Abstract
Smart distribution network (SDN) is an important part of smart grid (SG), and its dispatching control level is closely related to the safety and reliability of power system. In order to comprehensively and systematically evaluate the dispatching and control level of smart distribution network, this paper constructs an evaluation index system based on the considerations of reliability, economy, effectiveness, adaptability and cleanness. Taking into account the disadvantages of subjective weighting methods and the objective weighting methods, this paper puts forward a kind of subjective and objective mixed evaluation method for dispatching control level of SDN. In view of the great influence of expert opinions of subjective weighting method and the high data dependence of objective weighting method, the binomial coefficient method of subjective weighting is combined with the multi-objective programming method of objective weighting to give weight to each index in the comprehensive evaluation index system of dispatching control level of SDN. Case studies verify the proposed method has great significance to the evaluation of the dispatching control level of SDN. It can effectively evaluate the dispatching level of SDN and provide a reference for the improvement of the dispatching control level of SDN.
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References
Ma Siyuan, Urpelainen Johannes (2018) Distributed power generation in national rural electrification plans: an international and comparative evaluation. Energy Res Soc Sci 44:1–5
Gao Mengyou, Bingyin Xu, Fan Kaijun, Zhang Xinhui (2015) Distributed feeder automation based on automatic recognition of real-time feeder topology. Autom Electr Power Syst 39(9):127–131
Song I, Jung W, Kim J et al (2013) Operation schemes of smart distribution networks with distributed energy resources for loss reduction and service restoration. IEEE Trans Smart Grid 4(1):367–374
Agüero Julio Romero, Chongfuangprinya Panitarn, Shao Shengnan, Xu Le, Jahanbakhsh Farbod and Willis H. Lee, “Integration of plug-in electric vehicles and distributed energy resources on power distribution systems,” 2012 IEEE International Electric Vehicle Conference, 2012
Miao X, Zhang D (2014) The opportunity and challenge of big data’s application in distribution grids. In: 2014 China international conference on electricity distribution (CICED), pp 962–964,
Hayes BP, Prodanovic M (2016) State forecasting and operational planning for distribution network energy management systems. IEEE Trans Smart Grid 7(2):1002–1011
Yuri R, Rodrigues AC, de Souza Zambroni, Ribeiro PF (2018) An inclusive methodology for plug-in electrical vehicle operation with G2V and V2G in smart microgrid environments. Electr Power Energy Syst 102:312–323
Navarro-Espinosa A, Moreno R, Lagos T, Ordoñez F, Sacaan R, Espinoza S, Rudnick H (2017) Improving distribution network resilience against earthquakes. In: IET international conference on resilience of transmission and distribution networks (RTDN), Birmingham, pp 1–6
Loukarakis E, Dent C (2016) Distribution network optimization for real-time generation & flexible demand management. In: 2016 power systems computation conference (PSCC), Genoa, pp 1–7
Tan S, Xu J-X, Panda SK (2013) Optimization of distribution network incorporating distributed generators: an integrated approach. In: IEEE transactions on power systems, 28(3): 2421–2432
Su L-P, Wu X-Y (2017) Research and implementation of intelligent distribution network efficiency evaluation system. In: 2017 10th international conference on intelligent computation technology and automation (ICICTA), Changsha, pp 157–160
Liu Jia, Cheng Haozhong, Zeng Pingliang et al (2018) Rapid assessment of maximum distributed generation output based on security distance for interconnected distribution networks. Electr Power Energy Syst 101:13–24
Bie Z, Zhang P, Li G et al (2012) Reliability evaluation of active distribution systems including microgrids. IEEE Trans Power Syst 27(4):2342–2350
Rocha LF, Borges CLT, Taranto GN (2017) Reliability evaluation of active distribution networks including islanding dynamics. IEEE Trans Power Syst 32(2):1545–1552
Xu NZ, Chung CY (2016) Reliability evaluation of distribution systems including vehicle-to-home and vehicle-to-grid. IEEE Trans Power Syst 31(1):759–768
Heidari S, Fotuhi-Firuzabad M, Kazemi S (2015) Power distribution network expansion planning considering distribution automation. IEEE Trans Power Syst 30(3):1261–1269
Zeng Bo, Li Yingzi, Zhang Jianhua, Liu Zongqi, Wang Jing (2016) Comprehensive evaluation model and method for smart distribution network planning under new electricity market layout. Power Syst Technol 40(11):3309–3315
Qiu S, Zhang D, Du X An evaluation method of data valuation based on analytic hierarchy process. In 2017 14th international symposium on pervasive systems, algorithms and networks (ISPAN), Exeter, UK, pp 524–528, June. 2017
Li Z, Xue Y (2012) Fuzzy analytic hierarchy process model based on particle swarm optimization. Appl Res Comput 29(12):4549–4552
Wang Shouxiang, Ge Leijiao, Cai Shengxia et al (2017) An improved interval AHP method for assessment of cloud platform-based electrical safety monitoring system. J Electr Eng Technol 12(2):959–968
Wang M, Wu T Load restoration optimization based on entropy weight method, In: 2016 IEEE international conference on power and renewable energy (ICPRE), Shanghai, China, pp 368–372, Oct. 2016
Hsiao Ying-Tung (2004) Multiobjective evolution programming method for feeder reconfiguration. IEEE Trans Power Syst 19(1):594–599
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This work is supported in part by the National Natural Science Foundation of China (51807314).
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Wang, YQ., Ge, L. & Zhang, N. Hybrid Evaluation Method for Dispatching Control Level of Smart Distribution Network. J. Electr. Eng. Technol. 14, 2263–2275 (2019). https://doi.org/10.1007/s42835-019-00267-x
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DOI: https://doi.org/10.1007/s42835-019-00267-x