Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation. Construction of an accurate and reliable prediction model for the volatility changes in energy consumption can provide valuable reference information for policy makers of the government and for the energy industry. In view of this, a novel improved model is developed in this article by integrating the modified state transition algorithm (MSTA) with the Gaussian processes regression (GPR) approach for non-fossil energy consumption predictions for China at the end of the 13th Five-Year Project, in which the MSTA is utilized for effective optimization of hyper-parameters in GPR. Aiming for validating the superiority of MSTA, several comparisons are conducted on two well-known functions and the optimization results show the effectiveness of modification in the state transition algorithm (STA). Then, based on the latest statistical renewable energy consumption data, the MSTA-GPR model is utilized to generate consumption predictions for overall renewable energy and each single renewable energy source, including hydropower, wind, solar, geothermal, biomass and other energies, respectively. The forecasting results reveal that the proposed improved GPR can promote the forecasting ability of basic GPR and obtain the best prediction effect among all the other comparison models. Finally, combined with the forecasting results, the trend of each renewable energy source is analyzed.
At present, the distribution network is in an important opportunity period of rapid development and improvement of efficiency. A large amount of new energy and distributed energy are connected to the distribution network, which is beyond the capacity of some power grids. Therefore, the influence of nearby access of distributed power sources such as wind power and solar power on the distribution network is taken into account. This paper evaluates and studies the adaptability of distribution network after distributed power supply is connected to distribution network. Firstly, a new definition of the adaptability of distribution network is given. and the adaptability evaluation system of distribution network is sorted out and identified based on this, and the index system of adaptability evaluation of distribution network is constructed. Then the combined weight of the evaluation model is determined by the Analytic Hierarchy Process (AHP) and Entropy Weight method (EM). Then, a distribution network adaptability evaluation model based on quantum adaptive particle swarm optimization support vector machine (QAPSO-SVM) is constructed. Finally, through the comparative analysis of examples and the sensitivity analysis of the results, the superiority of the quantum adaptive particle swarm optimization support vector machine model in the adaptability evaluation of distribution network is proved.INDEX TERMS Distribution network adaptability, analytic hierarchy process, entropy weight method, combined weight, sensitivity analysis, QAPSO-SVM.
For the three-phase imbalance management, this paper points out that the capacity of static var generator (SVG) can be configured based on smart meter measurement data, and the SVGs coordinated control strategy is used to compensate the three-phase imbalance load of the distribution network. Communication bus and coordinated control strategy are introduced in the proposed system. so that the SVG connected downstream of the low-voltage line can use the remaining capacity after compensating its downstream unbalanced current to sequentially compensate for the insufficient capacity of the upstream SVG. The low voltage problem of each node is further reduced by the reverse power flows from the downstream SVG to the upstream SVG. Simulations for actual effect evaluation of the proposed compensation system were done in Matlab/Simulink. Validity of the proposed system is confirmed by the simulation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.