دانلود مقاله ISI انگلیسی شماره 54647
ترجمه فارسی عنوان مقاله

تحقیق در مورد استراتژی برنامه ریزی مطلوب برای شبکه توزیع فعال با استفاده از بهینه سازی ذرات همراه با الگوریتم تغذیه باکتری ها

عنوان انگلیسی
Research on optimal schedule strategy for active distribution network using particle swarm optimization combined with bacterial foraging algorithm
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
54647 2016 10 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Electrical Power & Energy Systems, Volume 78, June 2016, Pages 637–646

چکیده انگلیسی

Comparing with the traditional distribution network, a significant feature of the active distribution network (ADN) is that the performance of distributed generation (DG) units, energy storage units and micro-grid (MG) in the network is controllable for the distribution network operator. Considering the characteristics of the distributed power supply and micro-grid, and giving full play to the advantages of distributed generation technology in the economic, environmental and energy aspects, this paper highlights an environmental protection and energy saving optimal schedule model for ADN. The scheduling model focuses on the minimum network loss, minimum voltage deviation and minimum difference between peak and valley load. In addition, the two stage algorithm is presented to solve the proposed multi-objective scheduling model of ADN. First, a set of Pareto solutions are obtained by using the proposed particle swarm optimization combined with bacterial foraging algorithm (PSO-BFO) to solve multi-objective optimization problems, then the optimal schedule strategy of ADN is gained through evaluating the Pareto solutions with entropy weight decision-making method. To avoid the search falling into local optimal solution, the two-value crossover operator is introduced to exchange the information among subpopulations and update the position of related particles. Meanwhile, the adaptive adjusting inertia constant strategy is used to improve the algorithm convergence speed. Finally, the case study results demonstrate the rationality of the proposed optimal schedule model and the validity of its solution algorithm for ADN.