Reliability worth analysis of distributed generation enhanced distribution system considering the customer cost model based on optimal radial basis function neural network

Reliability worth evaluation is one of the most important functions in power system planning and operations. The costs incurred by consumers, customer interruption costs (CIC), as a result of interruptions in their electricity supply are deemed key gnomon of customer expectations and therefore of reliability cost/worth. Hence, the authenticity of the reliability worth evaluation is directly related to choose proper interruption cost model. In this paper, an average or aggregated reliability cost model (AAM) is proposed by using the new radial basis function neural network (RBFNN) with optimum steepest descent (OSD) learning algorithm. The failure mode effect analysis technique (FMEA) is adopted for reliability worth assessment. In addition, the impact of introducing distributed generation units (DG) into the system is presented. The description of the proposed method is tested by evaluating the reliability worth of a radial distribution test system. The results indicate that using the proposed RBFNN in AAM can ensue in accurate understanding the reliability worth.