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Introduction: The characteristics of intermittency and volatility brought by a high proportion of renewable energy impose higher requirements on load forecasting in modern power system. Currently, load forecasting methods mainly include statistical models and machine learning methods, but they exhibit relative rigidity in handling the uncertainty, volatility, and nonlinear relationships of new energy, making it difficult to adapt to instantaneous load changes and the complex impact of meteorological factors. The Transformer model, as an algorithm used in natural language processing, with its self-attention mechanism and powerful nonlinear modeling capability, can help address the aforementioned issues. Methods: However, its current performance in time series processing is suboptimal. Therefore, this paper improves the Transformer model through two steps, namely, Data-Slicing and Channel-independence, enhancing its adaptability in load forecasting. Results: By using load data from Northern Ireland as an example, we compared GRU, CNN, and traditional Transformer models. We validated the effectiveness of this algorithm in short-term load forecasting using MAPE and MSE as indicators. Discussion: The results indicate that, in short-term load forecasting, the MDS method, compared to GRU, CNN, and traditional Transformer methods, has generally reduced the MSE by over 48%, and achieved a reduction of over 47.6% in MAPE, demonstrating excellent performance. [ABSTRACT FROM AUTHOR]

In recent years, selective laser melting (SLM) has gained an important place in fabrication due to their strong individualization which cannot be manufactured using conventional processes such as casting or forging. By proper control of the SLM processing parameters, characteristics of the alloy can be optimized. In the present work, 316L stainless steel (SS), as a widely used biomedical material, is investigated in terms of the effects of scanning speed on in vitro biocompatibility during SLM process. Cytotoxicity assay is adopted to assess the in vitro biocompatibility. The results show the scanning speed strongly affects the in vitro biocompatibility of 316L SS parts and with prolongs of incubation time, the cytotoxicity increase and the in vitro biocompatibility gets worse. The optimal parameters are determined as follows: scanning speed of 900 mm/s, laser power of 195 W, hatch spacing of 0.09 mm and layer thickness of 0.02 mm. The processing parameters lead to the change of surface morphology and microstructures of samples, which can affect the amount of toxic ions release, such as Cr, Mo and Co, that can increase risks to patient health and reduce the biocompatibility.

Summary: In order to accommodate additional plug‐in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle‐to‐grid (V2G) technology has been regarded as a cost‐effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users' privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub‐problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak‐shaving and valley‐filling scheduling algorithm is built up. The proposed algorithm can be conducted in real‐time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak‐shaving and valley‐filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak‐shaving and valley‐filling strategy is near‐linear, which verifies the proposed scheme can be carried out very efficiently. [ABSTRACT FROM AUTHOR]

With an ever‐increasing number of plug‐in electric vehicles (PEVs), there is a fast‐growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load peaks, increasing energy losses, and decreasing node voltage. However, the computational complexity is a critical issue to obtain the coordinated charging strategies especially for a large number of PEVs. In this context, it is very essential to analyze the computational performance of the centralized coordinated charging methods. In this paper, a paradigm for analyzing the computational performance is provided. Three centralized methods with different standpoint, viz., to minimize carbon emissions, to minimize load variance, and to minimize generation cost, are investigated to conduct a computational performance analysis from the grid operator perspective. First, the optimization theory is employed to transform the three engineering problems into the mathematical programming models. Then, whether the mathematical programming models are convex or nonconvex is analyzed. The results show that the first two mathematical programming models are convex, and the third mathematical programming model is nonconvex. And it demonstrates that the centralized scheduling model that is convex programming has a better computational performance theoretically. At last, simulations are carried out to verify the theoretical computational performance for different types of centralized coordinated charging methods. [ABSTRACT FROM AUTHOR]