Sharif University of Technology Scientia Iranica 1026-3098 Articles in Press 2025 12 20 GRIDMAP-Based modeling and MIPSO-driven optimization for the placement and relocation of reclosers, sectionalizers, fuses, remote-controlled switches, and manual switches in distribution grids 24110 10.24200/sci.2025.66489.10513 EN Mehdi Mohammadian Mehr Faculty of Engineering, Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. Hossein Farzin Faculty of Engineering, Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. 0000-0002-4433-1982 Journal Article 2025 07 06 This paper introduces a framework for the optimal allocation of protection and control devices including reclosers, sectionalizers, fuses, remote-controlled switches, and manual switches in power distribution systems. Central to this study is the development of the GRIDMAP model, a scalable, matrix-based representation of the distribution network topology. GRIDMAP enables detailed modeling of complex grid structures, supports both permanent and temporary fault analysis, and integrates real-world operational constraints such as device relocation and load growth. Building upon this modeling foundation, a Modified Particle Swarm Optimization (MIPSO) algorithm is implemented to solve the allocation problem. The algorithm leverages GRIDMAP’s structure to evaluate candidate locations efficiently, apply relocation strategies for existing devices, and minimize both installation and customer interruption costs. The MIPSO model outperforms ACO and ICA in cost and speed, shows stable results over 10 runs, and sensitivity analysis highlights the trade-off between equipment quality, total cost, and computation time. Notably, the model allows the relocation of existing equipment within the optimization process, and can be extended to other metaheuristic algorithms with ease. The proposed method is validated through four distinct scenarios on a modified IEEE 69-bus distribution system. Overall, GRIDMAP proves to be a robust and adaptable tool for advanced distribution grid planning and reliability enhancement. https://scientiairanica.sharif.edu/article_24110_a697ab0dcf803186970e7bb5ed0909fb.pdf