Selection scheme of electrochemical energy storage based on interval analytic hierarchy process method

被引:0
|
作者
Li J. [1 ]
Ma H. [1 ]
Tian C. [2 ]
Hui D. [1 ]
机构
[1] China Electric Power Research Institute, Beijing
[2] Electric Power Research Institute, State Grid Jilin Electric Power Company Limited, Changchun
来源
关键词
Battery energy storage; Characteristics of working condition; Energy storage selection; Photovoltaic power station; Stabilize fluctuation;
D O I
10.13336/j.1003-6520.hve.20160907003
中图分类号
学科分类号
摘要
The selection of energy storage is an important part of energy storage planning, which pursues the applicability of technology and the economy of investment cost. The process involves multiple decision index, and with incomplete information, qualitative and quantitative information doping and so on, so it is a complex decision problem with multi objective and multi attribute. Analytic hierarchy process (AHP) is a combination of qualitative and quantitative analysis, systematic, hierarchical analysis method, and it can be used to solve the problem of energy storage selection. The basic data of energy storage technology are presented in the form of interval or qualitative description, so the interval analytic hierarchy process (IAHP) which is a combination of interval numbers and analytic hierarchy process is adopted to improve the operability of the selection scheme. In the process of building selection model using the IAHP method, because there are not enough quantitative data to be used as a decision-making basis, subjective and objective combination weighting method is chosen for determining the weight of the index. Firstly, the weight of the first level decision index is determined based on the expert’s experience. Then, the difference of the response degree of the energy storage technology to the expected value of the working condition is considered to determine the second layer decision weights based on entropy method. Finally, the verification of the scheme is carried out, in the condition of stabilizing output fluctuation, based on a 20 MW PV power plant. The results show that the applicability of lithium battery is highest, the second is the liquid flow battery and the common lead acid battery, the sodium sulfur battery, the colloid battery and the lead carbon battery are relatively poor. Through the case analysis, it is proved that the method can realize the comprehensive quantitative comparison between different energy storage types, which can provide technical support for the energy storage planning in the theoretical method. © 2016, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
引用
收藏
页码:2707 / 2714
页数:7
相关论文
共 21 条
  • [1] Tian L., Li J., Cheng L., Wind farm ramp rate control using energy storage based on probability prediction, High Voltage Engineering, 41, 10, pp. 3233-3239, (2015)
  • [2] Liang L., Li J., Hui D., Optimization configuration for capacity of energy storage system in large-scale wind farm, High Voltage Engineering, 37, 4, pp. 930-936, (2011)
  • [3] Gao Z., Fan H., Xu S., Et al., Topology and SVPWM modulation strategy of the improved bi-directional Z-source converter for energy storage, High Voltage Engineering, 41, 10, pp. 3240-3248, (2015)
  • [4] Department of Energy Sandia National Laboratories, ES-Select™ Documentation and User's Manual, (2012)
  • [5] Barin A., Canha L.N., Abaide A.D.A.R., Et al., Selection of storage energy technologies in a power quality scenario-the AHP and the Fuzzy logic, pp. 3615-3620, (2009)
  • [6] Faouzi B.A., Nes H.H., Faten H., Analytic hierarchy process selection for batteries storage technologies, 2013 International Conference on Electrical Engineering and Software Applications (ICEESA), (2013)
  • [7] Wang C., Yu B., Xiao J., Et al., Sizing of energy storage systems for output smoothing of renewable energy systems, Proceedings of the CSEE, 32, 16, pp. 1-8, (2012)
  • [8] Ye J., Xue J., Wu F., Et al., Application analysis and capacity configuration of battery energy storage in renewable generation system, Chinese Journal of Power Sources, 37, 2, pp. 333-335, (2013)
  • [9] Wang C., Sun W., Yi T., Et al., Review on energy storage application planning and benefit evaluation methods in smart grid, Proceedings of the CSEE, 33, 7, pp. 33-41, (2013)
  • [10] Satty T.L., The Analytic Hierarchy Process, (1980)