Application of swarm intelligence algorithms to energy management of prosumers with wind power plants

The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers

On the role of microsomal aldehyde dehydrogenase in metabolism of aldehydic products of lipid peroxidation

AbstractTo elucidate a possible role of membrane-bound aldehyde dehydrogenase in the detoxication of aldehydic products of lipd peroxidation, the substrate specificity of the highly purified microsomal enzyme was investigated. The aldehyde dehydrogenase was active with different aliphatic aldehydes including 4-hydroxyalkenals, but did not react with malonic dialdehyde. When Fe/ADP-ascorbate-induced lipid peroxidation of arachidonic acid was carried out in an in vitro system, the formation of products which react with microsomal aldehyde dehydrogenase was observed parallel with malonic dialdehyde accumulation

Improving accuracy and generalization performance of small-size recurrent neural networks applied to short-term load forecasting

The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

High-confidence glycosome proteome for procyclic form <em>Trypanosoma brucei</em> by epitope-tag organelle enrichment and SILAC proteomics

The glycosome of the pathogenic African trypanosome Trypanosoma brucei is a specialized peroxisome that contains most of the enzymes of glycolysis and several other metabolic and catabolic pathways. The contents and transporters of this membrane-bounded organelle are of considerable interest as potential drug targets. Here we use epitope tagging, magnetic bead enrichment, and SILAC quantitative proteomics to determine a high-confidence glycosome proteome for the procyclic life cycle stage of the parasite using isotope ratios to discriminate glycosomal from mitochondrial and other contaminating proteins. The data confirm the presence of several previously demonstrated and suggested pathways in the organelle and identify previously unanticipated activities, such as protein phosphatases. The implications of the findings are discussed

Improvement of rock crushing quality based on load specifications set for electrically-driven hydraulic drilling rigs

The analysis was completed on the main production factors which determine energy characteristics of an electrically-driven hydraulic roller-bit drilling rig during drilling operations under the condition of the Far North deposit. In addition, the correlation ratio between the parameters and electric energy consumption was analyzed for the DM-H drilling rig. The equations expressing the relation of the drilling rig load to the drilling speed have been identified. In order to improve the quality of rock crushing, we have modified the method of determining the properties and condition of the rock mass in order to correct the connection layout of the blasting circuit, the activation system, the change in the type of an explosive agent, as well as the composition and weight of the blasthole charge. The proposed approach allows reducing the cost of drilling and blasting operations by 6 % through the improvement in the accuracy of the designed physical and mechanical properties in terms of both the stratum depth and the strike of the mining block