Typical sequential scenario analysis method for economic operation of microgrid

被引：0
|
作者：
Ding M. [1 ]
Xie J. [1 ]
Pan H. [1 ]
Chu M. [1 ]
机构：
[1] Anhui Key Lab of New Energy Utilization and Energy Conservation, Hefei University of Technology, Hefei
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2017年 / 37卷 / 04期
关键词：
Clustering; Distributed power generation; Microgrid; Operation optimization; Scenario analysis; Typical scenario;
D O I：
10.16081/j.issn.1006-6047.2017.04.002
中图分类号：
学科分类号：
摘要：
The economic operation of microgrid involves the analysis and evaluation of different scenarios and schemes. The sequential, periodic and uncertain variation of regional loads and wind/solar power outputs affects the operation of microgrid. Typical scenario analysis method is proposed, which partitions and clusters synchronously the massive original data of wind/solar power outputs and loads for the computation period to form the typical scenarios for reflecting the characteristics of historical data. An economic operation optimization model is established for the microgrid with different distributed generations and energy storages. The results of economic operation optimization based on the scenario data obtained by the typical sequential scenario method, the complete-period sequential scenario method and the simplified scenario method for a microgrid are compared to verify the effectiveness of the proposed method. © 2017, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码：11 / 16
页数：5
相关论文
共 16 条
  • [1] Laasseter R.H., Akhil A., Marnay C., Et al., Integration of Distributed Energy Resources: The CERTS Microgrid Concept, (2002)
  • [2] Liu C., Wang X., Liu S., Et al., Economic dispatch model considering battery lifetime for microgrid, Electric Power Automation Equipment, 35, 10, pp. 29-36, (2015)
  • [3] Dou X., Yuan J., Wu Z., Et al., Improved configuration optimization of PV-wind-storage capacities for grid-connected microgrid, Electric Power Automation Equipment, 36, 3, pp. 26-32, (2016)
  • [4] Liu X., Ding M., Zhang Y., Et al., Dynamic economic dispatch for microgrids, Proceedings of the CSEE, 31, 31, pp. 77-84, (2011)
  • [5] Ding M., Zhang Y., Mao M., Et al., Economic operation optimization for microgrids including Na/S battery storage, Proceedings of the CSEE, 31, 4, pp. 7-14, (2011)
  • [6] Wang T., Bi T., Wang H., Et al., Decision tree based online stability assessment scheme for power systems with renewable generations, CSEE Journal of Power & Energy Systems, 1, 2, pp. 53-61, (2015)
  • [7] Wang J., Chen J., Shu H., Microgrid capacity configuration optimization based on reliability, Electric Power Automation Equipment, 34, 4, pp. 120-127, (2014)
  • [8] Ding M., Wang B., Zhao B., Et al., Configuration optimization of capacity of standalone PV-wind-diesel-battery hybrid microgrid, Power System Technology, 37, 3, pp. 575-581, (2013)
  • [9] Ma X., Wu Y., Fang H., Et al., Optimal sizing of hybrid solar-wind distributed generation in an islanded microgrid using improved bacterial foraging algorithm, Proceedings of the CSEE, 31, 25, pp. 17-25, (2011)
  • [10] Li L., Tang W., Bai M., Et al., Multi-objective locating and sizing of distributed generators based on time-sequence characteristics, Automation of Electric Power Systems, 37, 3, pp. 58-63, (2013)
12