Investigating the effect of competitiveness power in estimating the average weighted price in electricity market

TL;DR

This study examines how market competitiveness influences electricity price forecasting accuracy, demonstrating that incorporating market power indices improves prediction models using real Iranian market data.

Contribution

It introduces a forecasting model that integrates market power indices to enhance accuracy, highlighting the significance of competitiveness in price prediction.

Findings

Market power indices improve forecasting accuracy.

Competitiveness extent significantly affects price formation.

Market players should consider market power in price forecasts.

Abstract

This paper evaluates the impact of the power extent on price in the electricity market. The competitiveness extent of the electricity market during specific times in a day is considered to achieve this. Then, the effect of competitiveness extent on the forecasting precision of the daily power price is assessed. A price forecasting model based on multi-layer perception via back propagation with the Levenberg-Marquardt mechanism is used. The Residual Supply Index (RSI) and other variables that affect prices are used as inputs to the model to evaluate the market competitiveness. The results show that using market power indices as inputs helps to increase forecasting accuracy. Thus, the competitiveness extent of the market power in different daily time periods is a notable variable in price formation. Moreover, market players cannot ignore the explanatory power of market power in price forecasting. In this research, the real data of the electricity market from 2013 is used and the main source of data is the Grid Management Company in Iran.