Sponsor(s): Northeastern University
12 issues per year
Current Issue: Issue 09, 2020
Established in 1986, the Chinese journal of Control and Decision is sponsored by Northeastern University and under the administration of the Ministry of Education of China. The journal is published monthly with a total of 256 pages in A4 size, and is distributed at home and abroad. For more than 30 years, "Control and Decision" has adhered to its mission and purpose, and has committed to gathering and disseminating outstanding academic achievements, inspiring scientific and technological innovations, and advancing the development of China's academic disciplines. The journal has published a great number of high-standard, original, and outstanding research achievements, both in theory and in practical applications, in the field of control and decision-making. With a good academic reputation in both academia and industry, high academic influence, and excellent level of publications, "Control and Decision" has attained top rankings in both core impact factors and comprehensive evaluation indicators. The journal has received many prestigious awards including "Top 100 Outstanding Academic Journals of China", "China's Most Influential Academic Journals”, and so on, and has been included in key national and international databases. In November 2019, the journal was selected for the “China Science and Technology Journal Excellence Action Plan”, a national project jointly implemented by the China Association for Science and Technology, the Ministry of Finance, the Ministry of Education, the Ministry of Science and Technology, the General Administration of Press and Publication, the Chinese Academy of Sciences and the Chinese Academy of Engineering. This will be yet another great motivation for “Control and Decision” to contribute relentlessly to the construction of the Chinese science and technology journals system of open innovation, cooperation, and world-class.
Editor in Chief: Filiwang, Guang-Hong Yang
Control and Decision,2020,Vol 35,No. 09
With the access of large-scale renewable energy to microgrid (MG), its uncertainty directly affects the optimal scheduling of MG. In this paper, aiming at maximizing the generation profits of MG, an optimization model of day-ahead generation scheduling for MG is constructed, with consideration of the energy storage, demand response, and error processing of renewable generation prediction. Then, an improved artificial bee colony algorithm based on crossover and mutation is proposed to solve this problem. In the employed bee phase and onlooker bee phase, crossover and mutation are introduced to improve the neighborhood search strategy, in order to ensure the diversity of offspring population. In the scout bee phase, an initialization mechanism based on global search is constructed to improve the ability of searching global optimal solution. Finally, the simulation results demonstrate the effectiveness of the model and the superiority of the algorithm.