Mehrdad Tahmasebi - Department of Electrical Power Engineering, Eyvan-e-Gharb Branch, Islamic Azad University, Eyvan,Iran
Jagadeesh Pasupuleti - Department of Electrical Power Engineering, Universiti Tenaga Nasional, Serdang,Malaysia

One of the most important realities and uncertainties in the deregulated electricity market is day-ahead eelectricity price. Electricity price scenario generation in day-ahead markets using proposed Enhanced path-based scenario generation method based on autoregressive moving average is developed in this paper. A new enhanced path-based scenario generation method to generate scenarios of the random variable and uncertainties modeling to achieve lower mean absolute percentage error for scenario generation compared to path-based autoregressive moving average method is proposed. Comparison of expected values obtained from the proposed method and path-based ARMA method, as well as real values, shows lower mean absolute percentage error for proposed method. It is observed that the mean absolute percentage error is decreased 18.6% for day-ahead electricity price using newly proposed scenario generation method. Lower mean absolute percentage error indicates higher accuracy of this method for generation of scenarios.

Electricity price; uncertainty; Scenario generation; autoregressive moving average