Capacity benefit margin assessment in the presence of renewable energy. (23rd June 2020)

• Record Type: Journal Article
• Authors: Mohammed, Olatunji Obalowu; Mustafa, Mohd Wazir; Aman, Muhammad Naveed; Salisu, Sani; Otuoze, Abdulrahaman Okino
• Is Part Of: International transactions on electrical energy systems. Volume 30:Number 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-23
• Subjects: capacity benefit margin -- graph theory -- multi‐area power systems -- reliability -- wind power generation; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.12502
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: Energy sustainability and reliability issues have created great concerns globally, instigating various utilities to create interconnections with one another for system security and techno‐economic benefits. Capacity benefit margin (CBM) is the amount of transmission capacity reserved between interconnected systems for emergency power exchange between utilities. It is usually estimated by evaluating the reliability of the generating units of interconnected systems to know the amount of external generation required during emergency supply shortage. Various techniques have been employed in literature to evaluate CBM. However, despite the global awareness on the continuous shift from conventional power generation sources to renewable energy sources, the assessment of CBM in the presence of renewable energy has not been addressed. Moreover, existing techniques for CBM calculation are based on iterative optimization and may not scale well to more than one deficient area. To solve these issues, this article incorporates wind power (WP) generation in CBM computation. The proposed technique is based on graph theory and can be used to calculate CBM in the presence of multi‐deficient areas and renewable energy sources, in particular, WP. Simulations using the IEEE 24‐bus reliability test system in MATLAB show that the proposed technique is significantly better in terms of scalability and accuracy as compared to existing techniques. The proposed technique can be employed by utilities for CBM estimation in the presence of renewable energy sources.