Development of Renewable Energy System Using a Distributed Slack Bus Model

In this study, a distributed slack bus (DSB) approach utilizing combined participation factors, which are based on the scheduled generation capacities of the system, has been developed to allocate system losses among the generators. A DSB algorithm has been created and executed using a Newton Raphso...

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Datum:2026
1. Verfasser: Al-Rawi, Muhanned
Format: Artikel
Sprache:Englisch
Veröffentlicht: Інститут енергетичних машин і систем ім. А. М. Підгорного Національної академії наук України 2026
Online Zugang:https://journals.uran.ua/jme/article/view/350958
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Назва журналу:Energy Technologies & Resource Saving

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Energy Technologies & Resource Saving
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Zusammenfassung:In this study, a distributed slack bus (DSB) approach utilizing combined participation factors, which are based on the scheduled generation capacities of the system, has been developed to allocate system losses among the generators. A DSB algorithm has been created and executed using a Newton Raphson solver within the MATLAB environment. The IEEE 14 bus system serves as a case study for this research. Renewable energy sources are integrated into the system, and a comparative analysis of the generation costs is conducted between systems incorporating renewable energy sources and those relying solely on thermal generators, evaluated through both the single slack bus (SSB) model and the DSB model. The implementation of the DSB led to a decrease in overall real power generation, reducing it from 272.593 MW to 272.409 MW in the 14 bus system, alongside a reduction in generation costs across both bus types. Additionally, real power line losses were minimized. The alterations in generation levels of the voltage-controlled buses fostered an effective economic dispatch scheme, accurately reflecting the network parameters. The introduction of wind and solar generators significantly lowered the cost of generation compared to systems devoid of these resources. Furthermore, employing combined participation factors yielded an even more precise network model.