Abstract—This paper proposes a new ANN-ANN-PSO model solely for forecasting Market Clearing Price (MCP) in Indian Energy Exchange (IEX). IEX is one of the India’s electricity power trading platforms where more than 2600 participants across utilities from 27 states, 5 Union Territories, more than 500 private generators and more than 2300 open access consumers are doing business with IEX. In such a competitive electricity market, generating companies (Gencos) assess bidding strategies to maximize their profits. Gencos have to make an intelligent decision to bid the MCP beforehand based on limited information available. The accuracy in the forecasted MCP will aid Gencos in enhancing the chances of winning bids, since the MCP depends on the bidding participant behavior of both seller and buyer in the market. Thus, a most favorable bidding strategy is a challenging task for GenCos. This paper uses a similar-day approach for forecasting the MCP. The recent available historical data from January 1, 2014 to March 16, 2014 is used in this research work. This paper also investigates the performance related issues of the proposed ANN-ANN-PSO model with respect to ANN and ANN-PSO models.

Index Terms—Keywords: Error variance, Market clearing price, Mean absolute percentage error, Neural network, Particle swarm optimization

Cite: Smitha Elsa Peter and I Jacob Raglend, "SHORT TERM FORECASTING OF MARKET CLEARING PRICE IN INDIAN ENERGY EXCHANGE USING ANN-PSO MODEL," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 3, No. 3, pp. 82-95, July 2014.