Sensitivity evaluation and classification method of power users to power supply reliability
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Urumqi Power Supply Company, State Grid Xinjiang Electric Power Co., Ltd., Urumqi, Xinjiang 830011, China
Abstract:To urge power supply companies to effectively fulfill their political, economic and social responsibilities, and to achieve effective mobilization of demand-side resources and efficient management of power distribution resources, the sensitivity evaluation and the classification of power users were investigated. The sensitivity characteristics of power users were analyzed, and the definition of power user sensitivity was proposed. A power user sensitivity evaluation index system was established based on three factors of politics, economy and society, and the evaluation index matrix was established. The quantitative and qualitative index data preprocessing methods were provided. The power user sensitivity evaluation algorithms were introduced based on analytic hierarchy process (AHP), fuzzy C-means (FCM) clustering algorithm and comprehensive evaluation method combining the two methods. The evaluation algorithm was applied to a provincial science and technology core area, and the sensitivities of industry and commerce in the area users, residents and public service users were evaluated. The results show that the sensitivity levels of each user can be obtained by the proposed method, which can provide reference for the planning and construction of network distribution in the region.
ZHAO C Y, MIAO L L, LI M. Application of user portrait technology in electricity safety services[J]. Public Electricity,2019, 34(3): 16-17. (in Chinese)
XIN M M, ZHANG Y C,XIE D. Summary of researches on consumer behavior analysis based on big power data [J]. Electrical Automation,2019, 41(1): 1-4. (in Chinese)
[3]
SALMERON J L, RAHIMI S A,NAVALI A M, et al. Medical diagnosis of rheumatoid arthritis using data dri-ven PSO-FCM with scarce datasets[J]. Neurocompu-ting, 2017, 232:104-112.
[4]
WEI W, ZHOU Y T, ZHU J, et al. Reliability assessment for AC/DC hybrid distribution network with high penetration of renewable energy[J]. IEEE Access, doi: 10.1109/ACCESS.2019.2947707.
[5]
ZOU P, CHEN Q X, XIA Q, et al. Incentive compatible pool-based electricity market design and implementation: a Bayesian mechanism design approach[J]. Applied Energy,2015,158:508-518.
[6]
IGNATIEVA K, TRCK S. Modeling spot price dependence in Australian electricity markets with applications to risk management[J]. Computers and Operations Research,2016,66:415-433.
LIU B, LIU J C. Modeling practice of forecasting electricity fees sensitive customers based on user profile analysis[J]. Power Systems and Big Data,2017,20(8): 20-24. (in Chinese)
[8]
ZHU R K, LIANG Q C, ZHAN H Y. Analysis of ae-roengine performance and selection based on fuzzy comprehensive evaluation[J]. Procedia Engineering, 2017, 174:1202-1207.
[9]
BENSON C C, DEEPA V, LAJISH V L, et al. Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm[C]∥ Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics. Piscataway:IEEE, 2016:187-192.
[10]
LUO X, HONG T Z, CHEN Y X, et al. Electric load shape benchmarking for small- and medium-sized commercial buildings[J]. Applied Energy, 2017, 204:715-725.
[11]
HOU K Y, SHAO G H, WANG H M, et al. Research on practical power system stability analysis algorithm based on modified SVM[J]. Protection and Control of Modern Power Systems, doi: 10.1186/s41601-018-0086-0.
[12]
YUAN X H,TAN Q X,LEI X H, et al. Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine[J]. Energy, 2017, 129:122-137.