Multi-objective optimization of PVDGs in power distribution networks using the self adaptive tyrannosaurus algorithm

The integration of renewable energy-based Distributed Generators (DGs) has become a key strategy to address the growing electricity demand and to support the transition toward sustainable power systems. Among these technologies, Photovoltaic (PV) systems, as a form of green distributed generation (GDG), can significantly enhance power quality and reduce network losses when optimally deployed. This paper proposes a multi-objective optimization framework for the optimal placement and sizing of Photovoltaic Distributed Generation (PVDG) units in radial distribution networks (RDNs). The objectives include minimizing total active power losses (TAPL), total reactive power losses (TRPL), the Average Voltage Deviation Index (AVDI), and the investment cost of PVDG units ( IC PVDG ). To achieve this, a self-adaptive version of the Tyrannosaurus Optimization Algorithm (TROA), combined with simulated annealing (TROA_SA), is employed to enhance convergence reliability and accuracy. The proposed framework is validated on IEEE 33-bus and IEEE 69-bus test systems. Results demonstrate that deploying two optimally allocated PVDG units reduces TAPL by 52.04 % and 57.86 % in the IEEE 33-bus and IEEE 69-bus systems, respectively, while simultaneously improving AVDI and overall network efficiency. These findings confirm the effectiveness and practical relevance of the proposed approach as a decision-support tool for power distribution planning and smart grid development.