Optimal Configuration of Renewable Energy DGs Based on Improved Northern Goshawk Optimization Algorithm Considering Load and Generation Uncertainties

This paper proposes a distributed generator (DG) optimization framework based on an improved northern goshawk optimization (INGO) algorithm to install multiple DGs into the distributed system (DS) simultaneously. Various case studies are conducted in constant or varying load and generation models. The framework considers the power system's critical constraints while optimizing each DG's location, capacity, and power factor to minimize energy losses and voltage deviations and improve DG penetration levels and system voltage distribution. The effectiveness and stability of the INGO algorithm were first verified in two constant test systems, and the effect of the optimal power factor of the DG on the system was considered. The results of all case studies show that the INGO algorithm performs better than other existing methods in reducing active power losses and voltage deviations. When the three parameters of generator location, capacity, and power factor were optimized simultaneously, the active power losses of the two test systems were reduced by 93.96% and 98.11%, respectively. On this basis, photovoltaics (PVs) and wind turbines (WTs) as renewable energy DGs (REDGs) were also introduced in two changing test systems. The performance gap between two REDGs and conventional generators (CGs) is compared. The all-day active energy losses in the two tested systems were reduced by 88.27% and 94.74% after WTs installation. Much higher than 51.23% and 53.64% after installing PVs, almost as good as installing CGs. Therefore, WTs can replace CGs to reduce carbon emissions because the difference in energy loss and voltage deviation between the two is tiny.