基于AHP—熵权法—模糊综合分析的智能配电网综合效益评估

Comprehensive Benefit Evaluation of Smart Distribution Network Based on Analytic Hierarchy Process-Entropy Method-Fuzzy Comprehensive Analysis

随着电力体制改革,智能配电网也在进一步发展建设。首先,为评估其综合效益水平,建立了包含可靠性、电能质量、经济性、环保性、交互性、技术性六个维度35个指标的指标体系,指标权重采用主客观权重相结合的方法,主观权重由通过集中程度检验的专家意见利用层次分析法(Analytic Hierarchy Process,AHP)求各自权重,再由熵权法处理得到;客观权重由熵权法计算,采用模糊综合分析法评估,建立了基于改进AHP—熵权法—模糊综合分析的评估方法,并对实际算例进行了评估分析,该方法不仅能明确智能配电网当前发展情况,还能对其发展方向提供参考。其次,为提升方法的适应性,通过灵敏度分析确定了对评估结果较灵敏的两对指标:电能质量和经济性、电能质量和技术性,可为不同情况指标权重的调整提供依据。最后,为减小数据提供不足引起的评估困难,确定了关键指标的选取方法,通过计算指标间Spearman相关系数确定包括21个指标的最小指标集,若数据不足可采用关键指标评估,算例证明,所提关键指标筛选方法评估结果与原结果误差较小,能增强方法的适用性与可行性。

With the reform of the power system,the smart distribution network is also being further developed and constructed.Firstly,in order to evaluate its comprehensive benefit level,an index system with 35 indicators in six dimensions including reliability,power quality,economy,environmental protection,interactivity,and technology was established.The index weight was obtained by combining subjective and objective weights.The subjective weights were obtained from expert opinions that had passed the concentration degree test using Analytic Hierarchy Process(AHP)to obtain their respective weights,and then processed by the entropy weight method.The objective weights were obtained by the entropy weight method.The fuzzy comprehensive analysis method was used for the final comprehensive evaluation.We established an evaluation method based on improved AHP-entropy weight method-fuzzy comprehensive analysis,and evaluated and analyzed the actual calculation examples.This method can not only clarify the current development of smart distribution network,but also provide a reference for its development direction.Secondly,in order to improve the adaptability of the method,two pairs of indicators that are more sensitive to the evaluation results are determined through sensitivity analysis,they are power quality and economy,power quality and technology,which can provide a basis for adjusting the weights of indicators in different situations.Finally,in order to reduce the evaluation difficulties caused by insufficient data,the selection method of key indicators was determined.The minimum set of evaluable indicators was determined by calculating the Spearman correlation coefficient between the indicators,including 21 indicators.If the data is insufficient,the key indicators can be used for evaluation.The calculation example proves that the evaluation result of the proposed key indicators screening method has less error compared with the original one,which can enhance the applicability and feasibility of the method.