Author Identifier (ORCID)
Mehrdad Ghahramani: https://orcid.org/0000-0002-5926-0996
Daryoush Habibi: https://orcid.org/0000-0002-7662-6830
Hamid Soleimani: https://orcid.org/0000-0001-9178-2973
Asma Aziz: https://orcid.org/0000-0003-3538-0536
Abstract
Electric power systems are increasingly becoming more decentralized. Many communities depend on isolated power systems that operate independently of the main grid. Remote, islanded, and isolated systems face challenges due to the intermittency and unpredictability of renewable energy sources. This paper reviews the current status of renewable integration and control in stand-alone power systems. It examines techniques to enhance system reliability through energy storage, hybrid systems, and advanced predictive models. Additionally, the issues related to connecting stand-alone systems, focusing on reliability and renewable penetration, are discussed. The scalability of stand-alone power systems is analyzed based on classifications of small-, medium-, and large-scale systems, highlighting their differences and specific challenges. The South West Interconnected System of Western Australia is used as a case study at a large scale to illustrate the complexities of operating a power system with high levels of rooftop solar and wind units. This paper also reviews various methodologies for modeling the uncertainty associated with these systems, which are categorized into stochastic, fuzzy, hybrid, Information Gap Decision Theory, robust, interval, and data-driven approaches. The advantages and limitations of each method in uncertainty modeling are discussed.
Document Type
Journal Article
Date of Publication
9-1-2025
Volume
7
Issue
3
Publication Title
Clean Technologies
Publisher
MDPI
School
School of Engineering
RAS ID
83742
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Ghahramani, M., Habibi, D., Ghamari, S., Soleimani, H., & Aziz, A. (2025). Renewable-based isolated power systems: A review of scalability, reliability, and uncertainty modeling. Clean Technologies, 7(3), 80. https://doi.org/10.3390/cleantechnol7030080