Search

The utilization of a new energy hydrogen production system is an effective approach to enhance the absorption capacity of renewable energies such as wind and solar power. The current research on energy management of electrolyzer, both domestically and internationally, primarily focuses on single-electrolyzer. The energy management of single-electrolyzer fails to account for the nonlinearity in its operational characteristics, thereby posing challenges in considering the hydrogen production efficiency of multi-electrolyzers and its impact on system economics. The present study focuses on the energy management of a novel hydrogen production system incorporating multi-electrolyzers. The energy management optimization model incorporates wind power, photovoltaic systems, batteries, and multiple electrolyzers to achieve targets for new energy consumption rate, economic income, and hydrogen production rate. Taking into account the operational characteristics of a single electrolyzer and production constraints, the multi-objective optimization problem is solved by strength Pareto evolutionary algorithm 2 (SPEA2). The simulation research demonstrates that the proposed energy management strategy can achieve a 100% absorption rate of newly generated power from renewable sources, while simultaneously increasing the hydrogen production efficiency per unit by 5.15%. The effective management of energy in a multi-electrolyzers hydrogen production system is crucial for enhancing the efficiency of hydrogen production and effectively addressing the limitations associated with single-electrolyzer operation and energy management.

With the increasing penetration rate of renewable energy, the inertia of the power grid gradually decreases, and the transient support capacity is seriously insufficient. It brings a series of security problems to the power grid. A distributed grid-connected topology cascaded by energy storage and open-winding permanent magnet synchronous generator (OW-PMSG) is proposed in this paper. By the topology, the renewable energy can be consumed. At the same time, the diesel engines can provide power backup and inertia support. By modelling the OW-PMSG and analyzing the power flow vector, a power transfer strategy based on the source-grid phase closed-loop control is designed to achieve stable synchronous operation of the generator. Based on this strategy, the energy storage converter can have synchronous generator characteristics, which achieves further improvement of grid inertia and transient support capability. The simulation results verify the feasibility of the proposed control strategy.

Under the ′dual carbon′ goal, the transformation of the power system is accelerating, and wind power prediction technology is of great significance to the construction of a new power system with a high proportion of new energy. In order to improve the accuracy and robustness of wind power prediction, an numerical weather predicition (NWP) implicit correction algorithm based on multi-objective collaborative training is proposed. Firstly, the necessity of NWP correction and the problems of the two-step prediction method based on NWP explicit correction are analyzed. Then, aiming at the problems existing in the two-step prediction method, the optimization method based on multi-objective collaborative training uses the neural network to perform NWP implicit correction, train the model in an end-to-end manner, and realize the functions of NWP implicit correction and wind power prediction at the same time. Combined with the measured data of a wind farm, the specific calculation case analysis proves that the proposed algorithm has an improving effect on short-term, medium- and long-term wind power prediction. In addition, the algorithm only requires one network and avoids secondary calculation, saving computing and storage costs.

Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input. [ABSTRACT FROM AUTHOR]

In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence factor is designed to strike a balance between global exploration and local exploitation capabilities. This factor enables the algorithm to effectively explore the solution space while exploiting the promising regions to refine the search. Furthermore, an adaptive position update strategy is developed by combining Levy flight and golden sine. This strategy enhances the algorithm's solution accuracy, global search capability, and search speed. Levy flight allows longer jumps to explore distant regions, while golden sine guides the search towards the most promising areas. Extensive simulations on 16 standard benchmark functions demonstrate the effectiveness of the proposed HI-GWO algorithm. The results indicate that the HI-GWO algorithm outperforms other state-of-the-art intelligent algorithms in terms of optimization performance. Moreover, the performance of the HI-GWO algorithm is evaluated in a real-world path planning experiment, where a comparison with the traditional grey wolf algorithm and ant colony algorithm validates the superior efficiency of the improved algorithm. It exhibits excellent optimization ability, robust global search capability, high convergence accuracy, and enhanced robustness in diverse and complex scenarios. The proposed HI-GWO algorithm contributes to advancing the field of mobile robot path planning by providing a more effective and efficient optimization approach. Its improvements in convergence accuracy, iteration speed, and robustness make it a promising choice for various practical applications. [ABSTRACT FROM AUTHOR]

Ultrasensitive electrochemical detection on hazardous substances like antibiotics and pesticides is essential but still challenging in rapid test technology. Herein, the first electrode using highly conductive metal–organic frameworks (HCMOFs) for electrochemical detection of chloramphenicol is proposed. The design of electrocatalyst Pd(II)@Ni3(HITP)2 with ultra‐sensitivity in detection of chloramphenicol is demonstrated by loading Pd onto HCMOFs. An ultra‐low limit of detection (LOD) of 0.2 nM (64.6 pg/mL) was observed for these materials, which is 1–2 orders of magnitude lower than the other reported materials for chromatographic detection. Moreover, the proposed HCMOFs showed long‐time stability over 24 h. The superior detection sensitivity can be attributed to the high conductivity of Ni3(HITP)2, and the large Pd loading. The experimental characterizations and computational investigation determined the Pd loading mechanism in Pd(II)@Ni3(HITP)2, revealing PdCl2 adsorption on the abundant adsorption sites of Ni3(HITP)2. The proposed electrochemical sensor design using HCMOFs was demonstrated to be both effective and efficient, showing that the adoption of HCMOFs decorated with other effective electrocatalysts, which combine high conductivity and high catalytic activity, is of great advantage for ultrasensitive detection. [ABSTRACT FROM AUTHOR]