• Energy Research
• 2021-2025close
• engineering and technology close
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<abstract> <p>The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.</p> </abstract>

The use of superconducting magnetic energy storage (SMES) has been widely reported in the literature to mitigate various load frequency control (LFC) issues in a multi-area power system. Most of these are thyristor based SMES, employing proportional type controllers. In this paper, a supervisory adaptive model predictive control scheme (AMPC) is proposed for a voltage source converter based relatively small rating SMES for LFC application. The reference power command for the SMES is derived from the AMPC in such a manner that it effectively handles the operational constraints of the SMES system. A representative first-order system emulating the inner loop dynamics of the SMES chopper system, is tuned offline via genetic algorithm (GA) and is used to derive dynamic constraints for the cost function in AMPC. The effectiveness of the scheme is demonstrated through simulations.

Abstract SRM (Switched Reluctance motor) is gaining much interest in industries due to its straightforward structure, low-cost manufacturability and dependability which makes it better than other electric machines. SRM drive is the most appropriate for variable speed tasks. Additionally, the performance of the current and speed control for the SRM driver framework could be negatively influenced by noise, disturbances, and inactivity of load torque. To solve this difficulty, this paper develop an ANFIS based Speed and Current control with Torque Ripple Minimization using Hybrid SSD-SFO for SRM. The main goal of this work is to obtain preferred current and speed performance of SRM with minimum torque ripple. For concurrent regulator of the speed and current, an ANFIS (Adaptive Neuro-Fuzzy Inference System) structure is employed which includes two controlling loops. The inside loop is regulated for control of current and the outside loop is regulated for control of speed even with perfect choice of switching angles. The dynamic conduct of SRM is studied to restrict the current and speed that reduces the ripple of torque. Hybrid SSD-SFO (social ski-diver based sunflower optimization) procedure is employed to achieve the parameter values of current and speed control of SRM. The proposed scheme is accomplished by MATLAB/ Simulink environment. The results show that the hybrid SSD-SFO scheme given the better performance compared to the SSD and SFO algorithm.

Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

To reduce plastic waste generation from failed product batches during industrial injection molding, the sustainable production of representative prototypes is essential. Interesting is the more recent hybrid injection molding (HM) technique, in which a polymeric mold core and cavity are produced via additive manufacturing (AM) and are both placed in an overall metal housing for the final polymeric part production. HM requires less material waste and energy compared to conventional subtractive injection molding, at least if its process parameters are properly tuned. In the present work, several options of AM insert production are compared with full metal/steel mold inserts, selecting isotactic polypropylene as the injected polymer. These options are defined by both the AM method and the material considered and are evaluated with respect to the insert mechanical and conductive properties, also considering Moldex3D simulations. These simulations are conducted with inputted measured temperature-dependent AM material properties to identify in silico indicators for wear and to perform cooling cycle time minimization. It is shown that PolyJetted Digital acrylonitrile-butadiene-styrene (ABS) polymer and Multi jet fusioned (MJF) polyamide 11 (PA11) are the most promising. The former option has the best durability for thinner injection molded parts, and the latter option the best cooling cycle times at any thickness, highlighting the need to further develop AM options.