Resemblance measurement of electricity market behavior based on a data distribution model

Abstract

Abstract In this paper, a data modeling method is proposed to assess the similarities of electricity market performance patterns in various times. To put the method into operation, an approximate data distribution (DD) model and a difference measuring technique are proposed to assess the similarities among the data series based on the physical outline or appearance of data distributions. The DD model is utilized to organize price suggestions, load and Share Weighted Average Lerner Index (SWALI) data based on their dependencies. Afterwards, distinction between DD models is measured using the Resemblance Measurement technique for their Minimum Total Cost (MTC) values. Based on the amount of Minimum total cost, the resemblance of electricity market manner in two time horizons is investigated, and the similar electricity market manner will be recognized. Since determining the market manner or the resemblance of market between two time horizons by pure statistical data is not easy, in this paper a method is proposed to characterize the market manner using mathematical formulations. In order to demonstrate the accuracy of this proposed method the real-world data of New York electricity market, New York Independent System Operator (NYISO) for the period 2002–2010 is used.