Statistical Evaluation of Wind Speed Forecast Models for Microgrid Distributed Control

Cruz Victorio, Marcos Eduardo; Kazemtabrizi, Behzad; Shahbazi, Mahmoud

Abstract

With the increasing needs to decarbonise existing energy systems, there is an effort to integrate small-scale distributed generation sources, such as wind generators, with the electric demand in circuits known as microgrids. The operation of distributed variable renewable resources is subject to an optimum operating regime, ahead of real-time, which rely on output forecast. However, many wind speed forecast models are designed for centralised controllers, which are vulnerable to control failures. A suitable wind forecast model for a distributed control system is therefore required for optimal and reliable use of renewable generation. This paper presents a comparison of wind speed forecast models suited for distributed control, evaluating them in terms of the statistical significant difference in accuracy and computational resource requirements. This is essential since computational resources are limited in distributed control schemes. The data used in this paper is the historical wind speed of the Auchencorth Moss Atmospheric Observatory from 2016 to the end of 2019. Two forecast model types based on Auto-Regression and Artificial Neural Network are compared using the Diebold-Mariano test. Results show that Artificial Neural Network models with parallel hidden layers have the highest accuracy with statistical significant difference, while remaining suitable for microgrid distributed control.

Citation

Cruz Victorio, M. E., Kazemtabrizi, B., & Shahbazi, M. (2022). Statistical Evaluation of Wind Speed Forecast Models for Microgrid Distributed Control. IET Smart Grid, 5(5), 347-362. https://doi.org/10.1049/stg2.12073

• Journal Article Type: Article
• Acceptance Date: May 11, 2022
• Online Publication Date: Jun 9, 2022
• Publication Date: 2022-10
• Deposit Date: May 17, 2022
• Publicly Available Date: Jun 20, 2022
• Journal: IET Smart Grid
• Print ISSN: 2515-2947
• Electronic ISSN: 2515-2947
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 5
• Pages: 347-362
• DOI: https://doi.org/10.1049/stg2.12073
• Public URL: https://durham-repository.worktribe.com/output/1208206