Ranking Load in Microgrid System Based on the Priority Weight Calculation of Power Supply
Corressponding author's email:
anhqh@hcmute.edu.vnDOI:
https://doi.org/10.54644/jte.68.2022.1079Keywords:
Ranking load, Microgrid, Covariance, Criteria layer, Scheme layerAbstract
This paper proposes a method of ranking load in Microgird system based on the calculation of priority weights on the continuity of power supply of the loads. The proposed method applies the covariance matrix of the criterion layer to determine values of each criterion. The fuzzy preference relation matrix is used to replace the pairwise comparison matrix of the scheme layer. The weight of the criterion layer and the scheme layer are combined to get the final weights of each load bus. This solves the problem of consistency factor when evaluating loads from experts and Microgrid system operators. The calculation of priority weighting on the continuity of power supply for loads supports system operators to be proactive in planning load shedding or load shedding due to system failures, thereby minimizing damage to the system and electricity customers. The proposed method is calculated on the diagram of the IEEE 16 bus Microgrid system with six sources and eight loads.
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References
A. Cagnano, E. De Tuglie, and P. Mancarell, “Microgrids: Overview and guidelines for practical implementations and operation,” Applied Energy, Vol. 258, Jan. 2020.
B. Moran, “Microgrid load management and control strategies,” 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Morelia, Mexico, 2016, pp. 1-4.
Zhang Z, Wang JX, and Cao XY, “An energy management method of island microgrid based on load classification scheduling,” Autom Electr Power Syst, Vol. 39, pp. 17–23, 2015.
A. Mutanen, M. Ruska, S. Repo and P. Jarventausta, “Customer Classification and Load Profiling Method for Distribution Systems,” in IEEE Transactions on Power Delivery, vol. 26, no. 3, pp. 1755-1763, Jul. 2011.
Kai-leZhou, Shan-linYang, and ChaoShen, “A review of electric load classification in smart grid environment,” Renewable and Sustainable Energy Reviews, Vol. 24, pp. 103-110, Aug. 2013.
S. Zhong and K. Tam, Hierarchical Classification of Load Profiles Based on Their Characteristic Attributes in Frequency Domain, in IEEE Transactions on Power Systems, vol. 30, no. 5, pp. 2434-2441, Sep. 2015.
Ministry of Industry and Trade Vietnam, Regulations on electrical equipment, 2006.
B. Feng, M. Fu, H. Ma, Y. Xia and B. Wang, “Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance,” in IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6253-6263, Nov. 2014.
Z. Deng and J. Wang, “Multi-Sensor Data Fusion Based on Improved Analytic Hierarchy Process,” in IEEE Access, vol. 8, pp. 9875-9895, 2020.
Z. Xie, “Cov-AHP: An improvement of analytic hierarchy process method,” J. Quant. Tech. Econ., vol. 32, no. 8, pp. 137–148, 2015.
Jizhong Zhu, “Optimization of Power System Operation,” Wiley-IEEE Press, 2015.
L.-W. Lee, “Group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency,” Expert Syst. Appl., vol. 39, no. 14, pp. 11666–11676, Oct. 2012.
W. Shi, X. Xie, C. Chu and R. Gadh, “A distributed optimal energy management strategy for microgrids,” 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2014, pp. 200-205.
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