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[1] Xu Qingshan, Xu Minjiao, Li Guodong, Jiang Ling, et al. Configuration optimization modelof multi-energy distributed generation system [J]. Journal of Southeast University (English Edition), 2017, 33 (2): 182-188. [doi:10.3969/j.issn.1003-7985.2017.02.010]
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Configuration optimization modelof multi-energy distributed generation system()
面向多源分布式混合发电系统的电源配置优化模型
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
33
Issue:
2017 2
Page:
182-188
Research Field:
Electrical Engineering
Publishing date:
2017-06-30

Info

Title:
Configuration optimization modelof multi-energy distributed generation system
面向多源分布式混合发电系统的电源配置优化模型
Author(s):
Xu Qingshan1 Xu Minjiao1 Li Guodong2 Jiang Ling2
1School of Electrical Engineering, Southeast University, Nanjing 210096, China
2Electric Power Research Institute of State Grid Tianjin Electric Power Company, Tianjin 300392, China
徐青山1 徐敏姣1 李国栋2 蒋菱2
1东南大学电气工程学院, 南京210096; 2国网天津市电力公司电力科学研究院, 天津300392
Keywords:
multi-energy complementation distributed generation(DG) optimal configuration energy management comprehensive evaluation index(CEI) analytic hierarchy process(AHP)
多能互补 分布式发电 配置优化 能量管理 综合评价指标 层次分析
PACS:
TM744
DOI:
10.3969/j.issn.1003-7985.2017.02.010
Abstract:
To integrate different renewable energy resources effectively in a microgrid, a configuration optimization model of a multi-energy distributed generation(DG)system and its auxiliary equipment is proposed. The model mainly consists of two parts, the determination of initial configuration schemes according to user preference and the selection of the optimal scheme. The comprehensive evaluation index(CEI), which is acquired through the analytic hierarchy process(AHP)weight calculation method, is adopted as the evaluation criterion to rank the initial schemes. The optimal scheme is obtained according to the ranking results. The proposed model takes the diversity of different equipment parameters and investment cost into consideration and can give relatively suitable and economical suggestions for system configuration. Additionally, unlike Homer Pro, the proposed model considers the complementation of different renewable energy resources, and thus the rationality of the multi-energy DG system is improved compared with the single evaluation criterion method which only considers the total cost.
为了有效整合微电网中不同新能源发电设备, 提出了一种面向多能互补分布式新能源发电系统及其辅助设备的电源配置优化模型.该模型首先确定电源配置的初始方案, 然后根据用户偏好对初始方案进行优选.通过层次分析指标权重计算方法得到各初始方案的综合评估指标值, 并对初始方案进行排序.针对不同型号设备参数多样性及投资成本的差异, 该方法可以给出相对合适、经济的电源配置方案建议.此外, 较之目前主流Homer Pro, 所提出的规划方法考虑了多种新能源之间互补性, 相比仅考虑综合成本的单一评价方式, 提高了多能源微网配置的合理性.

References:

[1] Karki R, Billinton R. Reliability/cost implications of PV and wind energy utilization in small isolated power systems[J]. IEEE Transactions on Energy Conversion, 2011, 16(4): 368-373. DOI:10.1109/60.969477.
[2] Borowy B S, Salameh Z M. Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system[J]. IEEE Transactions on Energy Conversion, 1996, 11(2): 367-373. DOI:10.1109/60.507648.
[3] Xu Q S, Zang H X, Bian H H. Establishment and feasibility researches of practical solar radiation model[J]. Acta Engergiae Solaris Sinica, 2011, 32(8): 1180-1185.(in Chinese)
[4] Ma Y W, Yang P, Guo H X, et al.Power source planning of wind-PV-biogas renewable energy distributed generation system[J].Power System Technology, 2012, 32(9): 9-14.(in Chinese)
[5] Koutroulis E, Kolokotsa D, Potirakis A, et al. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms[J]. Solar Energy, 2006, 80(9): 1072-1088. DOI:10.1016/j.solener.2005.11.002.
[6] Li H, Zang C, Zeng P, et al. A genetic algorithm-based hybrid optimization approach for microgrid energy management[C]//IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems. Shenyang, China, 2015: 1474-1478. DOI:10.1109/cyber.2015.7288162.
[7] Dufo-López R, Bernal-Agustín J L. Design and control strategies of PV-Diesel systems using genetic algorithms[J]. Solar Energy, 2015, 79(1): 33-46. DOI:10.1016/j.solener.2004.10.004.
[8] Khatod D K, Pant V, Sharma J. Evolutionary programming based optimal placement of renewable distributed generators[J]. IEEE Transactions on Power Systems, 2013, 28(2): 683-695. DOI:10.1109/tpwrs.2012.2211044.
[9] Li C B, Chang H, Feng X, et al. Expansion planning of wind-thermal co-generation system in microgrid[J]. Electric Power Automation Equipment, 2014, 34(3): 47-51.(in Chinese)
[10] Dursun B, Gokcol C, Umut I, et al. Techno-economic evaluation of a hybrid PV-wind power generation system[J]. International Journal of Green Energy, 2013, 10(2): 117-136. DOI:10.1080/15435075.2011.641192.
[11] Yang Q, Ma S Y, Tang X J, et al. Evaluation index system construction and application of microgrid planning[J]. Automation of Electric Power Systems, 2012, 36(9): 13-17.(in Chinese)
[12] Xiao J, Bai L Q, Wang C S, et al.Method and software for planning and designing of microgrid[J].Proceedings of the CSEE, 2012, 32(25): 149-157.(in Chinese)
[13] Chen H. Optimum capacity determination of stand-alone hybrid generation system considering cost and reliability[J]. Applied Energy, 2013, 103: 155-164. DOI:10.1016/j.apenergy.2012.09.022.
[14] Zhong Y. Energy complementary evaluation index and its application[D].Changsha: College of Electrical and Information Engineering, Hunan University, 2013.(in Chinese)

Memo

Memo:
Biography: Xu Qingshan(1979—), male, doctor, professor, xuqing-shan@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.51377021), the Science and Technology Project of State Grid Corporation of China(No.SGTJDK00DWJS1600014).
Citation: Xu Qingshan, Xu Minjiao, Li Guodong, et al. Configuration optimization model of multi-energy distributed generation system[J].Journal of Southeast University(English Edition), 2017, 33(2):182-188.DOI:10.3969/j.issn.1003-7985.2017.02.010.
Last Update: 2017-06-20