YU Li, JIN Xin, LI Zeng, XIONG Yan, HUANG Wenchao. An Intelligent Scheduling Approach for Electric Power Generation[J]. Chinese Journal of Electronics, 2018, 27(6): 1170-1175. doi: 10.1049/cje.2018.09.013
Citation: YU Li, JIN Xin, LI Zeng, XIONG Yan, HUANG Wenchao. An Intelligent Scheduling Approach for Electric Power Generation[J]. Chinese Journal of Electronics, 2018, 27(6): 1170-1175. doi: 10.1049/cje.2018.09.013

An Intelligent Scheduling Approach for Electric Power Generation

doi: 10.1049/cje.2018.09.013
Funds:  This work is supported by the National Natural Science Foundation of China (No.61520106007).
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  • Corresponding author: XIONG Yan (corresponding author) received the B.S., M.S., and Ph.D. degrees from the University of Science and Technology of China, in 1983, 1986, and 1990, respectively. He is currently a professor with the School of Computer Science and Technology, University of Science and Technology of China. His main research interests include distributed processing, mobile computing, computer network and information security. (Email:yxiong@ustc.edu.cn)
  • Received Date: 2018-05-18
  • Rev Recd Date: 2018-06-08
  • Publish Date: 2018-11-10
  • Although China is vigorously developing clean energy and nuclear power, the thermal power generation (mainly coal power) is still the most important power generation method at present. Economic load dispatch (ELD) is a typical optimization problem in power systems which lots of researchers are trying to explore. The purpose of ELD is to increase the efficiency of thermal power generation under the conditions of load and operational constraints. When it comes to power generation scheduling, manual operation is still the main form, which is inefficient. In order to use a large amount of historical power generation data to improve the efficiency of power generation scheduling and achieve the effect of energy conservation, we propose an intelligent power generation scheduling system based on Deep neural networks (DNN) and Ant colony optimization (ACO). Experiments show that our DNN algorithm can predict the unit coal consumption precisely. Compared with the dynamic programming algorithm and equal differential increment rate algorithm, ACO can complete power generation scheduling tasks more quickly and efficiently.
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