LMP step pattern detection based on real-time data

Locational marginal pricing (LMP) methodology has been widely adopted by most independent system operators (ISOs) and regional transmission organizations (RTOs) in today's electricity markets. Previous studies show that LMP has a step change characteristic with varying load. This can be used by market participants to predict the future electricity price and potential step change of LMP. In this paper, an effective algorithm using quality threshold (QT) clustering is proposed to detect the step change pattern of the hourly LMP. A set of indices to differentiate various patterns is introduced. Furthermore, a web-based tool is built to demonstrate the price behavior of different locations based on the 5 minutes real-time LMP data from ISOs/RTOs. The user friendly design with clustering functionality ensures easy statistical study over a large amount of historical data.