheme results in reductions of 21.3%, 15.4%, and 66.2%, 26.0% compared to VRCD and PSO-FS. Full article

The development of energy storage systems is significant for solving problems related to climate change. A hybrid energy storage system (HESS), combining batteries with ultracapacitors, may be a feasible way to improve the efficiency of electric vehicles and renewable energy applications. However, most existing research requires comprehensive modelling of HESS components under different operating conditions, hindering optimisation and real-world application. This study proposes a novel approach to analysing the set of differential equations of a substitute model of HESS and validates a model-based approach to investigate the performance of an HESS composed of a Valve-Regulated Lead Acid (VRLA) Absorbent Glass Mat (AGM) battery and a Maxwell ultracapacitor in a parallel configuration. Consequently, the set of differential equations describing the HESS dynamics is provided. The dynamics of this system are modelled with a double resistive–capacitive (2-RC) scheme using data from Hybrid Pulse Power Characterisation (HPPC) and pseudo-random cycles. Parameters are identified using the Levenberg–Marquardt algorithm. The model’s accuracy is analysed, estimated and verified using Mean Square Errors (MSEs) and Normalised Root Mean Square Errors (NRMSEs) in the range of a State of Charge (SoC) from 0.1 to 0.9. Limitations of the proposed models are also discussed. Finally, the main advantages of HESSs are highlighted in terms of energy and open-circuit voltage (OCV) characteristics. Full article

In order to address the challenges associated with optimizing multi-timescale operations and allocating ultra-short-term energy storage for HWP integration, this study takes into account both the economic and reliability aspects of the HWP integration base. It proposes a model for optimizing operations and allocating energy storage capacity, achieving optimization across long-term, short-term, and ultra-short-term operations for an MECB. Initially, operation optimization is implemented for an entire group of terraced hydropower plants by regulating them with annual regulating capabilities on a long-term timescale. The objectives are to maximize the daily average minimum output and annual power generation. Subsequently, short-term operation optimization focuses on maximizing HWP power feed-in, minimizing new energy power curtailment, and reducing residual load standard deviation while ensuring the guaranteed output optimization results for the long term. Finally, to mitigate ultra-short-term fluctuations in new energy, a HESS with specified capacity and power is configured with the goal of minimizing comprehensive costs. Additionally, to address the challenge of smoothing negative fluctuations, which is hindered by charging and discharging efficiency limitations, a variable baseline is introduced, deviating from the conventional 0 MW baseline. A simulation study based on data from the hydro–wind–PV hybrid project in the Beipanjiang River Basin, China, demonstrates the following: (1) after long-term system optimization, the total power generation capacity of the system increases by 9.68%, while the peak-to-valley difference in output is significantly reduced; (2) short-term system optimization significantly reduces both the average variance in residual loads and the amount of power curtailed over five representative days; (3) the system incorporates 398.62 MWh of lithium-ion battery storage with a power of 412.47 MW and 51.09 MWh of supercapacitor storage with a power of 223.32 MW, which, together, completely smooth out the ultra-short-term fluctuations in new energy output. Full article

Considering environmental concerns, electric vehicles (EVs) are gaining popularity over conventional internal combustion (IC) engine-based vehicles. Hybrid energy-storage systems (HESSs), comprising a combination of batteries and supercapacitors (SCs), are increasingly utilized in EVs. Such HESS-equipped EVs typically outperform standard electric vehicles. However, the effective management of power sources to meet varying power demands remains a major challenge in the hybrid electric vehicles. This study presents the development of a MATLAB Simulink model for a hybrid energy-storage system aimed at alleviating the load on batteries during periods of high power demand. Two parallel combinations are investigated: one integrating the battery with a supercapacitor and the other with a photovoltaic (PV) system. These configurations address challenges encountered in EVs, such as power fluctuations and battery longevity issues. Although batteries are commonly used in conjunction with solar PV systems for energy storage, they incur higher operating costs due to the necessity of converters. The findings suggest that the proposed supercapacitor–battery configuration reduces battery peak power consumption by up to 39%. Consequently, the supercapacitor–battery HESS emerges as a superior option, possibly prolonging battery cycle life by mitigating stress induced by fluctuating power exchanges during the charging and discharging phases. Full article

A design toolbox has been developed for hybrid energy storage systems (HESSs) that employ both batteries and supercapacitors, primarily focusing on optimizing the system sizing/cost and mitigating battery aging. The toolbox incorporates the BaSiS model, a non-empirical physical–electrochemical degradation model for lithium-ion batteries that enables accurate simulations of battery performance and degradation under realistic operating conditions. The paper presents a detailed description of the parameterization, and validation process for the battery model, emphasizing the high accuracy and strong reliability of the battery aging prediction. The HESS design toolbox can be used to investigate the impact of various battery/supercapacitor configurations and energy management algorithms on the design, battery degradation, and system investment cost of the hybrid storage system. To illustrate the effectiveness of the design toolbox, a case study on Dynamic Moderation frequency support in the UK grid was conducted. For this use case, the application of hybrid storage energy systems is well suited due to the highly dynamic power regulation requirements in island grids with low inertia. By utilizing the fast response of supercapacitors, the stress on the battery caused by short-term high-power peaks can be significantly alleviated. In this way, the hybrid storage system effectively reduces either the battery size or the battery aging rate. In summary, this research highlights the crucial role of a comprehensive analysis in the design of hybrid energy storage systems, addressing both battery aging and overall system costs. The design toolbox can provide transparency regarding the design space and assist in determining the most suitable HESS configuration for a given application. Full article

The fluctuation and intermittency of wind power generation seriously affect the stability and security of power grids. Aiming at smoothing wind power fluctuations, this paper proposes a flywheel–battery hybrid energy storage system (HESS) based on optimal variational mode decomposition (VMD). Firstly, the grid-connected power and charging–discharging power of the HESS are determined based on the sliding average algorithm. Secondly, the VMD algorithm, optimized using long short-term memory (LSTM), is used to decompose the hybrid energy storage power (HESP) into a series of sub-modes with frequencies from low to high. Then, the state of charge of the battery energy storage system and the speed of the flywheel energy storage system are monitored in real time, and the primary power of the HESS is modified according to the rules formulated by fuzzy control. Finally, through a simulation example, it is concluded that the method meets the requirements of smoothing wind power fluctuations and gives full play to the characteristics of energy storage battery and flywheel energy storage to ensure the stable operation of the energy storage system. The method presented in this paper can provide a reference for HESP configuration and control operation strategy formulation. Full article