Impact of wind speed modelling on the predictive reliability assessment of wind‐based microgrids. Issue 15 (29th October 2019)

• Record Type: Journal Article
• Authors: Al‐Muhaini, Mohammad; Bizrah, Asad; Heydt, Gerald; Khalid, Muhammad
• Is Part Of: IET renewable power generation. Volume 13:Issue 15(2019)
• Page Start: 2947
• Page End: 2956
• Publication Date: 2019-10-29
• Subjects: autoregressive moving average processes -- power generation reliability -- wind power plants -- renewable energy sources -- probability -- reliability -- distributed power generation -- power distribution reliability; reliability analysis -- Roy Billinton test system -- system indices -- predictive reliability assessment -- wind‐based microgrids -- power utilities -- utilising renewable energy resources -- environmental reasons -- economic reasons -- renewable resources -- microgrid reliability analyses -- accurate reliability assessment -- power distribution systems -- system reliability assessment techniques -- wind‐based electric microgrids -- Markov model -- different wind speed -- probability distribution function; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2019.0596
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Power utilities have shown great interest in utilising renewable energy resources for various environmental and economic reasons. However, the modelling and integration of conventional and renewable resources into electric microgrids will increase the complexity of microgrid reliability analyses. Therefore, an accurate reliability assessment of power distribution systems is expected to become a major challenge in the future. More extensive research must be conducted to improve system reliability assessment techniques and determine the impact of increased penetration of renewable resources. In this work, the reliability of wind‐based electric microgrids was evaluated using a Markov model, taking the intermittent nature of wind speed into account. The effects of different wind speed modelling techniques based on the auto‐regressive moving average method, Markov model, and probability distribution function on the reliability analysis of electric microgrids were assessed. The Roy Billinton test system was used to illustrate the analysis and evaluate the different load and system indices.