An Intelligent Scheduling Approach for Electric Power Generation. Issue 6 (1st November 2018)

• Record Type: Journal Article
• Authors: Yu, Li; Jin, Xin; Li, Zeng; Xiong, Yan; Huang, Wenchao
• Is Part Of: Chinese journal of electronics. Volume 27:Issue 6(2018)
• Page Start: 1170
• Page End: 1175
• Publication Date: 2018-11-01
• Subjects: Deep learning -- Ant colony optimization (ACO) -- Smart grid; coal -- dynamic programming -- energy conservation -- neural nets -- power engineering computing -- power generation dispatch -- power generation scheduling -- thermal power stations; power generation scheduling tasks -- intelligent scheduling approach -- electric power generation -- clean energy -- nuclear power -- thermal power generation -- coal power -- historical power generation data -- economic load dispatch -- optimization problem -- power systems -- energy conservation -- intelligent power generation scheduling system -- deep neural network -- ant colony optimization -- DNN algorithm -- coal consumption; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2018.09.013
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.