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In this study, a cold storage container was simulated with the aim of improving the freezing process through innovative techniques. To accelerate the freezing rate, porous foam was introduced within the storage unit, while water was enhanced by the dispersion of hybrid nanopowders. Additionally, radiative cooling was incorporated as a method to further expedite solidification. The numerical model, validated through the Galerkin method, demonstrated high accuracy and reliability. Results showed a noteworthy decrement in freezing time by about 65.78% when using porous foam, highlighting its role in enhancing thermal conductivity within the system. The incorporation of radiative cooling increased the freezing rate by around 57%, providing further evidence of its effectiveness in speeding up solidification. Moreover, the addition of nanopowders to the H 2 O mixture resulted in a 4.24% decrease in period time. The prominence of this work lies in its ability to significantly enhance cold energy storage systems by combining multiple advanced techniques. This innovative approach not only improves efficiency but also offers potential applications in fields such as cryogenics and thermal management.

Pumped storage plants (PSPs) play an important role in renewable energy consumption in power systems. Variable‐speed technology is a new and critical direction for the development of PSPs. In pump mode, variable‐speed pumped storage units (VSPSUs) have wider power regulation ranges and more flexible power responses than fixed‐speed pumped storage units (FSPSUs); however, the corresponding quantification study of VSPSUs is rare. Therefore, this study aims to quantify the static and dynamic power regulation characteristics of VSPSU in pump mode. First, the modeling of the fast speed control strategy of VSPSU in pump mode is improved. Then, the power regulation range of the VSPSU is explored. The factors (i.e., power command, governor, and converter) affecting the power response rapidity are quantified through the engineering case of a variable‐speed PSP adopting the doubly fed induction machine. Accordingly, suggestions on the settings of the parameters are provided. Quantification results show that parameter tuning based on this study effectively solves the problem of low regulation rate caused by great power fluctuations under typical power regulation cases in pump mode. This VSPSU has the fastest regulation rate of 13.75%/s when adjusting 40% power. This study could provide technical support to the practical operation of VSPSU. Power regulation characteristics in pump mode of variable‐speed pumped storage unit.

One of the challenges for the commercialization of PCM-based cold storage systems is their ability to absorb load fluctuations, the ability for quick charge and discharge, as well as the potential for energy saving by reducing the compressor running time. The present work describes the possibilities for energy conservation through the experimental integration of latent thermal energy storage in an electricity-driven cold storage unit. A portable cold storage unit with a net volume of 1 m 3 (35 l) was retrofitted with a PCM-based heat exchanger unit. The unit was designed to maintain the temperature inside the storage space at 9–10 °C for 1 h using an organic phase change material. The PCM-based heat exchange surface embedding was created to consider the maximum surface exposure area and minimum thickness to reduce the charging (freezing) and discharging (melting) periods. Experiments were carried out with and without PCM to observe the backup time given by the PCM unit, the frequency of on–off compressor cycles, and the potential for energy and economic savings. Based on a six-hr trial at a set temperature of 9 °C, it was found that the designed heat exchanger unit provided a stable temperature for 55 min on compressor shut down at an average ambient temperature of 35° C, thus validating the design. The compressor on–off cycles are reduced from 6 to 1 per hour, compressor on time is reduced by 31%, and an energy saving of about 40% is obtained with PCM integration. The energy savings per kg of PCM is about 5.5% which is the highest reported for cold storage in the literature to date.

In the recent decade, a significant increase in the penetration level of renewable energy sources (RESs) into the distribution grid is evident due to the world's shift towards clean energy and to increase the reliability or inboard manner resiliency of electrical distribution system. RES based microgrids are the most favorable option available, especially to enhance resiliency. However, the integration of RES over the distribution grid would hamper the grid stability due to its stochastic nature under normal conditions. During extreme weather conditions, RES behavior is completely uncertain. Hence there is a need to eliminate the adverse effects caused by the RES and make the distribution grid more reliable and stable under normal and resilient conditions. To address these issues, many researchers proposed several methods to place energy storage units (ESUs) and microgrids (RES integrated), which can support critical loads at an optimal location in the distribution system during normal and extreme conditions, respectively. The aim of this article is to consolidate and review the research towards various approaches to formulate the problem (optimal location, allocation, and operation of ESU and microgrids to face regular and extreme weather condition) and tools to solve it for enhanced system flexibility and resiliency. Based on the review, a generalized methodology has been designed to adapt the inputs and address both conditions. At the end of the review, future aspects for ESU to strengthen resistance and resiliency of its own are presented, which can be helpful to further improve the reliability and resiliency of the distribution system.

Electric Vehicles (EV) usage is increasing over the last few years due to a rise in fossil fuel prices and the rate of increasing carbon dioxide (CO2) emissions. The EV charging stations are powered by the existing utility power grid systems, increasing the stress on the utility grid and the load demand at the distribution side. The DC grid-based EV charging is more efficient than the AC distribution because of its higher reliability, power conversion efficiency, simple interfacing with renewable energy sources (RESs), and integration of energy storage units (ESU). The RES-generated power storage in local ESU is an alternative solution for managing the utility grid demand. In addition, to maintain the EV charging demand at the microgrid levels, energy management and control strategies must carefully power the EV battery charging unit. Also, charging stations require dedicated converter topologies, control strategies and need to follow the levels and standards. Based on the EV, ESU, and RES accessibility, the different types of microgrids architecture and control strategies are used to ensure the optimum operation at the EV charging point. Based on the above said merits, this review paper presents the different RES-connected architecture and control strategies used in EV charging stations. This study highlights the importance of different charging station architectures with the current power converter topologies proposed in the literature. In addition, the comparison of the microgrid-based charging station architecture with its energy management, control strategies, and charging converter controls are also presented. The different levels and types of the charging station used for EV charging, in addition to controls and connectors used in the charging station, are discussed. The experiment-based energy management strategy is developed for controlling the power flow among the available sources and charging terminals for the effective utilization of generated renewable power. The main motive of the EMS and its control is to maximize usage of RES consumption. This review also provides the challenges and opportunities for EV charging, considering selecting charging stations in the conclusion.

Pumped storage units (PSUs) often operate away from optimal working conditions to accommodate complex and variable operational tasks. This characteristic inherently results in a significantly higher probability of failure compared to conventional hydropower units. However, existing fault diagnosis studies commonly suffer from limitations such as single application scenarios, inadequate extraction of abnormal state information, and a lack of fault data, making them difficult to implement in real-world power station operations. To address these challenges, this study proposes a specialized abnormal information feature extraction tool for PSUs based on nonlinear dynamics and tensor learning algorithms, and further develops an intelligent fault diagnosis model using machine learning. Firstly, a novel nonlinear dynamic method named weighted fuzzy entropy (WFE) is introduced by integrating the root mean square and fuzzy entropy. This method characterizes time-series signal variations from both complexity and amplitude perspectives. Secondly, based on tensor learning theory, the WFE is extended to both the time and frequency domains, leading to the development of tensor-weighted fuzzy entropy (TWFE), which enables multi-dimensional extraction of abnormal information. Finally, an intelligent fault diagnosis model for PSUs established, incorporating data acquisition, feature extraction, and model recognition, with TWFE and random forests. In the intelligent operation and maintenance of a micro pumped storage power station, the proposed model successfully identifies hydraulic-mechanical faults such as vortex rope, imbalance, and rubbing, achieving a diagnostic accuracy of over 90%. The findings of this study provide valuable insights for enhancing the inspection and monitoring capabilities of PSUs.

The new energy power generation is becoming increasingly important in the power system. Such as photovoltaic power generation has become a research hotspot, however, due to the characteristics of light radiation changes, photovoltaic power generation is unstable and random, resulting in a low utilization rate and directly affecting the stability of the power grid. To solve this problem, this paper proposes a coordinated control strategy for a new energy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage unit. Firstly, the variational mode decomposition algorithm is used to separate the high and low frequencies of the power signal, which is conducive to the rapid and accurate suppression of the power fluctuation of the energy storage system. Secondly, the fuzzy control algorithm is introduced to balance the power between energy storage. In this paper, the actual data is used for simulation, and the simulation results show that the strategy realizes the effective suppression of the bus voltage fluctuation and the accurate control of the internal state of the energy storage unit, effectively avoiding problems such as overshoot and over-discharge, and can significantly improve the stability of the photovoltaic power generation system and the stability of the Direct Current bus. It is of great significance to promote the development of collaborative control technology for photovoltaic hybrid energy storage units.

As the core component of pumped storage stations (PSS), pumped storage units (PSU) require a scientific and comprehensive evaluation method to guide the selection of optimal units and support the development of the new-type power system (NPS). This paper aims to address the symmetry issues in PSU evaluation methods by proposing an innovative approach based on evolutionary combination weighting and cloud model theory, thereby adapting to the long-term asymmetric evolution of the power system. First, the subjective and objective weights of indicators at all levels for PSU are obtained using the analytic hierarchy process (AHP) and the entropy weight method (EWM). Then, the optimal combination coefficients for subjective and objective weights are determined through game theory, achieving symmetry and balance between the subjective and objective weights. Subsequently, dynamic correction of the indicator weights is realized using a designed evolutionary response function, enabling the weights to evolve dynamically in response to the asymmetric development of the power system. Finally, the cloud model is employed to characterize the randomness and fuzziness of evaluation boundaries, which enhances the adaptability of the evaluation process and the interpretability of results. The simulation results show that, when considering the long-term asymmetric evolution of the power system, the expected score deviations of secondary indicators are approximately 4.7%, 1.3%, 3.5%, and 7.7%, respectively, with an overall score deviation of about 6.4%. The proposed method not only achieves symmetry and balance between subjective and objective factors in traditional evaluation but also accommodates the asymmetric evolution requirements of the power system.

In high-renewable-energy power systems, the demand for fast-responding capabilities is growing. To address the limitations of conventional closed-loop frequency control, where the integral coefficient cannot dynamically adjust the frequency regulation command based on the state of charge (SoC) of energy storage units, this paper proposes a secondary frequency regulation control strategy based on variable integral coefficients for multiple energy storage units. First, a power-uniform controller is designed to ensure that thermal power units gradually take on more regulation power during the frequency regulation process. Next, a control framework based on variable integral coefficients is proposed within the secondary frequency regulation model, along with an objective function that simultaneously considers both Automatic Generation Control (AGC) command tracking performance and SoC recovery requirements of energy storage units. Finally, a gradient descent optimization method is used to dynamically adjust the gain of the energy storage integral controller, allowing multiple energy storage units to respond in real-time to AGC instructions and SoC variations. Simulation results confirm the effectiveness of the proposed method. Compared to traditional strategies, the proposed approach takes into account the SoC discrepancies among multiple energy storage units and the duration of system net power imbalances. It successfully implements secondary frequency regulation while achieving dynamic power allocation among the units.

The full-size converter-based variable-speed pumped storage unit (FSC-VSPSU) is widely regarded as the future direction of variable-speed pumped storage technology due to its wide operating range and fast switching capabilities. However, previous studies often assume a constant DC-link voltage, which is not applicable to FSC-VSPSU, as fluctuations in grid-side active power can affect the DC-link voltage, thereby threatening system stability. To address this issue, this article proposes a crowbar-less low-voltage ride-through (LVRT) control strategy for FSC-VSPSU. The proposed approach effectively mitigates the elevated system costs inherent in conventional crowbar circuit implementations by harnessing the significant energy storage potential of the rotor to absorb power imbalances during LVRT. Furthermore, a novel parameter design methodology for the DC-link voltage controller is introduced to guarantee that the DC-link voltage consistently remains within the allowable threshold range during LVRT. The effectiveness of the proposed control strategy and the accuracy of the parameter design methodology have been validated through MATLAB(R2023a)/Simulink.