A self-power generation scheduling model under load demand and uncertainty of a by-product of gas production in enterprises microgrid

被引：0
|
作者：
机构：
[1] State Key Laboratory for Manufacturing System Engineering (Xi'an Jiaotong University), Xi'an 710049, Shaanxi Province
[2] Ministry of Education Key Lab. For Intelligent Networks and Networks Security, Xi'an Jiaotong University, Xi'an 710049, Shaanxi Province
来源：
Liu, K. (kliu@sei.xjtu.edu.cn) | 1600年 / Chinese Society for Electrical Engineering卷 / 34期
关键词：
Gas production; Microgrid; Safety constraint; Scenario tree; Self-power generation optimal scheduling; Uncertainty;
D O I：
10.13334/j.0258-8013.pcsee.2014.13.007
中图分类号：
学科分类号：
摘要：
Scheduling of the self-generation plants is very helpful to the energy saving and emission reduction for enterprises microgrid. In this paper, the objective function was to minimize the overall cost of the electricity. The scenario tree was used to analyze the uncertainties of electricity load and gas supplication. The self-generation stochastic model was built. In this model, rectangle set was used to describe the safety constraint. In order to avoid that rectangle set is too conservative, we used ellipsoid set instead of rectangle set by adjusting risk parameter. State transition diagram of gas holder level was given to transform safety constraint into linear constraint. Results show that during off-peak periods, most of the enterprises load is afforded by the grid; and that during peak periods, most of load is afforded by the self-generation plants. In addition, using ellipsoid set instead of rectangle set reduces decision conservatism by adjusting the risk coefficient. © 2014 Chinese Society for Electrical Engineering.
引用
收藏
页码：2063 / 2070
页数：7
相关论文
共 24 条
  • [1] Gao Y., Pan J., Ji G., Et al., A time-series modeling method based on the boosting gradient-descent theory, Science China, 54, 5, pp. 1325-1337, (2011)
  • [2] Gao Y., Gao F., Zhai Q., Et al., Balance analysis of large-scale power consuming corporation, Proceedings of the CSEE, 29, 19, pp. 70-77, (2009)
  • [3] Noldel K., Morari M., Electrical load tracking scheduling of a steelplant, Computers and Chemical Engineering, 34, 11, pp. 1899-1903, (2010)
  • [4] Hait A., Artigues C., On electrical load tracking scheduling for a steel plant, Computers and Chemical Engineering, 35, 12, pp. 3044-3047, (2011)
  • [5] Ashok S., Peak-load management in steel plants, Applied Energy, 83, 5, pp. 413-424, (2006)
  • [6] Babu C.A., Ashok S., Peak load management in electrolytic process industries, IEEE Trans. on Power Systems, 23, 2, pp. 399-405, (2008)
  • [7] Wang Z., Gao F., Zhai Q., Et al., An integrated optimization model for generation and batch production load scheduling in energy intensive enterprise, Power and Energy Society General Meeting, 2012 IEEE, pp. 1-8, (2012)
  • [8] Fu H., Wang F., Based on Kalman filtering theory gas forecast system research, 2010 Third International Conference on Information and Computing, pp. 69-72, (2010)
  • [9] Zhang X., Zhao J., Wang W., Et al., COG holder level prediction model based on least on square support vector machine and its application, Control and Decision, 25, 8, pp. 1178-1188, (2010)
  • [10] Zhang Q., Gu Y., Ti W., Et al., Supply-demand forecasting model of blast furnace gas in iron & steel works and its application, Journal of Northeastern University, 31, 12, pp. 1737-1740, (2010)
123