Statistical evaluation of wind speed forecast models for microgrid distributed control

Abstract

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 relies 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 (ANN) are compared using the Diebold‐Mariano test. Results show that ANN models with parallel hidden layers have the highest accuracy with statistical significant difference, while remaining suitable for microgrid distributed control.

Keywords

• centralised control
• distributed control
• distributed power generation
• neural nets
• power generation control
• power generation planning