Hybridization of Improved Northern Goshawk Optimization and Line Loadability Index for Reconfiguration Considering Solar and Electric Vehicles

Abstract

In today's electrical distribution systems (EDS), the rise of renewable energy sources (RESs) and electric vehicles (EVs) holds immense significance. Despite their positive environmental impact, these technologies pose challenges due to their unpredictable nature. Efficiency and reliability are top priorities for EDSs, which can be achieved through optimal network reconfiguration (ONR). This approach addresses integrating varying RESs and EVs levels by considering distribution losses and the line loadability index (LLI). To enhance network performance by identifying optimal branches and tie-lines for switching, a novel meta-heuristic algorithm, the northern goshawk optimization algorithm (NGO), is proposed. To boost its search capabilities, an improved version, called improved NGO (INGO), introduces a levy flight distribution and an adaptive parameter. Simulations on unified Egyptian network (UEN) system across diverse scenarios demonstrate INGO's computational efficiency surpassing basic NGO and other algorithms like stochastic fractal search (SFS), harmony search algorithm (HAS), and artificial rabbits algorithm (ARO). INGO outperforms in target function optimization and computation time, showcasing its adaptability for real-time applications through reduced losses and enhanced loadability. In the initial network configuration, real power losses were measured at 805.73 kW and reactive power losses at 361.18 kVAr. Notably, the lowest voltage magnitude was recorded at bus-30, reaching 0.9463 p.u. The reliability index SAIDI was calculated as 3.148, corresponding to a maximum loadability of 1.73101 p.u. Following optimal reconfiguration with INGO, significant enhancements were observed. Losses reduced notably to 768.37 kW for real power and 360.86 kVAr for reactive power. The lowest voltage at bus-25 increased to 0.9511 p.u. Furthermore, SAIDI improved to 3.001, while loadability increased to 1.73903 p.u. These improvements were consistent even with varying PV and EV load penetrations.