Search Results (10)

The integration of new renewable energy sources, such as wind and solar power, is characterized by strong randomness and volatility, which increases the risk of power grid system frequency fluctuations exceeding limits. Traditional thermal power units are unable to frequently respond to frequency regulation signals, necessitating the incorporation of energy storage technologies for primary frequency control. This paper presents a primary frequency control strategy for a flywheel–battery hybrid energy storage system (HESS) based on fuzzy adaptation and state-of-charge (SOC) self-recovery. First, a frequency response system model for primary frequency regulation in flywheel–battery hybrid energy storage was formulated. The frequency regulation command is divided into high-frequency and low-frequency components, which are allocated to the flywheel and the battery, respectively. Fuzzy control and regression functions were employed to adjust and constrain the frequency deviation, frequency deviation rate, and SOC. Subsequently, considering the SOC and frequency deviation of each energy storage component, a SOC self-recovery strategy was proposed. Finally, a simulation analysis was performed using a system benchmark capacity of 600 MW. Under step load disturbance conditions, the proposed strategy reduces the maximum frequency deviation by 10.52% and the steady-state frequency deviation by 8.35% compared with traditional methods. Under random load disturbance conditions, the root mean square (RMS) value of frequency deviation is reduced by 7.34%, and the peak-to-valley difference of frequency decreases by 6.74%. Compared to energy storage without SOC self-recovery, the RMS values of SOC for flywheel storage and battery storage are reduced by 8.79% and 16.68%, respectively. The results demonstrate that the proposed control strategy effectively improves the system’s frequency regulation performance, reduces energy storage output fluctuations, and enhances the SOC self-recovery effect of the HESS. Full article

This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) in battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain in optimising battery longevity, energy efficiency, and safety. AI-driven techniques—such as machine learning (ML), neural networks (NNs), and reinforcement learning (RL)—enhance battery state of health (SOH) and state of charge (SOC) predictions, as well as temperature regulation, offering superior accuracy over traditional methods. Additionally, AI-powered control frameworks optimise energy distribution, regenerative braking, and power allocation under varying driving conditions. Deep RL enables adaptive, self-learning capabilities that improve energy efficiency and extend battery life, even in dynamic environments. This review also examines the integration of the Internet of Things (IoT) and big data analytics in EV systems, enabling predictive maintenance and fleet-level optimisation. By analysing these advancements, this paper highlights AI’s pivotal role in shaping next-generation, energy-efficient EVs. Full article

As the penetration of renewable energy sources (RESs) in power systems continues to increase, their volatility and unpredictability have exacerbated the burden of frequency regulation (FR) on conventional generator units (CGUs). Therefore, to reduce frequency deviations caused by comprehensive disturbances and improve system frequency stability, this paper proposes an integrated strategy for hybrid energy storage systems (HESSs) to participate in primary frequency regulation (PFR) of the regional power grid. Once the power grid frequency exceeds the deadband (DB) of the HESS, the high-frequency signs of the power grid frequency are managed by the battery energy storage system (BESS) through a division strategy, while the remaining parts are allocated to pumped hydroelectric energy storage (PHES). By incorporating positive and negative virtual inertia control and adaptive droop control, the BESS effectively maintains its state of charge (SOC), reduces the steady-state frequency deviation of the system, and provides rapid frequency support. When the system frequency lies within the DB of the HESS, an SOC self-recovery strategy restores the BESS SOC to an ideal range, further enhancing its long-term frequency regulation (FR) capability. Finally, a regional power grid FR model is established in the RT-1000 real-time simulation system. Simulation validation is conducted under three scenarios: step disturbances, short-term continuous disturbances, and long-term RES disturbances. The results show that the proposed integrated strategy for HESS participation in PFR not only significantly improves system frequency stability but also enhances the FR capability of the BESS. Full article

Ensuring efficiency and safety is critical when developing charging strategies for lithium-ion batteries. This paper introduces a novel method to optimize fast charging for cylindrical Li-ion NMC 3Ah cells, enhancing both their charging efficiency and thermal safety. Using Model Predictive Control (MPC), this study presents a cost function that estimates the thermal safety boundary of Li-ion batteries, emphasizing the relationship between the temperature gradient and the state of charge (SoC) at different temperatures. The charging control framework combines an equivalent circuit model (ECM) with minimal electro-thermal equations to estimate battery state and temperature. Optimization results indicate that at ambient temperatures, the optimal charging allows the cell’s temperature to self-regulate within a safe operating range, requiring only one additional minute to reach 80% SoC compared to a typical fast-charging protocol (high current profile). Validation through numerical simulations and real experimental data from an NMC 3Ah cylindrical cell demonstrates that the simple approach adheres to the battery’s electrical and thermal limitations during the charging process. Full article

Following the inauguration, the Trump administration authorized a series of anti-immigrant policies, including modifications to the public charge regulation. This study analyzed the effect of the publication of a proposed public charge rule in 2018 on the risk of preterm birth between uninsured and privately insured Latinx birthing people in the United States by using natality files from the National Center for Health Statistics. In total, 1,375,580 Latinx birthing people reported private insurance as their primary source of delivery from 2014 to 2019, while 317,056 Latinx birthing people reported self-pay as their primary source of delivery during the same period. After the publication of the proposed public charge rule in 2018, the odds of preterm birth among uninsured foreign-born Latinx birthing people increased by 6.2% compared with privately insured foreign-born Latinx birthing people (OR: 1.062; 95% CI: 1.016, 1.110). On the other hand, the odds of preterm births among uninsured US-born Latinx birthing people did not significantly increase after the publication of the proposed rule compared with privately insured US-born Latinx birthing people. These findings suggest the publication of the public charge rule proposed in 2018 may be associated with adverse birth outcomes among uninsured foreign-born Latinx birthing people in the United States. Full article

Power system stability has been suffering increasing threats with the ever-growing penetration of intermittent renewable generation such as wind power and solar power. Battery energy storage systems (BESSs) could mitigate frequency fluctuation of the power system because of their accurate regulation capability and rapid response. By dividing the Area Control Error (ACE) signal and the State of Charge (SOC) of battery energy storage systems into different intervals, the frequency control task of BESSs could be determined by considering the frequency control demand of the power grid in each interval and SOC self-recovery. The well-developed model predictive control can be employed to attain the optimal control variable sequence of BESSs in the following time, which can determine the output depths of BESSs and action timing at different intervals. The simulation platform MATLAB/Simulink is used to build two secondary frequency control scenarios of BESSs for providing frequency regulation service. The feasibility of the presented strategy is demonstrated by simulation results of a sample system. Full article

Nitrogen-doped multi-walled carbon nanotubes (N-MWCNTs) are widely used for drug delivery. One of the main challenges is to clarify their interaction with hypoxia-inducible factor 1 alpha (HIF-1α), the lack of which leads to oncological and cardiovascular diseases. In the presented study, N-MWCNTs were synthesized by catalytic chemical vapor deposition and irradiated with argon ions. Their chemical state, local structure, interfaces, Stone–Wales defects, and doping with nitrogen were analyzed by high resolution transmission electron microscopy (HRTEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. Using experimental data, supercells of functionalized N-MWCNTs with an oxygen content of 2.7, 4 and 6 at. % in carboxyl groups were built by quantum chemical methods. Our analysis by the self-consistent charge density functional tight-binding (SCC DFTB) method shows that a key role in the functionalization of CNTs with carboxyl groups belongs to Stone–Wales defects. The results of research in the decoration of CNTs with HIF-1α demonstrate the possibility of wave-diffusion drug delivery. The nature of hybridization and relaxation determines the mechanism of oxygen regulation with HIF-1α molecules, namely, by OH-(OH–C) and OH-(O=C) chemical bonds. The concentration dependence of drug release in the diffusion mode suggests that the best pattern for drug delivery is provided by the tube with a carboxylic oxygen content of 6 at. %. Full article

Photovoltaic (PV) power generation has developed rapidly in recent years. Owing to its volatility and intermittency, PV power generation has an impact on the power quality and operation of the power system. To mitigate the impact caused by the PV generation, an energy storage (ES) system is applied to the PV plants. The capacity configuration and control strategy based on the stochastic optimization method have become an important research topic. However, the accuracy of the probability distribution model is insufficient and a stochastic optimization method is rarely used in a control strategy. In this paper, a stochastic optimization method for the energy storage system (ESS) configuration considering the self-regulation of the battery state of charge (SoC) is proposed. Firstly, to reduce the sampling error when typical scenarios of PV power are generated, a time-divided probability distribution model of the ultra-short-term predicted error of PV power is established. On this basis, to solve the problem that SoC reaches the threshold frequently, a self-regulation model of the SoC based on multiple scenarios is established, which can regulate the SoC according to rolling PV power prediction. A stochastic optimization configuration model of the energy storage system is constructed, which can reduce the impact of PV uncertainty on the configuration result. Finally, the proposed stochastic optimization method is validated. The fitting error of the time-divided probability distribution model is 15.61% lower than that of the t-distribution. The expected revenue of the optimal configuration in this paper is 8.86% higher than the scheme with a fixed probability distribution model, and 16.87% higher than without considering the stochastic optimization method. Full article

The temporal resolution of the demand and generation profiles may have a significant impact on the estimation of self-sufficiency and self-consumption for consumers and prosumers. As an example, measuring the load profile, with a low temporal resolution, may lead to the under-estimation of energy consumption, while measuring solar irradiation with a low temporal resolution may lead to the over-estimation of on-site energy generation. Storage systems may reduce errors due to the lower temporal resolution by 8–10 times or even more, depending on the capacity of the batteries. Besides self-generation and self-consumption, there are other indicators that can be influenced by temporal resolution that deserve to be investigated. This is a detailed study of the influence of temporal resolution and the time averaging on a hybrid photovoltaic-battery system; this study encompasses both economic and technical aspects, from the calculation of savings on the electricity bill to the estimation of the equivalent cycles of battery storage system. To this end, the three-minute load profile of a real case study is used to obtain other three load profiles with temporal resolution equal to 15, 30, and 60 min via data averaging. Therefore, the authors analyze the influence and the impact of temporal resolution and data averaging in terms of: The size of the photovoltaic generator and the capacity of the storage system; the savings in the electricity bill and the balance between costs and savings; the peak values and the average values of power flows during high generation and low generation; the profile of the storage system over the year; the utilization rate of the storage system and the rated power of the electronic converter that regulates the charge and the discharge; the profile of the state of charge of the storage system and the life-time estimation of batteries through the calculation of the equivalent number of cycles. Full article

Energy management control is essential to microgrids (MGs), especially to single-phase ones. To handle the variety of distributed generators (DGs) that can be found in a MG, e.g., renewable energy sources (RESs) and energy storage systems (ESSs), a coordinated power regulation is required. The latter are generally battery-based systems whose lifetime is directly related to charge/discharge processes, whereas the most common RESs in a MG are photovoltaic (PV) units. Hybrid energy storage systems (HESS) extend batteries life expectancy, thanks to the effect of supercapacitors, but they also require more complex control strategies. Conventional droop methodologies are usually applied to provide autonomous and coordinated power control. This paper proposes a method for coordination of a single-phase MG composed by a number of sources (HESS, RES, etc.) using power line signaling (PLS). In this distributed control strategy, a signal whose frequency is higher than the grid is broadcasted to communicate with all DGs when the state of charge (SoC) of the batteries reaches a maximum value. This technique prevents batteries from overcharging and maximizes the power contribution of the RESs to the MG. Moreover, different commands apart from the SoC can be broadcasted, just by changing to other frequency bands. The HESS master unit operates as a grid-forming unit, whereas RESs act as grid followers. Supercapacitors in the HESS compensate for energy peaks, while batteries respond smoothly to changes in the load, also expanding its lifetime due to less aggressive power references. In this paper, a control structure that allows the implementation of this strategy in single-phase MGs is presented, with the analysis of the optimal range of PLS frequencies and the required self-adaptive proportional-resonant controllers. Full article