Abstract
In the deregulated environment, participants always want to predict the market clearing prices so as to earn the maximum profit. This prediction becomes even more difficult in India where there are two power exchanges operate separately. In this paper, price matching along with their inter-dependability on each zone is determined. The dataset is obtained from the official website of Indian Energy Exchange (IEX) and Power Exchange of India Limited (PXIL). These two exchanges are responsible for every short-term transaction in the Indian power market. The data consists of prices in Rs/MWh for each time block from November 1, 2014 to October 31, 2015. The amount of data is huge and, therefore, a powerful analysis method is needed. This paper presents a clustering of zonal prices by using principal component analysis (PCA) and K-means clustering. Seven distinct and meaningful clusters are found. The coefficients of PCA give interesting insights behind the zonal prices.
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