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Comprehensive evaluation of equipment utilization on DG-connected distribution network based on pignistic probability distance optimum evidence synthesis

机译:基于概率概率距离最优证据综合的分布式配电网设备利用率综合评价

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摘要

Large-scale distributed generation (DG) accessing to the distribution network (DN), will affect the distribution network equipment utilization rate (DNEUR). However, at present, there is a lack of comprehensive research on the effect of DG connected to grid on the comprehensive evaluation of DNEUR. Hence, this paper constructs a comprehensive evaluation system of DNEUR, according to the characteristics of DG power generation and the influence of DG connected to grid on the DNEUR. Comprehensive evaluation system includes six aspects: power supply reliability, safety criterion, grid structure, load characteristics, grid construction margin and equipment load capacity. In order to overcome the limitations of Analytic Hierarchy Process (AHP) and entropy weight method, a new AHP-entropy weight combination weighting method based on Pignistic probability distance optimal evidence synthesis is proposed. And on this basis, a comprehensive evaluation model is constructed. Finally, the operation data of five typical regions are used to verify the model. The results show that the method proposed in this paper can effectively reduce the influence of deviation from the expert weight. In the process of AHP-entropy weighting, the weight of the subjective and objective weights is adjusted scientifically according to the optimization results.
机译:大规模分布式发电(DG)访问配电网络(DN),将影响配电网络设备利用率(DNEUR)。但是,目前尚缺乏关于DG并网对DNEUR综合评价效果的综合研究。因此,根据DG发电的特点和并网的DG对DNEUR的影响,本文构建了DNEUR的综合评价系统。综合评估系统包括六个方面:供电可靠性,安全标准,电网结构,负荷特性,电网建设裕度和设备负荷能力。为了克服层次分析法和熵权法的局限性,提出了一种基于Pignistic概率距离最优证据综合的AHP-熵权组合加权法。在此基础上,构建了综合评价模型。最后,使用五个典型区域的操作数据来验证模型。结果表明,本文提出的方法可以有效地减少偏离专家权重的影响。在AHP熵加权过程中,根据优化结果科学调整主,客观权重。

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