Publication: Electricity demand uncertainty modeling using enhanced path-based scenario generation method
Date
2017
Authors
Tahmasebi M.
Pasupuleti J.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
One of the most important realities and uncertainties in the deregulated electricity market is electricity demand. Electricity demand scenario generation in day-ahead markets using newly 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 5% for electricity demand using newly proposed scenario generation method. Lower mean absolute percentage error indicates higher accuracy of this method for generation of scenarios. � 2017 IEEE.
Description
Commerce; Deregulation; Electric power utilization; Errors; Gaussian noise (electronic); Power markets; Uncertainty analysis; Autoregressive moving average; Autoregressive moving average method; Day ahead market; Deregulated electricity market; Electricity demands; Mean absolute percentage error; Scenario generation; Uncertainty; Electric power generation