Allocation and control of multi-devices voltage regulation in distribution systems via rough set theory and grasshopper algorithm: A practical study

• Novel approach combining SRST with GOA for efficient multi-devices control. • Simultaneous optimal allocation of SCBs, DGDs, and AVRs in distribution networks. • Minimization of power losses, operational costs, and improved voltage regulation. • Adaptive strategy considering daily load variations tested on Tala feeder in Egypt. • Significant improvements in system efficiency, voltage stability, and cost savings. The distribution system is the largest component of electrical power systems, playing a crucial role in delivering electricity to consumers. However, most distribution networks are uncontrolled, resulting in issues such as insufficient monitoring, poor planning, high energy losses, inadequate voltage regulation, low reliability, and frequent overloading. To address these challenges, this paper presents optimal planning, control, and operation of integrated Switched Capacitor Banks (SCBs), Distributed Generators Devices (DGDs), and Automatic Voltage Regulators (AVRs) for enhancing the performance of the distribution systems. A novel integrated approach is presented combining Statistical Rough Set Theory (SRST) with the Grasshopper Optimization Algorithm (GOA) for the allocation and control of these multi-devices in distribution systems individually and simultaneously. The proposed combined SRST-GOA is designed in a multi-objective framework, focusing on minimizing power loss and cost as well as voltage regulation improvement. Also, this strategy is developed for the optimal dispatch of SCBs, DGDs, and AVRs in response to daily load variations, significantly improving system performance. A practical study of the Tala distribution system in the Menoufia governorate, Egypt, is addressed showcasing the effectiveness of the combined SRST-GOA in reducing losses, improving voltages, and maximizing annual savings. Results show reductions in energy losses, enhanced voltage profiles, and maximized cost savings, substantiating the proposed method's effectiveness compared to traditional approaches such as the standard GOA, well-known Particle Swarm Optimizer (PSO) technique, Honey Badger Algorithm (HBA) and Tunicate Swarm Algorithm (TSA). The proposed method achieved remarkable results when applied to the Tala distribution system in Egypt. The simultaneous allocation of SCBs, DGDs, and AVRs reduced power losses from 1,361.66 kW to 85.43 kW, representing a 93.7 % reduction. The voltage profile was significantly improved, correcting the minimum bus voltage from outside the permissible limit and raising it to 1.0453 p.u. The optimization also enhanced reliability, stabilized voltage levels, and effectively adapted to daily load variations, achieving 4.59 % reduction in daily losses compared to conventional methods. This research advances distribution system management by addressing the simultaneous deployment and coordination of multiple devices, providing a robust, scalable solution for modern electrical networks. The results validate the hybrid SRST-GOA technique as an efficient tool for achieving substantial operational and economic benefits in distribution systems.