An Estimation Method for Schedulable Capability of Aggregated Electric Vehicles Considering Response Uncertainty

Abstract

Accurate assessment of schedulable capacity is a prerequisite for AEVs（aggregated electric vehicles） to participate in auxiliary services， and how to quantify users' willingness to respond is a challenge in schedulable capacity assessment. In this regard， a schedulable capacity assessment method for AEVs considering response uncertainty is proposed. Firstly， a one-dimensional cloud model based on an improved sigmoid function is used to establish the user uncertainty response models of incentive level and charging time margin respectively to analyze the influence of these two factors on the users' response behavior； then， the two factors are integrated with the entropy weight method to establish a two-dimensional cloud model reflecting the users' response willingness； finally， the two-dimensional cloud model is used to modify the schedulable capacity obtained by the traditional Monte Carlo simulation method. The example shows that the proposed method can correctly and effectively quantify the uncertainty of users' response and accurately assess the schedulable capacity of AEVs.

Keywords

• aggregated electric vehicles
• schedulable capacity
• response uncertainty
• cloud model
• monte carlo simulation