Peak‐valley period partition and abnormal time correction for time‐of‐use tariffs under daily load curves based on improved fuzzy c‐means

Abstract

Abstract Peak‐valley period partition of load curve is a key aspect of time‐of‐use (ToU) tariff to improve power load characteristics, such as shifting peak loads towards valley time periods. Fuzzy clustering algorithm is an effective and popular method commonly used to solve the peak‐valley period partition of load curves, but it still encounters the difficulty of dividing some data within the boundary regions of different time periods. Therefore, this paper presents a peak‐valley period partition and abnormal time correction scheme for ToU tariffs under typical daily load curves based on improved fuzzy C‐means (FCM) clustering algorithm. In order to improve the accuracy of peak‐valley period partition, modified fuzzy membership functions are proposed to improve the initialization of FCM clustering, and a loss function‐based method is presented for calculating the fuzzy parameters of those membership functions. To resolve the problem of abnormal time partitioning within the boundaries of different time periods, an abnormal time period recognition model and a correction model based on fuzzy subsethood are proposed to obtain the final corrected peak‐valley time period partitioning results. On the MATLAB R2020b platform, the effectiveness of the proposed method is verified through two real daily load curves with a time resolution of 5 min.

Keywords

• finite difference methods
• pattern clustering
• pattern recognition
• performance index
• power generation dispatch
• power grids