Optimisation-driven technical, economic, and environmental enhancement of renewable and EVCS-integrated faulted Indian distribution networks using DFACTS devices

Abstract

The rising integration of renewable distributed generators (RDGs) and electric vehicle charging stations (EVCSs) in modern distribution networks introduces significant technical, economic, and environmental challenges, particularly under faulted operating conditions. The converter-interfaced characteristics of these sources often reduce system fault current contribution and inertia, leading to degraded voltage and frequency stability. This study proposes an enhanced resilience framework for a faulted Indian 28-bus radial distribution system (RDS) integrated with RDGs and EVCSs through the optimal allocation of Distributed Flexible AC Transmission System (DFACTS) devices ‒ Distribution Static Synchronous Compensator (DSTATCOM), Unified Power Quality Conditioner (UPQC), Distributed Static Series Synchronous Compensator (DSSSC), Distributed Static VAR Compensator (DSVC), and Distributed Thyristor Controlled Series Capacitor (DTCSC). A bio-inspired Black Widow Optimisation (BWO) algorithm is utilised and evaluated against Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Marine Predators Algorithm (MPA) for multi-objective optimisation. The combined objective is to get the best environmental performance while minimising technical and economic impacts. BWO consistently beats the other algorithms, with the lowest average total objective (0.487), maximum (0.505), and minimum (0.485) values. It also has a standard deviation of 0.012, an average convergence of 120 iterations, and a CPU time of 8.5 s. Compared to GA, these changes mean that optimisation works up to 16.2% better and calculations are up to 22% faster. The simulation results confirm the concept that the proposed BWO-based model performs well in improving voltage stability, reducing active power loss, improving frequency response, and reducing CO 2 emissions. This suggests that it is robust and can be used to improve supply systems that are rich in renewable resources and prone to breakdowns.